Textbook Outcomes Code
Abdimajid Mohamed
11/15/2022
library(dplyr)##
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## intersect, setdiff, setequal, unionlibrary(gtsummary)
library(survival)
library(labelled)
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library(ggplot2)
library(survminer)## Loading required package: ggpubrlibrary(VIM)## Loading required package: colorspace## Loading required package: grid## VIM is ready to use.## Suggestions and bug-reports can be submitted at: https://github.com/statistikat/VIM/issues##
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## sleeplibrary(tibble)
library(ggplot2)
library(OddsPlotty)
library(e1071)
library(ggthemes)
library(mlbench)
library(caret)## Loading required package: lattice##
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## lift## The following object is masked from 'package:survival':
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## clusterload("TextbookOutcomes.RData")
TextbookOutcomes <-
TextbookOutcomes %>%
mutate(TextbookOutcomes, Age = ifelse(Age >= 85, "85+",
ifelse(Age >= 75, "75-84",
ifelse(Age >= 65, "65-74",
ifelse(Age >=0, "0-65", NA)))))### Recoding mortality
TextbookOutcomes$DOpertoD[TextbookOutcomes$DOpertoD == -99] <- "No"
TextbookOutcomes$DOpertoD[TextbookOutcomes$DOpertoD == 0] <- "Yes"
TextbookOutcomes$DOpertoD[TextbookOutcomes$DOpertoD == 1] <- "Yes"
TextbookOutcomes$DOpertoD[TextbookOutcomes$DOpertoD == 2] <- "Yes"
TextbookOutcomes$DOpertoD[TextbookOutcomes$DOpertoD == 3] <- "Yes"
TextbookOutcomes$DOpertoD[TextbookOutcomes$DOpertoD == 4] <- "Yes"
TextbookOutcomes$DOpertoD[TextbookOutcomes$DOpertoD == 5] <- "Yes"
TextbookOutcomes$DOpertoD[TextbookOutcomes$DOpertoD == 6] <- "Yes"
TextbookOutcomes$DOpertoD[TextbookOutcomes$DOpertoD == 7] <- "Yes"
TextbookOutcomes$DOpertoD[TextbookOutcomes$DOpertoD == 8] <- "Yes"
TextbookOutcomes$DOpertoD[TextbookOutcomes$DOpertoD == 9] <- "Yes"
TextbookOutcomes$DOpertoD[TextbookOutcomes$DOpertoD == 10] <- "Yes"
TextbookOutcomes$DOpertoD[TextbookOutcomes$DOpertoD == 11] <- "Yes"
TextbookOutcomes$DOpertoD[TextbookOutcomes$DOpertoD == 12] <- "Yes"
TextbookOutcomes$DOpertoD[TextbookOutcomes$DOpertoD == 13] <- "Yes"
TextbookOutcomes$DOpertoD[TextbookOutcomes$DOpertoD == 14] <- "Yes"
TextbookOutcomes$DOpertoD[TextbookOutcomes$DOpertoD == 15] <- "Yes"
TextbookOutcomes$DOpertoD[TextbookOutcomes$DOpertoD == 16] <- "Yes"
TextbookOutcomes$DOpertoD[TextbookOutcomes$DOpertoD == 17] <- "Yes"
TextbookOutcomes$DOpertoD[TextbookOutcomes$DOpertoD == 18] <- "Yes"
TextbookOutcomes$DOpertoD[TextbookOutcomes$DOpertoD == 19] <- "Yes"
TextbookOutcomes$DOpertoD[TextbookOutcomes$DOpertoD == 20] <- "Yes"
TextbookOutcomes$DOpertoD[TextbookOutcomes$DOpertoD == 21] <- "Yes"
TextbookOutcomes$DOpertoD[TextbookOutcomes$DOpertoD == 22] <- "Yes"
TextbookOutcomes$DOpertoD[TextbookOutcomes$DOpertoD == 23] <- "Yes"
TextbookOutcomes$DOpertoD[TextbookOutcomes$DOpertoD == 24] <- "Yes"
TextbookOutcomes$DOpertoD[TextbookOutcomes$DOpertoD == 25] <- "Yes"
TextbookOutcomes$DOpertoD[TextbookOutcomes$DOpertoD == 26] <- "Yes"
TextbookOutcomes$DOpertoD[TextbookOutcomes$DOpertoD == 27] <- "Yes"
TextbookOutcomes$DOpertoD[TextbookOutcomes$DOpertoD == 28] <- "Yes"
TextbookOutcomes$DOpertoD[TextbookOutcomes$DOpertoD == 29] <- "Yes"
TextbookOutcomes$DOpertoD[TextbookOutcomes$DOpertoD == 30] <- "Yes"
TextbookOutcomes$PRNCPTX[TextbookOutcomes$PRNCPTX == "EXCISION AMPULLA VATER"] <- NA
TextbookOutcomes$PRNCPTX[TextbookOutcomes$PRNCPTX == "EXCISION LESION PANCREAS"] <- NA
TextbookOutcomes$PRNCPTX[TextbookOutcomes$PRNCPTX == "UNLISTED PROCEDURE PANCREAS"] <- NA
TextbookOutcomes$PRNCPTX[TextbookOutcomes$PRNCPTX == "PNCRTECT WHIPPLE W/O PANCREATOJEJUNOSTOMY"] <- "Whipple"
TextbookOutcomes$PRNCPTX[TextbookOutcomes$PRNCPTX == "PNCRTECT W/PANCREATOJEJUNOSTOMY"] <- "Whipple"
TextbookOutcomes$PRNCPTX[TextbookOutcomes$PRNCPTX == "PNCRTECT DSTL STOT W/PNCRTCOJEJUNOSTOMY"] <- "Distal Pancreatectomy"
TextbookOutcomes$PRNCPTX[TextbookOutcomes$PRNCPTX == "PNCRTECT DSTL STOT W/O PNCRTCOJEJUNOSTOMY"] <- "Distal Pancreatectomy"
TextbookOutcomes$PRNCPTX[TextbookOutcomes$PRNCPTX == "PNCRTECT PROX STOT W/PANCREATOJEJUNOSTOMY"] <- "Whipple"
TextbookOutcomes$PRNCPTX[TextbookOutcomes$PRNCPTX == "PNCRTECT PROX STOT W/O PANCREATOJEJUNOSTOMY"] <- "Whipple"
TextbookOutcomes$PRNCPTX[TextbookOutcomes$PRNCPTX == "PANCREATECTOMY TOTAL"] <- "Total Pancreatectomy"
TextbookOutcomes$PRNCPTX[TextbookOutcomes$PRNCPTX == "PNCRTECT DSTL NR-TOT W/PRSRV DUO CHLD-TYP PX"] <- "Total Pancreatectomy"
TextbookOutcomes$PRNCPTX[TextbookOutcomes$PRNCPTX == "HEPATECTOMY RESCJ PARTIAL LOBECTOMY"] <- "Partial Lobectomy"
TextbookOutcomes$PRNCPTX[TextbookOutcomes$PRNCPTX == "HEPATECTOMY RESCJ TOTAL LEFT LOBECTOMY"] <- "Lobectomy"
TextbookOutcomes$PRNCPTX[TextbookOutcomes$PRNCPTX == "HEPATECTOMY RESCJ TOTAL RIGHT LOBECTOMY"] <- "Lobectomy"
TextbookOutcomes$PRNCPTX[TextbookOutcomes$PRNCPTX == "HEPATECTOMY RESCJ TRISEGMENTECTOMY"] <- "Trisegmentectomy"table(TextbookOutcomes$PRNCPTX)##
## Distal Pancreatectomy Lobectomy Partial Lobectomy
## 8292 4274 11817
## Total Pancreatectomy Trisegmentectomy Whipple
## 706 1366 16464TextbookOutcomes <-
TextbookOutcomes %>%
mutate(TextbookOutcomes, PatientGroup = ifelse(PRNCPTX == "Partial Lobectomy", "Liver",
ifelse(PRNCPTX == "Lobectomy", "Liver",
ifelse(PRNCPTX == "Trisegmentectomy", "Liver",
ifelse(PRNCPTX == "Whipple", "Pancreas",
ifelse(PRNCPTX == "Distal Pancreatectomy", "Pancreas",
ifelse(PRNCPTX == "Total Pancreatectomy", "Pancreas", NA)))))))### Recoding readmission
TextbookOutcomes$READMISSION1[TextbookOutcomes$READMISSION1 == "NULL"] <- NA
TextbookOutcomes$READMISSION1[TextbookOutcomes$READMISSION1 == "NULL"] <- NAtable(TextbookOutcomes$READMISSION1)##
## No Yes
## 27725 6219TextbookOutcomes$DOptoDis[TextbookOutcomes$DOptoDis == -99] <- NAtable(TextbookOutcomes$PRNCPTX)##
## Distal Pancreatectomy Lobectomy Partial Lobectomy
## 8292 4274 11817
## Total Pancreatectomy Trisegmentectomy Whipple
## 706 1366 16464TextbookOutcomes$RACE_NEW[TextbookOutcomes$RACE_NEW == "American Indian or Alaska Native"] <- "Other"
TextbookOutcomes$RACE_NEW[TextbookOutcomes$RACE_NEW == "Asian"] <- "Other"
TextbookOutcomes$RACE_NEW[TextbookOutcomes$RACE_NEW == "Native Hawaiian or Pacific Islander"] <- "Other"
TextbookOutcomes$RACE_NEW[TextbookOutcomes$RACE_NEW == "Black or African American"] <- "Black"
TextbookOutcomes$RACE_NEW[TextbookOutcomes$RACE_NEW == "Unknown/Not Reported"] <- NA
TextbookOutcomes$WEIGHT[TextbookOutcomes$WEIGHT ==-99] <- NA
TextbookOutcomes$HEIGHT[TextbookOutcomes$HEIGHT ==-99] <- NA
TextbookOutcomes$BMI <- (TextbookOutcomes$WEIGHT / TextbookOutcomes$HEIGHT / TextbookOutcomes$HEIGHT) *703
TextbookOutcomes <-
TextbookOutcomes %>%
mutate(TextbookOutcomes, BMILabled = ifelse(BMI >= 40, "Morbidly Obese",
ifelse(BMI >= 30, "Obese",
ifelse(BMI >= 25, "Overweight",
ifelse(BMI >=18.5, "Normal Weight",
ifelse(BMI <= 18.49, "Underweight", NA))))))TextbookOutcomes$ASACLAS[TextbookOutcomes$ASACLAS == "5-Moribund"] <- NA
TextbookOutcomes$ASACLAS[TextbookOutcomes$ASACLAS == "None assigned"] <- NA### Create MFI column
TextbookOutcomes$DIABETES[TextbookOutcomes$DIABETES == "INSULIN"] <- "Yes"
TextbookOutcomes$DIABETES[TextbookOutcomes$DIABETES == "NON-INSULIN"] <- "Yes"###Create new numeric column that will be used to create the MFI5 column 0, >1, and greater than >2
##Diabeties recoded for MFI
TextbookOutcomes <-
TextbookOutcomes %>%
mutate(TextbookOutcomes, Diabetes1 = ifelse(DIABETES == "INSULIN", "Yes",
ifelse(DIABETES == "NON-INSULIN", "Yes",
ifelse(DIABETES == "NO", "No", "NA"))))###Create new numeric column that will be used to create the MFI5 column 0, >1, and greater than >2 ###CHF recoded for MFI
TextbookOutcomes <- TextbookOutcomes %>% mutate(TextbookOutcomes, CHFMFI = ifelse(HXCHF == “Yes”, “1”, ifelse(HXCHF == “No”, “0”, “NA”)))
###Create new numeric column that will be used to create the MFI5 column 0, >1, and greater than >2 ### COPD recoded for MFI
TextbookOutcomes <- TextbookOutcomes %>% mutate(TextbookOutcomes, COPDMFI = ifelse(HXCOPD == “Yes”, “1”, ifelse(HXCOPD == “No”, “0”, “NA”)))
###Create new numeric column that will be used to create the MFI5 column 0, >1, and greater than >2 ### Hypertension with medications recoded for MFI
TextbookOutcomes <- TextbookOutcomes %>% mutate(TextbookOutcomes, HYPERMEDMFI = ifelse(HYPERMED == “Yes”, “1”, ifelse(HYPERMED == “No”, “0”, “NA”)))
##recode fnstatus2
TextbookOutcomes$FNSTATUS2[TextbookOutcomes$FNSTATUS2 == "Unknown"] <- NATextbookOutcomes <- TextbookOutcomes %>% mutate(TextbookOutcomes, FuncStatus = ifelse(FNSTATUS2 == “Yes”, “1”, ifelse(FNSTATUS2 == “No”, “0”, NA)))
##making the columns numeric to add them properly
TextbookOutcomesHYPERMEDMFI<−as.numeric(TextbookOutcomesHYPERMEDMFI) TextbookOutcomesCOPDMFI<−as.numeric(TextbookOutcomesCOPDMFI) TextbookOutcomesCHFMFI<−as.numeric(TextbookOutcomesCHFMFI) TextbookOutcomesDiabetesMFI<−as.numeric(TextbookOutcomesDiabetesMFI) TextbookOutcomesFuncStatus<−as.numeric(TextbookOutcomesFuncStatus)
###Creating MFI column
TextbookOutcomesMFI5<−(TextbookOutcomesHYPERMEDMFI + TextbookOutcomesCOPDMFI+TextbookOutcomesCHFMFI + TextbookOutcomesDiabetesMFI+TextbookOutcomesFuncStatus )
##recoding Hep and Pan targeted Complications to yes or no (will need to add in targeted complications Pan: Fistula, Delayed Gastric Emptying and what about percdrain? | Hep: Bile leakage, hepliverfail)
TextbookOutcomes$PAN_FISTULA[TextbookOutcomes$PAN_FISTULA =="Biochemical Leak only"] <- "No"
TextbookOutcomes$PAN_FISTULA[TextbookOutcomes$PAN_FISTULA =="No evidence of Biochemical Leak or POPF"] <- "No"
TextbookOutcomes$PAN_FISTULA[TextbookOutcomes$PAN_FISTULA =="Yes-persistent drainage, drain continued >7 days"] <- "Yes"
TextbookOutcomes$PAN_FISTULA[TextbookOutcomes$PAN_FISTULA =="Yes-clinical diagnosis, percutaneous drainage performed"] <- "Yes"
TextbookOutcomes$PAN_FISTULA[TextbookOutcomes$PAN_FISTULA =="Yes-persistent drainage, percutaneous drainage performed"] <- "Yes"
TextbookOutcomes$PAN_FISTULA[TextbookOutcomes$PAN_FISTULA =="Yes-persistent drainage, NPO-TPN"] <- "Yes"
TextbookOutcomes$PAN_FISTULA[TextbookOutcomes$PAN_FISTULA =="Yes-clinical diagnosis, drain continued >7 days"] <- "Yes"
TextbookOutcomes$PAN_FISTULA[TextbookOutcomes$PAN_FISTULA =="Yes, Grade B POPF present"] <- "Yes"
TextbookOutcomes$PAN_FISTULA[TextbookOutcomes$PAN_FISTULA =="Yes-clinical diagnosis, spontaneous wound drainage"] <- "Yes"
TextbookOutcomes$PAN_FISTULA[TextbookOutcomes$PAN_FISTULA =="Yes-clinical diagnosis, NPO-TPN"] <- "Yes"
TextbookOutcomes$PAN_FISTULA[TextbookOutcomes$PAN_FISTULA =="Yes-clinical diagnosis, reoperation performed"] <- "Yes"
TextbookOutcomes$PAN_FISTULA[TextbookOutcomes$PAN_FISTULA =="Yes, Grade C POPF present"] <- "Yes"
TextbookOutcomes$PAN_FISTULA[TextbookOutcomes$PAN_FISTULA =="Yes-persistent drainage, reoperation performed"] <- "Yes"
TextbookOutcomes$PAN_FISTULA[TextbookOutcomes$PAN_FISTULA =="Unknown"] <- NA
####Delayed Gastric Emptying Recode to yes or no
TextbookOutcomes$PAN_DELGASTRIC_20140315[TextbookOutcomes$PAN_DELGASTRIC_20140315 =="Yes-no oral intake by POD 14"] <- "Yes"
TextbookOutcomes$PAN_DELGASTRIC_20140315[TextbookOutcomes$PAN_DELGASTRIC_20140315 =="Yes-tube to external drainage/NG tube present/reinserted"] <- "Yes"
TextbookOutcomes$PAN_DELGASTRIC_20140315[TextbookOutcomes$PAN_DELGASTRIC_20140315 =="Unknown"] <- NA
###recoding Hep targeted complications bileleakage
TextbookOutcomes$HEP_BILELEAKAGE[TextbookOutcomes$HEP_BILELEAKAGE =="Unknown"] <- NA
TextbookOutcomes$HEP_BILELEAKAGE[TextbookOutcomes$HEP_BILELEAKAGE =="Yes-clinical diagnosis, drain continued on or after POD3"] <- "Yes"
TextbookOutcomes$HEP_BILELEAKAGE[TextbookOutcomes$HEP_BILELEAKAGE =="Yes-clinical diagnosis, percutaneous drainage performed"] <- "Yes"
TextbookOutcomes$HEP_BILELEAKAGE[TextbookOutcomes$HEP_BILELEAKAGE =="Yes-clinical diagnosis, reoperation performed"] <- "Yes"
TextbookOutcomes$HEP_BILELEAKAGE[TextbookOutcomes$HEP_BILELEAKAGE =="Yes-clinical diagnosis, spontaneous wound drainage"] <- "Yes"
TextbookOutcomes$HEP_BILELEAKAGE[TextbookOutcomes$HEP_BILELEAKAGE =="Yes-persistent drainage, drain continued on or after POD3"] <- "Yes"
TextbookOutcomes$HEP_BILELEAKAGE[TextbookOutcomes$HEP_BILELEAKAGE =="Yes-persistent drainage, percutaneous drainage performed"] <- "Yes"
TextbookOutcomes$HEP_BILELEAKAGE[TextbookOutcomes$HEP_BILELEAKAGE =="Yes-persistent drainage, reoperation performed"] <- "Yes"
##recoding hep targeted complications Liver failure (Post Hepatectomy Liver Failure)
TextbookOutcomes$HEP_LIVERFAIL[TextbookOutcomes$HEP_LIVERFAIL =="Yes-PHLF (receiving clotting factors to maintain INR)"] <- "Yes"
TextbookOutcomes$HEP_LIVERFAIL[TextbookOutcomes$HEP_LIVERFAIL =="Yes-meets criteria for PHLF"] <- "Yes"
TextbookOutcomes$HEP_LIVERFAIL[TextbookOutcomes$HEP_LIVERFAIL =="No-does not meet criteria for PHLF"] <- "No"
table(TextbookOutcomes$HEP_LIVERFAIL)##
## No Yes
## 16629 828table(TextbookOutcomes$PRNCPTX)##
## Distal Pancreatectomy Lobectomy Partial Lobectomy
## 8292 4274 11817
## Total Pancreatectomy Trisegmentectomy Whipple
## 706 1366 16464###Creating Targeted Column for Hep Complications
TextbookOutcomes1 <- TextbookOutcomes %>%
mutate(TextbookOutcomes, HepComplications = ifelse(HEP_LIVERFAIL == "Yes", "Yes",
ifelse(HEP_BILELEAKAGE == "Yes", "Yes", "No")))table(TextbookOutcomes1$HepComplications)##
## No Yes
## 15510 1842TextbookOutcomes2 <- TextbookOutcomes1 %>%
mutate(TextbookOutcomes, PanComplications = ifelse(PAN_FISTULA == "Yes", "Yes",
ifelse(PAN_DELGASTRIC_20140315 == "Yes", "Yes", "No")))str(TextbookOutcomes2$PanComplications)## chr [1:44235] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA ...sum(is.na(TextbookOutcomes2$PanComplications))## [1] 17754###creating a true textbookoutcomes column for pan and liver
TextbookOutcomes2$PanComplications[TextbookOutcomes2$PanComplications ==NA] <- "NA"
TextbookOutcomes2$PanComplications[TextbookOutcomes2$HepComplications ==NA] <- "NA"
table(TextbookOutcomes2$PanComplications)##
## No Yes
## 19879 6602TextbookOutcomes2$PanComplications[TextbookOutcomes2$PanComplications ==NA] <- "NA"
sum(is.na(TextbookOutcomes2$PanComplications))## [1] 17754table(is.na(TextbookOutcomes2$PanComplications))##
## FALSE TRUE
## 26481 17754### Ifelse function to create new column for any complication (will need to add in targeted complications Pan: Fistula, Delayed Gastric Emptying | Hep: Bile leakage, hepliverfail)
TextbookOutcomes3 <- TextbookOutcomes2 %>%
mutate(TextbookOutcomes, AnyComplication = ifelse(CDARREST == "Cardiac Arrest Requiring CPR", "Yes",
ifelse(CDMI == "Myocardial Infarction", "Yes",
ifelse(OUPNEUMO == "Pneumonia", "Yes",
ifelse(RENAINSF == "Progressive Renal Insufficiency", "Yes",
ifelse(OPRENAFL == "Acute Renal Failure", "Yes",
ifelse(PULEMBOL == "Pulmonary Embolism", "Yes",
ifelse(OTHDVT == "DVT Requiring Therapy", "Yes",
ifelse(RETURNOR == "Yes", "Yes",
ifelse(ORGSPCSSI == "Organ/Space SSI", "Yes",
ifelse(PRSEPIS == "Yes", "Yes",
ifelse(REINTUB == "Unplanned Intubation", "Yes",
ifelse(URNINFEC == "Urinary Tract Infection", "Yes",
ifelse(SUPINFEC == "Superficial Incisional SSI", "Yes",
ifelse(FAILWEAN == "On Ventilator greater than 48 Hours", "Yes",
ifelse(CNSCVA == "Stroke/CVA", "Yes",
ifelse(DEHIS == "Wound Disruption", "Yes", "No")))))))))))))))))##subsetting Procedure with DoptoDIS information to obtain the mean
TextbookOutcomesDistalPan <-
subset(TextbookOutcomes, PRNCPTX == "Distal Pancreatectomy")
TextbookOutcomesLobectomy <-
subset(TextbookOutcomes, PRNCPTX == "Lobectomy")
TextbookOutcomesOtherPan <-
subset(TextbookOutcomes, PRNCPTX == "Other Pancreas")
TextbookOutcomesPartialLobectomy <-
subset(TextbookOutcomes, PRNCPTX == "Partial Lobectomy")
TextbookOutcomesTotalPan <-
subset(TextbookOutcomes, PRNCPTX == "Total Pancreatectomy")
TextbookOutcomesTrisegmentectomy <-
subset(TextbookOutcomes, PRNCPTX == "Trisegmentectomy")
TextbookOutcomesWhipple <-
subset(TextbookOutcomes, PRNCPTX == "Whipple")###obtaining the 75th percential for LOS defined as day of surgery to date of discharge
SeventyFifthPercentileDistalPan = TextbookOutcomesDistalPan$DOptoDis
SeventyFifthPercentileLobectomy = TextbookOutcomesLobectomy$DOptoDis
SeventyFifthPercentileOtherPan = TextbookOutcomesOtherPan$DOptoDis
SeventyFifthPercentilePartialLobectomy = TextbookOutcomesPartialLobectomy$DOptoDis
SeventyFifthPercentileTotalPan = TextbookOutcomesTotalPan$DOptoDis
SeventyFifthPercentileTrisegmentectomy = TextbookOutcomesTrisegmentectomy$DOptoDis
SeventyFifthPercentileWhipple = TextbookOutcomesWhipple$DOptoDisquantile(SeventyFifthPercentileDistalPan, na.rm = T)## 0% 25% 50% 75% 100%
## 0 4 5 7 88quantile(SeventyFifthPercentileLobectomy, na.rm = T)## 0% 25% 50% 75% 100%
## 0 5 6 8 114quantile(SeventyFifthPercentileOtherPan, na.rm = T)## 0% 25% 50% 75% 100%
## NA NA NA NA NAquantile(SeventyFifthPercentilePartialLobectomy, na.rm = T)## 0% 25% 50% 75% 100%
## 0 3 5 6 85quantile(SeventyFifthPercentileTotalPan, na.rm = T)## 0% 25% 50% 75% 100%
## 0 6 8 12 89quantile(SeventyFifthPercentileTrisegmentectomy, na.rm = T)## 0% 25% 50% 75% 100%
## 0 5 7 10 78quantile(SeventyFifthPercentileWhipple, na.rm = T)## 0% 25% 50% 75% 100%
## 0 6 8 12 111TextbookOutcomes3$ELOSDistalPan <- ifelse(TextbookOutcomes3$PRNCPTX == "Distal Pancreatectomy" & TextbookOutcomes3$DOptoDis >= 7, "Yes", "No")
TextbookOutcomes3$ELOSLobectomy <- ifelse(TextbookOutcomes3$PRNCPTX == "Lobectomy" & TextbookOutcomes3$DOptoDis >= 8, "Yes", "No")
TextbookOutcomes3$ELOSOtherPan <- ifelse(TextbookOutcomes3$PRNCPTX == "Other Pancreas" & TextbookOutcomes3$DOptoDis >= 7, "Yes", "No")
TextbookOutcomes3$ELOSPartial <- ifelse(TextbookOutcomes3$PRNCPTX == "Partial Lobectomy" & TextbookOutcomes3$DOptoDis >= 6, "Yes", "No")
TextbookOutcomes3$ELOSTotal <- ifelse(TextbookOutcomes3$PRNCPTX == "Total Pancreatectomy" & TextbookOutcomes3$DOptoDis >= 12, "Yes", "No")
TextbookOutcomes3$ELOSTrisegmentectomy <- ifelse(TextbookOutcomes3$PRNCPTX == "Trisegmentectomy" & TextbookOutcomes3$DOptoDis >= 10, "Yes", "No")
TextbookOutcomes3$ELOSWhipple <- ifelse(TextbookOutcomes3$PRNCPTX == "Whipple" & TextbookOutcomes3$DOptoDis >= 12, "Yes", "No")
table(TextbookOutcomes3$ELOSPartial)##
## No Yes
## 39501 4153###recode 9 and up to be considered increased LOS
PanTextbookOutcomes <- TextbookOutcomes3 %>%
mutate(TextbookOutcomes3, ELOS = ifelse(ELOSDistalPan == "Yes", "Yes",
ifelse(ELOSLobectomy == "Yes", "Yes",
ifelse(ELOSOtherPan == "Yes", "Yes",
ifelse(ELOSPartial == "Yes", "Yes",
ifelse(ELOSTotal == "Yes", "Yes",
ifelse(ELOSTrisegmentectomy == "Yes", "Yes",
ifelse(ELOSWhipple == "Yes", "Yes", "No"))))))))
table(PanTextbookOutcomes$ELOS)##
## No Yes
## 30578 12853###subset pancreatic cancer patients prior to creating a Textbook Outcomes Table double the 75th percentile number afterwards
PanTextbookOutcomes <- subset(TextbookOutcomes4, PanComplications == “Yes” | PanComplications == “No”)
table(PanTextbookOutcomes$HepComplications)
PanTextbookOutcomes$TextbookOutcomeHep <- ifelse(PanTextbookOutcomes$ELOS == "No" & PanTextbookOutcomes$DOpertoD == "No" & PanTextbookOutcomes$HepComplications == "No" & PanTextbookOutcomes$AnyComplication == "No" & PanTextbookOutcomes$READMISSION1 == "No",
"Yes",
"No")
PanTextbookOutcomes$TextbookOutcomePan <- ifelse(PanTextbookOutcomes$ELOS == "No" & PanTextbookOutcomes$DOpertoD == "No" & PanTextbookOutcomes$PanComplications == "No" & PanTextbookOutcomes$AnyComplication == "No" & PanTextbookOutcomes$READMISSION1 == "No",
"Yes",
"No")
PanTextbookOutcomes$TextbookOutcomeAll <- ifelse(PanTextbookOutcomes$TextbookOutcomePan == "No" & PanTextbookOutcomes$TextbookOutcomeHep == "No",
"No",
"Yes")
table(PanTextbookOutcomes$TextbookOutcomeHep)##
## No Yes
## 20105 6950table(PanTextbookOutcomes$TextbookOutcomePan)##
## No Yes
## 21045 9165table(PanTextbookOutcomes$TextbookOutcomeAll)##
## No Yes
## 19790 16115PanTextbookOutcomes3 <- PanTextbookOutcomes %>% mutate(PanTextbookOutcomes, TextbookOutcomeAll = ifelse(TextbookOutcomePan == NA, “Yes”, ifelse(TextbookOutcomeHep == “Yes”, “Yes”,“NA”)))
table(PanTextbookOutcomes3$TextbookOutcomeAll)
PanTextbookOutcomes1 <- PanTextbookOutcomes %>% mutate(PanTextbookOutcomes, ModifiedFrailtyIndex5 = ifelse(MFI5 == 0, “0”, ifelse(MFI5 == 1, “1”, ifelse(MFI5 > 2, “>2”, NA))))
PantextBookOutcomesDT <-
PanTextbookOutcomes %>%
select(PatientGroup, DOptoDis, Age, SEX, RACE_NEW, BMILabled, ASACLAS, DOpertoD, OPTIME, ELOS, READMISSION1, STEROID, DIABETES, HYPERMED, ASACLAS, ASCITES, HXCHF, DYSPNEA, SMOKE, HXCOPD, DIALYSIS, OPRENAFL, FNSTATUS2, AnyComplication, TextbookOutcomeHep, TextbookOutcomePan, TextbookOutcomeAll, PanComplications, HepComplications, PRNCPTX)ByProcedure <-
PanTextbookOutcomes %>%
select(PatientGroup, DOptoDis, Age, SEX, RACE_NEW, BMILabled, ASACLAS, DOpertoD, OPTIME, ELOS, READMISSION1, STEROID, DIABETES, HYPERMED, ASACLAS, ASCITES, HXCHF, DYSPNEA, SMOKE, HXCOPD, DIALYSIS, OPRENAFL, FNSTATUS2, PAN_FISTULA, PAN_DELGASTRIC_20140315, HEP_LIVERFAIL, HEP_BILELEAKAGE, CDARREST, CDMI, OUPNEUMO, RENAINSF, OPRENAFL, PULEMBOL, OTHDVT, RETURNOR, ORGSPCSSI, PRSEPIS, REINTUB, URNINFEC, SUPINFEC, PRNCPTX)Underweight <-
PanTextbookOutcomes %>%
select(PatientGroup, DOptoDis, Age, SEX, RACE_NEW, BMILabled, ASACLAS, DOpertoD, TextbookOutcomeHep, TextbookOutcomePan, TextbookOutcomeAll)LOSTable <-
PanTextbookOutcomes %>%
select(PRNCPTX, DOptoDis, PatientGroup)LOSTable %>%
tbl_summary(
by = PRNCPTX,
digits = all_continuous() ~ 2,) %>%
add_p(pvalue_fun = ~style_pvalue(.x, digits = 2))%>%
bold_p() %>%
add_overall() %>%
modify_header(label ~ "**Variable**") %>%
modify_spanning_header(c("stat_1", "stat_2") ~ "**Textbook Outcomes*") %>%
bold_labels() ## 1316 observations missing `PRNCPTX` have been removed. To include these observations, use `forcats::fct_explicit_na()` on `PRNCPTX` column before passing to `tbl_summary()`.| Variable | Overall, N = 42,9191 | *Textbook Outcomes | Partial Lobectomy, N = 11,8171 | Total Pancreatectomy, N = 7061 | Trisegmentectomy, N = 1,3661 | Whipple, N = 16,4641 | p-value2 | |
|---|---|---|---|---|---|---|---|---|
| Distal Pancreatectomy, N = 8,2921 | Lobectomy, N = 4,2741 | |||||||
| DOptoDis | 6.00 (5.00, 9.00) | 5.00 (4.00, 7.00) | 6.00 (5.00, 8.00) | 5.00 (3.00, 6.00) | 8.00 (6.00, 12.00) | 7.00 (5.00, 10.00) | 8.00 (6.00, 12.00) | <0.001 |
| Unknown | 242 | 25 | 21 | 19 | 8 | 10 | 159 | |
| PatientGroup | <0.001 | |||||||
| Liver | 17,457 (41%) | 0 (0%) | 4,274 (100%) | 11,817 (100%) | 0 (0%) | 1,366 (100%) | 0 (0%) | |
| Pancreas | 25,462 (59%) | 8,292 (100%) | 0 (0%) | 0 (0%) | 706 (100%) | 0 (0%) | 16,464 (100%) | |
|
1
Statistics presented: Median (IQR); n (%)
2
Statistical tests performed: Kruskal-Wallis test; chi-square test of independence
|
||||||||
TextbookOutcomes2\(PanComplications[TextbookOutcomes2\)PanComplications ==NA] <- “NA”
TextbookOutcomes3 <- TextbookOutcomes2 %>% mutate(TextbookOutcomes, AnyComplication = ifelse(CDARREST == “Cardiac Arrest Requiring CPR”, “Yes”, ifelse(CDMI == “Myocardial Infarction”, “Yes”, ifelse(OUPNEUMO == “Pneumonia”, “Yes”, ifelse(RENAINSF == “Progressive Renal Insufficiency”, “Yes”, ifelse(OPRENAFL == “Acute Renal Failure”, “Yes”, ifelse(PULEMBOL == “Pulmonary Embolism”, “Yes”, ifelse(OTHDVT == “DVT Requiring Therapy”, “Yes”, ifelse(RETURNOR == “Yes”, “Yes”, ifelse(ORGSPCSSI == “Organ/Space SSI”, “Yes”, ifelse(PRSEPIS == “Yes”, “Yes”, ifelse(REINTUB == “Unplanned Intubation”, “Yes”, ifelse(URNINFEC == “Urinary Tract Infection”, “Yes”, ifelse(SUPINFEC == “Superficial Incisional SSI”, “Yes”, ifelse(FAILWEAN == “On Ventilator greater than 48 Hours”, “Yes”, ifelse(CNSCVA == “Stroke/CVA”, “Yes”, ifelse(DEHIS == “Wound Disruption”, “Yes”,
ByProcedure %>%
tbl_summary(
by = PatientGroup,
digits = all_continuous() ~ 2,) %>%
add_p(pvalue_fun = ~style_pvalue(.x, digits = 2))%>%
bold_p() %>%
add_overall() %>%
modify_header(label ~ "**Variable**") %>%
modify_spanning_header(c("stat_1", "stat_2") ~ "**Textbook Outcomes*") %>%
bold_labels() ## 1316 observations missing `PatientGroup` have been removed. To include these observations, use `forcats::fct_explicit_na()` on `PatientGroup` column before passing to `tbl_summary()`.## There was an error in 'add_p()' for variable 'PAN_FISTULA', p-value omitted:
## Error in stats::chisq.test(data[[variable]], as.factor(data[[by]])): 'x' and 'y' must have at least 2 levels## There was an error in 'add_p()' for variable 'PAN_DELGASTRIC_20140315', p-value omitted:
## Error in stats::chisq.test(data[[variable]], as.factor(data[[by]])): 'x' and 'y' must have at least 2 levels## There was an error in 'add_p()' for variable 'HEP_LIVERFAIL', p-value omitted:
## Error in stats::chisq.test(data[[variable]], as.factor(data[[by]])): 'x' and 'y' must have at least 2 levels## There was an error in 'add_p()' for variable 'HEP_BILELEAKAGE', p-value omitted:
## Error in stats::chisq.test(data[[variable]], as.factor(data[[by]])): 'x' and 'y' must have at least 2 levels| Variable | Overall, N = 42,9191 | *Textbook Outcomes | p-value2 | |
|---|---|---|---|---|
| Liver, N = 17,4571 | Pancreas, N = 25,4621 | |||
| DOptoDis | 6.00 (5.00, 9.00) | 5.00 (4.00, 7.00) | 7.00 (5.00, 10.00) | <0.001 |
| Unknown | 242 | 50 | 192 | |
| Age | <0.001 | |||
| 0-65 | 22,630 (53%) | 10,737 (62%) | 11,893 (47%) | |
| 65-74 | 13,302 (31%) | 4,660 (27%) | 8,642 (34%) | |
| 75-84 | 6,399 (15%) | 1,907 (11%) | 4,492 (18%) | |
| 85+ | 588 (1.4%) | 153 (0.9%) | 435 (1.7%) | |
| SEX | 0.040 | |||
| female | 21,264 (50%) | 8,754 (50%) | 12,510 (49%) | |
| male | 21,655 (50%) | 8,703 (50%) | 12,952 (51%) | |
| RACE_NEW | <0.001 | |||
| Black | 3,648 (10%) | 1,468 (11%) | 2,180 (9.8%) | |
| Other | 2,498 (6.9%) | 1,298 (9.3%) | 1,200 (5.4%) | |
| White | 29,943 (83%) | 11,130 (80%) | 18,813 (85%) | |
| Unknown | 6,830 | 3,561 | 3,269 | |
| BMILabled | <0.001 | |||
| Morbidly Obese | 1,926 (4.5%) | 908 (5.2%) | 1,018 (4.0%) | |
| Normal Weight | 13,726 (32%) | 5,229 (30%) | 8,497 (34%) | |
| Obese | 11,570 (27%) | 5,058 (29%) | 6,512 (26%) | |
| Overweight | 14,594 (34%) | 5,880 (34%) | 8,714 (34%) | |
| Underweight | 879 (2.1%) | 268 (1.5%) | 611 (2.4%) | |
| Unknown | 224 | 114 | 110 | |
| ASACLAS | <0.001 | |||
| 1-No Disturb | 428 (1.0%) | 253 (1.5%) | 175 (0.7%) | |
| 2-Mild Disturb | 10,403 (24%) | 4,395 (25%) | 6,008 (24%) | |
| 3-Severe Disturb | 29,056 (68%) | 11,495 (66%) | 17,561 (69%) | |
| 4-Life Threat | 2,951 (6.9%) | 1,270 (7.3%) | 1,681 (6.6%) | |
| Unknown | 81 | 44 | 37 | |
| DOpertoD | 609 (1.4%) | 257 (1.5%) | 352 (1.4%) | 0.47 |
| OPTIME | 270.00 (186.00, 373.00) | 216.00 (154.00, 300.00) | 312.00 (221.00, 409.00) | <0.001 |
| ELOS | 12,853 (30%) | 5,727 (33%) | 7,126 (28%) | <0.001 |
| Unknown | 242 | 50 | 192 | |
| READMISSION1 | 6,018 (18%) | 1,725 (13%) | 4,293 (22%) | <0.001 |
| Unknown | 9,960 | 4,301 | 5,659 | |
| STEROID | 1,290 (3.0%) | 533 (3.1%) | 757 (3.0%) | 0.65 |
| DIABETES | <0.001 | |||
| NO | 33,197 (77%) | 14,381 (82%) | 18,816 (74%) | |
| Yes | 9,722 (23%) | 3,076 (18%) | 6,646 (26%) | |
| HYPERMED | 21,343 (50%) | 8,021 (46%) | 13,322 (52%) | <0.001 |
| ASCITES | 169 (0.4%) | 96 (0.5%) | 73 (0.3%) | <0.001 |
| HXCHF | 164 (0.4%) | 59 (0.3%) | 105 (0.4%) | 0.25 |
| DYSPNEA | 0.082 | |||
| AT REST | 69 (0.2%) | 37 (0.2%) | 32 (0.1%) | |
| MODERATE EXERTION | 2,241 (5.2%) | 921 (5.3%) | 1,320 (5.2%) | |
| No | 40,609 (95%) | 16,499 (95%) | 24,110 (95%) | |
| SMOKE | 7,065 (16%) | 2,698 (15%) | 4,367 (17%) | <0.001 |
| HXCOPD | 1,670 (3.9%) | 640 (3.7%) | 1,030 (4.0%) | 0.049 |
| DIALYSIS | 151 (0.4%) | 51 (0.3%) | 100 (0.4%) | 0.10 |
| OPRENAFL | 0.30 | |||
| Acute Renal Failure | 373 (0.9%) | 162 (0.9%) | 211 (0.8%) | |
| No Complication | 42,546 (99%) | 17,295 (99%) | 25,251 (99%) | |
| FNSTATUS2 | 0.077 | |||
| Independent | 42,512 (99%) | 17,308 (99%) | 25,204 (99%) | |
| Partially Dependent | 319 (0.7%) | 111 (0.6%) | 208 (0.8%) | |
| Totally Dependent | 24 (<0.1%) | 8 (<0.1%) | 16 (<0.1%) | |
| Unknown | 64 | 30 | 34 | |
| PAN_FISTULA | 4,318 (17%) | 0 (NA%) | 4,318 (17%) | |
| Unknown | 17,684 | 17,457 | 227 | |
| PAN_DELGASTRIC_20140315 | 3,258 (13%) | 0 (NA%) | 3,258 (13%) | |
| Unknown | 17,577 | 17,457 | 120 | |
| HEP_LIVERFAIL | 828 (4.7%) | 828 (4.7%) | 0 (NA%) | |
| Unknown | 25,462 | 0 | 25,462 | |
| HEP_BILELEAKAGE | 1,252 (7.2%) | 1,252 (7.2%) | 0 (NA%) | |
| Unknown | 25,589 | 127 | 25,462 | |
| CDARREST | <0.001 | |||
| Cardiac Arrest Requiring CPR | 375 (0.9%) | 120 (0.7%) | 255 (1.0%) | |
| No Complication | 42,544 (99%) | 17,337 (99%) | 25,207 (99%) | |
| CDMI | 0.006 | |||
| Myocardial Infarction | 434 (1.0%) | 148 (0.8%) | 286 (1.1%) | |
| No Complication | 42,485 (99%) | 17,309 (99%) | 25,176 (99%) | |
| OUPNEUMO | 0.11 | |||
| No Complication | 41,484 (97%) | 16,903 (97%) | 24,581 (97%) | |
| Pneumonia | 1,435 (3.3%) | 554 (3.2%) | 881 (3.5%) | |
| RENAINSF | 0.20 | |||
| No Complication | 42,653 (99%) | 17,338 (99%) | 25,315 (99%) | |
| Progressive Renal Insufficiency | 266 (0.6%) | 119 (0.7%) | 147 (0.6%) | |
| PULEMBOL | 0.64 | |||
| No Complication | 42,386 (99%) | 17,246 (99%) | 25,140 (99%) | |
| Pulmonary Embolism | 533 (1.2%) | 211 (1.2%) | 322 (1.3%) | |
| OTHDVT | <0.001 | |||
| DVT Requiring Therapy | 957 (2.2%) | 284 (1.6%) | 673 (2.6%) | |
| No Complication | 41,962 (98%) | 17,173 (98%) | 24,789 (97%) | |
| RETURNOR | 1,768 (4.1%) | 510 (2.9%) | 1,258 (4.9%) | <0.001 |
| ORGSPCSSI | <0.001 | |||
| No Complication | 37,896 (88%) | 16,140 (92%) | 21,756 (85%) | |
| Organ/Space SSI | 5,023 (12%) | 1,317 (7.5%) | 3,706 (15%) | |
| PRSEPIS | 0.55 | |||
| None | 42,360 (99%) | 17,216 (99%) | 25,144 (99%) | |
| Sepsis | 162 (0.4%) | 74 (0.4%) | 88 (0.3%) | |
| Septic Shock | 20 (<0.1%) | 9 (<0.1%) | 11 (<0.1%) | |
| SIRS | 377 (0.9%) | 158 (0.9%) | 219 (0.9%) | |
| REINTUB | <0.001 | |||
| No Complication | 41,773 (97%) | 17,078 (98%) | 24,695 (97%) | |
| Unplanned Intubation | 1,146 (2.7%) | 379 (2.2%) | 767 (3.0%) | |
| URNINFEC | <0.001 | |||
| No Complication | 41,937 (98%) | 17,110 (98%) | 24,827 (98%) | |
| Urinary Tract Infection | 982 (2.3%) | 347 (2.0%) | 635 (2.5%) | |
| SUPINFEC | <0.001 | |||
| No Complication | 40,825 (95%) | 16,851 (97%) | 23,974 (94%) | |
| Superficial Incisional SSI | 2,094 (4.9%) | 606 (3.5%) | 1,488 (5.8%) | |
| PRNCPTX | <0.001 | |||
| Distal Pancreatectomy | 8,292 (19%) | 0 (0%) | 8,292 (33%) | |
| Lobectomy | 4,274 (10.0%) | 4,274 (24%) | 0 (0%) | |
| Partial Lobectomy | 11,817 (28%) | 11,817 (68%) | 0 (0%) | |
| Total Pancreatectomy | 706 (1.6%) | 0 (0%) | 706 (2.8%) | |
| Trisegmentectomy | 1,366 (3.2%) | 1,366 (7.8%) | 0 (0%) | |
| Whipple | 16,464 (38%) | 0 (0%) | 16,464 (65%) | |
|
1
Statistics presented: Median (IQR); n (%)
2
Statistical tests performed: Wilcoxon rank-sum test; chi-square test of independence
|
||||
ByProcedure %>%
tbl_summary(
by = PRNCPTX,
digits = all_continuous() ~ 2,) %>%
add_p(pvalue_fun = ~style_pvalue(.x, digits = 2))%>%
bold_p() %>%
add_overall() %>%
modify_header(label ~ "**Variable**") %>%
modify_spanning_header(c("stat_1", "stat_2") ~ "**Textbook Outcomes*") %>%
bold_labels() ## 1316 observations missing `PRNCPTX` have been removed. To include these observations, use `forcats::fct_explicit_na()` on `PRNCPTX` column before passing to `tbl_summary()`.## There was an error in 'add_p()' for variable 'ASCITES', p-value omitted:
## Error in stats::fisher.test(data[[variable]], as.factor(data[[by]])): FEXACT error 7(location). LDSTP=10560 is too small for this problem,
## (pastp=310.94, ipn_0:=ipoin[itp=121]=1692, stp[ipn_0]=312.105).
## Increase workspace or consider using 'simulate.p.value=TRUE'## There was an error in 'add_p()' for variable 'DYSPNEA', p-value omitted:
## Error in stats::fisher.test(data[[variable]], as.factor(data[[by]])): FEXACT error 501.
## The hash table key cannot be computed because the largest key
## is larger than the largest representable int.
## The algorithm cannot proceed.
## Reduce the workspace, consider using 'simulate.p.value=TRUE' or another algorithm.## There was an error in 'add_p()' for variable 'DIALYSIS', p-value omitted:
## Error in stats::fisher.test(data[[variable]], as.factor(data[[by]])): FEXACT error 7(location). LDSTP=10560 is too small for this problem,
## (pastp=363.838, ipn_0:=ipoin[itp=125]=1844, stp[ipn_0]=364.916).
## Increase workspace or consider using 'simulate.p.value=TRUE'## There was an error in 'add_p()' for variable 'FNSTATUS2', p-value omitted:
## Error in stats::fisher.test(data[[variable]], as.factor(data[[by]])): Bug in fexact3, it[i=4]=20390: negative key -984893980 (kyy=41809)## There was an error in 'add_p()' for variable 'RENAINSF', p-value omitted:
## Error in stats::fisher.test(data[[variable]], as.factor(data[[by]])): FEXACT error 7(location). LDSTP=10560 is too small for this problem,
## (pastp=288.129, ipn_0:=ipoin[itp=259]=345, stp[ipn_0]=286.738).
## Increase workspace or consider using 'simulate.p.value=TRUE'## There was an error in 'add_p()' for variable 'PRSEPIS', p-value omitted:
## Error in stats::fisher.test(data[[variable]], as.factor(data[[by]])): FEXACT error 501.
## The hash table key cannot be computed because the largest key
## is larger than the largest representable int.
## The algorithm cannot proceed.
## Reduce the workspace, consider using 'simulate.p.value=TRUE' or another algorithm.| Variable | Overall, N = 42,9191 | *Textbook Outcomes | Partial Lobectomy, N = 11,8171 | Total Pancreatectomy, N = 7061 | Trisegmentectomy, N = 1,3661 | Whipple, N = 16,4641 | p-value2 | |
|---|---|---|---|---|---|---|---|---|
| Distal Pancreatectomy, N = 8,2921 | Lobectomy, N = 4,2741 | |||||||
| PatientGroup | <0.001 | |||||||
| Liver | 17,457 (41%) | 0 (0%) | 4,274 (100%) | 11,817 (100%) | 0 (0%) | 1,366 (100%) | 0 (0%) | |
| Pancreas | 25,462 (59%) | 8,292 (100%) | 0 (0%) | 0 (0%) | 706 (100%) | 0 (0%) | 16,464 (100%) | |
| DOptoDis | 6.00 (5.00, 9.00) | 5.00 (4.00, 7.00) | 6.00 (5.00, 8.00) | 5.00 (3.00, 6.00) | 8.00 (6.00, 12.00) | 7.00 (5.00, 10.00) | 8.00 (6.00, 12.00) | <0.001 |
| Unknown | 242 | 25 | 21 | 19 | 8 | 10 | 159 | |
| Age | <0.001 | |||||||
| 0-65 | 22,630 (53%) | 4,420 (53%) | 2,616 (61%) | 7,281 (62%) | 371 (53%) | 840 (61%) | 7,102 (43%) | |
| 65-74 | 13,302 (31%) | 2,508 (30%) | 1,147 (27%) | 3,122 (26%) | 228 (32%) | 391 (29%) | 5,906 (36%) | |
| 75-84 | 6,399 (15%) | 1,249 (15%) | 480 (11%) | 1,298 (11%) | 99 (14%) | 129 (9.4%) | 3,144 (19%) | |
| 85+ | 588 (1.4%) | 115 (1.4%) | 31 (0.7%) | 116 (1.0%) | 8 (1.1%) | 6 (0.4%) | 312 (1.9%) | |
| SEX | <0.001 | |||||||
| female | 21,264 (50%) | 4,540 (55%) | 2,105 (49%) | 5,981 (51%) | 353 (50%) | 668 (49%) | 7,617 (46%) | |
| male | 21,655 (50%) | 3,752 (45%) | 2,169 (51%) | 5,836 (49%) | 353 (50%) | 698 (51%) | 8,847 (54%) | |
| RACE_NEW | <0.001 | |||||||
| Black | 3,648 (10%) | 833 (11%) | 343 (11%) | 1,032 (11%) | 52 (8.6%) | 93 (9.0%) | 1,295 (9.1%) | |
| Other | 2,498 (6.9%) | 389 (5.3%) | 343 (11%) | 879 (9.1%) | 38 (6.3%) | 76 (7.4%) | 773 (5.4%) | |
| White | 29,943 (83%) | 6,061 (83%) | 2,556 (79%) | 7,713 (80%) | 515 (85%) | 861 (84%) | 12,237 (86%) | |
| Unknown | 6,830 | 1,009 | 1,032 | 2,193 | 101 | 336 | 2,159 | |
| BMILabled | <0.001 | |||||||
| Morbidly Obese | 1,926 (4.5%) | 465 (5.6%) | 173 (4.1%) | 693 (5.9%) | 24 (3.4%) | 42 (3.1%) | 529 (3.2%) | |
| Normal Weight | 13,726 (32%) | 2,431 (29%) | 1,367 (32%) | 3,384 (29%) | 275 (39%) | 478 (35%) | 5,791 (35%) | |
| Obese | 11,570 (27%) | 2,525 (31%) | 1,217 (29%) | 3,503 (30%) | 162 (23%) | 338 (25%) | 3,825 (23%) | |
| Overweight | 14,594 (34%) | 2,681 (32%) | 1,417 (33%) | 3,995 (34%) | 222 (32%) | 468 (34%) | 5,811 (35%) | |
| Underweight | 879 (2.1%) | 164 (2.0%) | 72 (1.7%) | 165 (1.4%) | 20 (2.8%) | 31 (2.3%) | 427 (2.6%) | |
| Unknown | 224 | 26 | 28 | 77 | 3 | 9 | 81 | |
| ASACLAS | <0.001 | |||||||
| 1-No Disturb | 428 (1.0%) | 104 (1.3%) | 103 (2.4%) | 139 (1.2%) | 2 (0.3%) | 11 (0.8%) | 69 (0.4%) | |
| 2-Mild Disturb | 10,403 (24%) | 2,376 (29%) | 1,055 (25%) | 3,003 (25%) | 153 (22%) | 337 (25%) | 3,479 (21%) | |
| 3-Severe Disturb | 29,056 (68%) | 5,337 (64%) | 2,765 (65%) | 7,844 (67%) | 488 (69%) | 886 (65%) | 11,736 (71%) | |
| 4-Life Threat | 2,951 (6.9%) | 458 (5.5%) | 336 (7.9%) | 805 (6.8%) | 61 (8.7%) | 129 (9.5%) | 1,162 (7.1%) | |
| Unknown | 81 | 17 | 15 | 26 | 2 | 3 | 18 | |
| DOpertoD | 609 (1.4%) | 74 (0.9%) | 101 (2.4%) | 101 (0.9%) | 12 (1.7%) | 55 (4.0%) | 266 (1.6%) | <0.001 |
| OPTIME | 270.00 (186.00, 373.00) | 215.00 (159.00, 288.00) | 262.00 (196.00, 342.00) | 193.00 (139.00, 264.00) | 345.00 (238.50, 447.75) | 310.00 (225.00, 409.00) | 356.00 (279.00, 445.00) | <0.001 |
| ELOS | 12,853 (30%) | 2,753 (33%) | 1,220 (29%) | 4,153 (35%) | 193 (28%) | 354 (26%) | 4,180 (26%) | <0.001 |
| Unknown | 242 | 25 | 21 | 19 | 8 | 10 | 159 | |
| READMISSION1 | 6,018 (18%) | 1,386 (22%) | 493 (15%) | 1,020 (12%) | 146 (25%) | 212 (20%) | 2,761 (21%) | <0.001 |
| Unknown | 9,960 | 1,937 | 981 | 3,003 | 124 | 317 | 3,598 | |
| STEROID | 1,290 (3.0%) | 294 (3.5%) | 115 (2.7%) | 384 (3.2%) | 32 (4.5%) | 34 (2.5%) | 431 (2.6%) | <0.001 |
| DIABETES | <0.001 | |||||||
| NO | 33,197 (77%) | 6,254 (75%) | 3,518 (82%) | 9,703 (82%) | 436 (62%) | 1,160 (85%) | 12,126 (74%) | |
| Yes | 9,722 (23%) | 2,038 (25%) | 756 (18%) | 2,114 (18%) | 270 (38%) | 206 (15%) | 4,338 (26%) | |
| HYPERMED | 21,343 (50%) | 4,266 (51%) | 1,912 (45%) | 5,520 (47%) | 350 (50%) | 589 (43%) | 8,706 (53%) | <0.001 |
| ASCITES | 169 (0.4%) | 19 (0.2%) | 16 (0.4%) | 71 (0.6%) | 6 (0.8%) | 9 (0.7%) | 48 (0.3%) | |
| HXCHF | 164 (0.4%) | 41 (0.5%) | 13 (0.3%) | 44 (0.4%) | 2 (0.3%) | 2 (0.1%) | 62 (0.4%) | 0.40 |
| DYSPNEA | ||||||||
| AT REST | 69 (0.2%) | 11 (0.1%) | 8 (0.2%) | 26 (0.2%) | 0 (0%) | 3 (0.2%) | 21 (0.1%) | |
| MODERATE EXERTION | 2,241 (5.2%) | 473 (5.7%) | 236 (5.5%) | 609 (5.2%) | 37 (5.2%) | 76 (5.6%) | 810 (4.9%) | |
| No | 40,609 (95%) | 7,808 (94%) | 4,030 (94%) | 11,182 (95%) | 669 (95%) | 1,287 (94%) | 15,633 (95%) | |
| SMOKE | 7,065 (16%) | 1,372 (17%) | 696 (16%) | 1,769 (15%) | 128 (18%) | 233 (17%) | 2,867 (17%) | <0.001 |
| HXCOPD | 1,670 (3.9%) | 354 (4.3%) | 141 (3.3%) | 452 (3.8%) | 21 (3.0%) | 47 (3.4%) | 655 (4.0%) | 0.076 |
| DIALYSIS | 151 (0.4%) | 50 (0.6%) | 13 (0.3%) | 37 (0.3%) | 3 (0.4%) | 1 (<0.1%) | 47 (0.3%) | |
| OPRENAFL | <0.001 | |||||||
| Acute Renal Failure | 373 (0.9%) | 38 (0.5%) | 59 (1.4%) | 69 (0.6%) | 6 (0.8%) | 34 (2.5%) | 167 (1.0%) | |
| No Complication | 42,546 (99%) | 8,254 (100%) | 4,215 (99%) | 11,748 (99%) | 700 (99%) | 1,332 (98%) | 16,297 (99%) | |
| FNSTATUS2 | ||||||||
| Independent | 42,512 (99%) | 8,207 (99%) | 4,239 (99%) | 11,710 (99%) | 697 (99%) | 1,359 (100%) | 16,300 (99%) | |
| Partially Dependent | 319 (0.7%) | 71 (0.9%) | 26 (0.6%) | 81 (0.7%) | 7 (1.0%) | 4 (0.3%) | 130 (0.8%) | |
| Totally Dependent | 24 (<0.1%) | 3 (<0.1%) | 2 (<0.1%) | 6 (<0.1%) | 0 (0%) | 0 (0%) | 13 (<0.1%) | |
| Unknown | 64 | 11 | 7 | 20 | 2 | 3 | 21 | |
| PAN_FISTULA | 4,318 (17%) | 1,511 (18%) | 0 (NA%) | 0 (NA%) | 49 (7.1%) | 0 (NA%) | 2,758 (17%) | <0.001 |
| Unknown | 17,684 | 79 | 4,274 | 11,817 | 11 | 1,366 | 137 | |
| PAN_DELGASTRIC_20140315 | 3,258 (13%) | 407 (4.9%) | 0 (NA%) | 0 (NA%) | 98 (14%) | 0 (NA%) | 2,753 (17%) | <0.001 |
| Unknown | 17,577 | 43 | 4,274 | 11,817 | 5 | 1,366 | 72 | |
| HEP_LIVERFAIL | 828 (4.7%) | 0 (NA%) | 388 (9.1%) | 253 (2.1%) | 0 (NA%) | 187 (14%) | 0 (NA%) | <0.001 |
| Unknown | 25,462 | 8,292 | 0 | 0 | 706 | 0 | 16,464 | |
| HEP_BILELEAKAGE | 1,252 (7.2%) | 0 (NA%) | 438 (10%) | 572 (4.9%) | 0 (NA%) | 242 (18%) | 0 (NA%) | <0.001 |
| Unknown | 25,589 | 8,292 | 35 | 84 | 706 | 8 | 16,464 | |
| CDARREST | <0.001 | |||||||
| Cardiac Arrest Requiring CPR | 375 (0.9%) | 50 (0.6%) | 59 (1.4%) | 43 (0.4%) | 12 (1.7%) | 18 (1.3%) | 193 (1.2%) | |
| No Complication | 42,544 (99%) | 8,242 (99%) | 4,215 (99%) | 11,774 (100%) | 694 (98%) | 1,348 (99%) | 16,271 (99%) | |
| CDMI | <0.001 | |||||||
| Myocardial Infarction | 434 (1.0%) | 78 (0.9%) | 52 (1.2%) | 80 (0.7%) | 9 (1.3%) | 16 (1.2%) | 199 (1.2%) | |
| No Complication | 42,485 (99%) | 8,214 (99%) | 4,222 (99%) | 11,737 (99%) | 697 (99%) | 1,350 (99%) | 16,265 (99%) | |
| OUPNEUMO | <0.001 | |||||||
| No Complication | 41,484 (97%) | 8,046 (97%) | 4,088 (96%) | 11,528 (98%) | 664 (94%) | 1,287 (94%) | 15,871 (96%) | |
| Pneumonia | 1,435 (3.3%) | 246 (3.0%) | 186 (4.4%) | 289 (2.4%) | 42 (5.9%) | 79 (5.8%) | 593 (3.6%) | |
| RENAINSF | ||||||||
| No Complication | 42,653 (99%) | 8,264 (100%) | 4,227 (99%) | 11,759 (100%) | 701 (99%) | 1,352 (99%) | 16,350 (99%) | |
| Progressive Renal Insufficiency | 266 (0.6%) | 28 (0.3%) | 47 (1.1%) | 58 (0.5%) | 5 (0.7%) | 14 (1.0%) | 114 (0.7%) | |
| PULEMBOL | <0.001 | |||||||
| No Complication | 42,386 (99%) | 8,171 (99%) | 4,198 (98%) | 11,703 (99%) | 698 (99%) | 1,345 (98%) | 16,271 (99%) | |
| Pulmonary Embolism | 533 (1.2%) | 121 (1.5%) | 76 (1.8%) | 114 (1.0%) | 8 (1.1%) | 21 (1.5%) | 193 (1.2%) | |
| OTHDVT | <0.001 | |||||||
| DVT Requiring Therapy | 957 (2.2%) | 167 (2.0%) | 100 (2.3%) | 119 (1.0%) | 24 (3.4%) | 65 (4.8%) | 482 (2.9%) | |
| No Complication | 41,962 (98%) | 8,125 (98%) | 4,174 (98%) | 11,698 (99%) | 682 (97%) | 1,301 (95%) | 15,982 (97%) | |
| RETURNOR | 1,768 (4.1%) | 279 (3.4%) | 191 (4.5%) | 243 (2.1%) | 55 (7.8%) | 76 (5.6%) | 924 (5.6%) | <0.001 |
| ORGSPCSSI | <0.001 | |||||||
| No Complication | 37,896 (88%) | 7,323 (88%) | 3,853 (90%) | 11,136 (94%) | 628 (89%) | 1,151 (84%) | 13,805 (84%) | |
| Organ/Space SSI | 5,023 (12%) | 969 (12%) | 421 (9.9%) | 681 (5.8%) | 78 (11%) | 215 (16%) | 2,659 (16%) | |
| PRSEPIS | ||||||||
| None | 42,360 (99%) | 8,165 (98%) | 4,191 (98%) | 11,683 (99%) | 697 (99%) | 1,342 (98%) | 16,282 (99%) | |
| Sepsis | 162 (0.4%) | 36 (0.4%) | 31 (0.7%) | 37 (0.3%) | 2 (0.3%) | 6 (0.4%) | 50 (0.3%) | |
| Septic Shock | 20 (<0.1%) | 6 (<0.1%) | 5 (0.1%) | 4 (<0.1%) | 2 (0.3%) | 0 (0%) | 3 (<0.1%) | |
| SIRS | 377 (0.9%) | 85 (1.0%) | 47 (1.1%) | 93 (0.8%) | 5 (0.7%) | 18 (1.3%) | 129 (0.8%) | |
| REINTUB | <0.001 | |||||||
| No Complication | 41,773 (97%) | 8,144 (98%) | 4,122 (96%) | 11,662 (99%) | 677 (96%) | 1,294 (95%) | 15,874 (96%) | |
| Unplanned Intubation | 1,146 (2.7%) | 148 (1.8%) | 152 (3.6%) | 155 (1.3%) | 29 (4.1%) | 72 (5.3%) | 590 (3.6%) | |
| URNINFEC | 0.004 | |||||||
| No Complication | 41,937 (98%) | 8,100 (98%) | 4,179 (98%) | 11,599 (98%) | 686 (97%) | 1,332 (98%) | 16,041 (97%) | |
| Urinary Tract Infection | 982 (2.3%) | 192 (2.3%) | 95 (2.2%) | 218 (1.8%) | 20 (2.8%) | 34 (2.5%) | 423 (2.6%) | |
| SUPINFEC | <0.001 | |||||||
| No Complication | 40,825 (95%) | 8,077 (97%) | 4,105 (96%) | 11,468 (97%) | 678 (96%) | 1,278 (94%) | 15,219 (92%) | |
| Superficial Incisional SSI | 2,094 (4.9%) | 215 (2.6%) | 169 (4.0%) | 349 (3.0%) | 28 (4.0%) | 88 (6.4%) | 1,245 (7.6%) | |
|
1
Statistics presented: n (%); Median (IQR)
2
Statistical tests performed: chi-square test of independence; Kruskal-Wallis test; Fisher's exact test
|
||||||||
ByProcedurePanc <-
subset(ByProcedure, PatientGroup == "Pancreas")
ByProcedurePanc %>%
tbl_summary(
by = PRNCPTX,
digits = all_continuous() ~ 2,) %>%
add_p(pvalue_fun = ~style_pvalue(.x, digits = 2))%>%
bold_p() %>%
add_overall() %>%
modify_header(label ~ "**Variable**") %>%
modify_spanning_header(c("stat_1", "stat_2") ~ "**Textbook Outcomes*") %>%
bold_labels() ## There was an error in 'add_p()' for variable 'PatientGroup', p-value omitted:
## Error in stats::chisq.test(data[[variable]], as.factor(data[[by]])): 'x' and 'y' must have at least 2 levels## There was an error in 'add_p()' for variable 'ASACLAS', p-value omitted:
## Error in stats::fisher.test(data[[variable]], as.factor(data[[by]])): FEXACT error 501.
## The hash table key cannot be computed because the largest key
## is larger than the largest representable int.
## The algorithm cannot proceed.
## Reduce the workspace, consider using 'simulate.p.value=TRUE' or another algorithm.## There was an error in 'add_p()' for variable 'DYSPNEA', p-value omitted:
## Error in stats::fisher.test(data[[variable]], as.factor(data[[by]])): FEXACT error 6. LDKEY=462 is too small for this problem,
## (ii := key2[itp=693] = 1021557532, ldstp=13860)
## Try increasing the size of the workspace and possibly 'mult'## There was an error in 'add_p()' for variable 'FNSTATUS2', p-value omitted:
## Error in stats::fisher.test(data[[variable]], as.factor(data[[by]])): FEXACT error 6. LDKEY=462 is too small for this problem,
## (ii := key2[itp=505] = 87232564, ldstp=13860)
## Try increasing the size of the workspace and possibly 'mult'## There was an error in 'add_p()' for variable 'HEP_LIVERFAIL', p-value omitted:
## Error in stats::chisq.test(data[[variable]], as.factor(data[[by]])): 'x' and 'y' must have at least 2 levels## There was an error in 'add_p()' for variable 'HEP_BILELEAKAGE', p-value omitted:
## Error in stats::chisq.test(data[[variable]], as.factor(data[[by]])): 'x' and 'y' must have at least 2 levels## There was an error in 'add_p()' for variable 'PRSEPIS', p-value omitted:
## Error in stats::fisher.test(data[[variable]], as.factor(data[[by]])): FEXACT error 501.
## The hash table key cannot be computed because the largest key
## is larger than the largest representable int.
## The algorithm cannot proceed.
## Reduce the workspace, consider using 'simulate.p.value=TRUE' or another algorithm.| Variable | Overall, N = 25,4621 | *Textbook Outcomes | Whipple, N = 16,4641 | p-value2 | |
|---|---|---|---|---|---|
| Distal Pancreatectomy, N = 8,2921 | Total Pancreatectomy, N = 7061 | ||||
| PatientGroup | |||||
| Pancreas | 25,462 (100%) | 8,292 (100%) | 706 (100%) | 16,464 (100%) | |
| DOptoDis | 7.00 (5.00, 10.00) | 5.00 (4.00, 7.00) | 8.00 (6.00, 12.00) | 8.00 (6.00, 12.00) | <0.001 |
| Unknown | 192 | 25 | 8 | 159 | |
| Age | <0.001 | ||||
| 0-65 | 11,893 (47%) | 4,420 (53%) | 371 (53%) | 7,102 (43%) | |
| 65-74 | 8,642 (34%) | 2,508 (30%) | 228 (32%) | 5,906 (36%) | |
| 75-84 | 4,492 (18%) | 1,249 (15%) | 99 (14%) | 3,144 (19%) | |
| 85+ | 435 (1.7%) | 115 (1.4%) | 8 (1.1%) | 312 (1.9%) | |
| SEX | <0.001 | ||||
| female | 12,510 (49%) | 4,540 (55%) | 353 (50%) | 7,617 (46%) | |
| male | 12,952 (51%) | 3,752 (45%) | 353 (50%) | 8,847 (54%) | |
| RACE_NEW | <0.001 | ||||
| Black | 2,180 (9.8%) | 833 (11%) | 52 (8.6%) | 1,295 (9.1%) | |
| Other | 1,200 (5.4%) | 389 (5.3%) | 38 (6.3%) | 773 (5.4%) | |
| White | 18,813 (85%) | 6,061 (83%) | 515 (85%) | 12,237 (86%) | |
| Unknown | 3,269 | 1,009 | 101 | 2,159 | |
| BMILabled | <0.001 | ||||
| Morbidly Obese | 1,018 (4.0%) | 465 (5.6%) | 24 (3.4%) | 529 (3.2%) | |
| Normal Weight | 8,497 (34%) | 2,431 (29%) | 275 (39%) | 5,791 (35%) | |
| Obese | 6,512 (26%) | 2,525 (31%) | 162 (23%) | 3,825 (23%) | |
| Overweight | 8,714 (34%) | 2,681 (32%) | 222 (32%) | 5,811 (35%) | |
| Underweight | 611 (2.4%) | 164 (2.0%) | 20 (2.8%) | 427 (2.6%) | |
| Unknown | 110 | 26 | 3 | 81 | |
| ASACLAS | |||||
| 1-No Disturb | 175 (0.7%) | 104 (1.3%) | 2 (0.3%) | 69 (0.4%) | |
| 2-Mild Disturb | 6,008 (24%) | 2,376 (29%) | 153 (22%) | 3,479 (21%) | |
| 3-Severe Disturb | 17,561 (69%) | 5,337 (64%) | 488 (69%) | 11,736 (71%) | |
| 4-Life Threat | 1,681 (6.6%) | 458 (5.5%) | 61 (8.7%) | 1,162 (7.1%) | |
| Unknown | 37 | 17 | 2 | 18 | |
| DOpertoD | 352 (1.4%) | 74 (0.9%) | 12 (1.7%) | 266 (1.6%) | <0.001 |
| OPTIME | 312.00 (221.00, 409.00) | 215.00 (159.00, 288.00) | 345.00 (238.50, 447.75) | 356.00 (279.00, 445.00) | <0.001 |
| ELOS | 7,126 (28%) | 2,753 (33%) | 193 (28%) | 4,180 (26%) | <0.001 |
| Unknown | 192 | 25 | 8 | 159 | |
| READMISSION1 | 4,293 (22%) | 1,386 (22%) | 146 (25%) | 2,761 (21%) | 0.11 |
| Unknown | 5,659 | 1,937 | 124 | 3,598 | |
| STEROID | 757 (3.0%) | 294 (3.5%) | 32 (4.5%) | 431 (2.6%) | <0.001 |
| DIABETES | <0.001 | ||||
| NO | 18,816 (74%) | 6,254 (75%) | 436 (62%) | 12,126 (74%) | |
| Yes | 6,646 (26%) | 2,038 (25%) | 270 (38%) | 4,338 (26%) | |
| HYPERMED | 13,322 (52%) | 4,266 (51%) | 350 (50%) | 8,706 (53%) | 0.035 |
| ASCITES | 73 (0.3%) | 19 (0.2%) | 6 (0.8%) | 48 (0.3%) | 0.025 |
| HXCHF | 105 (0.4%) | 41 (0.5%) | 2 (0.3%) | 62 (0.4%) | 0.37 |
| DYSPNEA | |||||
| AT REST | 32 (0.1%) | 11 (0.1%) | 0 (0%) | 21 (0.1%) | |
| MODERATE EXERTION | 1,320 (5.2%) | 473 (5.7%) | 37 (5.2%) | 810 (4.9%) | |
| No | 24,110 (95%) | 7,808 (94%) | 669 (95%) | 15,633 (95%) | |
| SMOKE | 4,367 (17%) | 1,372 (17%) | 128 (18%) | 2,867 (17%) | 0.18 |
| HXCOPD | 1,030 (4.0%) | 354 (4.3%) | 21 (3.0%) | 655 (4.0%) | 0.19 |
| DIALYSIS | 100 (0.4%) | 50 (0.6%) | 3 (0.4%) | 47 (0.3%) | <0.001 |
| OPRENAFL | <0.001 | ||||
| Acute Renal Failure | 211 (0.8%) | 38 (0.5%) | 6 (0.8%) | 167 (1.0%) | |
| No Complication | 25,251 (99%) | 8,254 (100%) | 700 (99%) | 16,297 (99%) | |
| FNSTATUS2 | |||||
| Independent | 25,204 (99%) | 8,207 (99%) | 697 (99%) | 16,300 (99%) | |
| Partially Dependent | 208 (0.8%) | 71 (0.9%) | 7 (1.0%) | 130 (0.8%) | |
| Totally Dependent | 16 (<0.1%) | 3 (<0.1%) | 0 (0%) | 13 (<0.1%) | |
| Unknown | 34 | 11 | 2 | 21 | |
| PAN_FISTULA | 4,318 (17%) | 1,511 (18%) | 49 (7.1%) | 2,758 (17%) | <0.001 |
| Unknown | 227 | 79 | 11 | 137 | |
| PAN_DELGASTRIC_20140315 | 3,258 (13%) | 407 (4.9%) | 98 (14%) | 2,753 (17%) | <0.001 |
| Unknown | 120 | 43 | 5 | 72 | |
| HEP_LIVERFAIL | 0 (NA%) | 0 (NA%) | 0 (NA%) | 0 (NA%) | |
| Unknown | 25,462 | 8,292 | 706 | 16,464 | |
| HEP_BILELEAKAGE | 0 (NA%) | 0 (NA%) | 0 (NA%) | 0 (NA%) | |
| Unknown | 25,462 | 8,292 | 706 | 16,464 | |
| CDARREST | <0.001 | ||||
| Cardiac Arrest Requiring CPR | 255 (1.0%) | 50 (0.6%) | 12 (1.7%) | 193 (1.2%) | |
| No Complication | 25,207 (99%) | 8,242 (99%) | 694 (98%) | 16,271 (99%) | |
| CDMI | 0.16 | ||||
| Myocardial Infarction | 286 (1.1%) | 78 (0.9%) | 9 (1.3%) | 199 (1.2%) | |
| No Complication | 25,176 (99%) | 8,214 (99%) | 697 (99%) | 16,265 (99%) | |
| OUPNEUMO | <0.001 | ||||
| No Complication | 24,581 (97%) | 8,046 (97%) | 664 (94%) | 15,871 (96%) | |
| Pneumonia | 881 (3.5%) | 246 (3.0%) | 42 (5.9%) | 593 (3.6%) | |
| RENAINSF | 0.001 | ||||
| No Complication | 25,315 (99%) | 8,264 (100%) | 701 (99%) | 16,350 (99%) | |
| Progressive Renal Insufficiency | 147 (0.6%) | 28 (0.3%) | 5 (0.7%) | 114 (0.7%) | |
| PULEMBOL | 0.15 | ||||
| No Complication | 25,140 (99%) | 8,171 (99%) | 698 (99%) | 16,271 (99%) | |
| Pulmonary Embolism | 322 (1.3%) | 121 (1.5%) | 8 (1.1%) | 193 (1.2%) | |
| OTHDVT | <0.001 | ||||
| DVT Requiring Therapy | 673 (2.6%) | 167 (2.0%) | 24 (3.4%) | 482 (2.9%) | |
| No Complication | 24,789 (97%) | 8,125 (98%) | 682 (97%) | 15,982 (97%) | |
| RETURNOR | 1,258 (4.9%) | 279 (3.4%) | 55 (7.8%) | 924 (5.6%) | <0.001 |
| ORGSPCSSI | <0.001 | ||||
| No Complication | 21,756 (85%) | 7,323 (88%) | 628 (89%) | 13,805 (84%) | |
| Organ/Space SSI | 3,706 (15%) | 969 (12%) | 78 (11%) | 2,659 (16%) | |
| PRSEPIS | |||||
| None | 25,144 (99%) | 8,165 (98%) | 697 (99%) | 16,282 (99%) | |
| Sepsis | 88 (0.3%) | 36 (0.4%) | 2 (0.3%) | 50 (0.3%) | |
| Septic Shock | 11 (<0.1%) | 6 (<0.1%) | 2 (0.3%) | 3 (<0.1%) | |
| SIRS | 219 (0.9%) | 85 (1.0%) | 5 (0.7%) | 129 (0.8%) | |
| REINTUB | <0.001 | ||||
| No Complication | 24,695 (97%) | 8,144 (98%) | 677 (96%) | 15,874 (96%) | |
| Unplanned Intubation | 767 (3.0%) | 148 (1.8%) | 29 (4.1%) | 590 (3.6%) | |
| URNINFEC | 0.41 | ||||
| No Complication | 24,827 (98%) | 8,100 (98%) | 686 (97%) | 16,041 (97%) | |
| Urinary Tract Infection | 635 (2.5%) | 192 (2.3%) | 20 (2.8%) | 423 (2.6%) | |
| SUPINFEC | <0.001 | ||||
| No Complication | 23,974 (94%) | 8,077 (97%) | 678 (96%) | 15,219 (92%) | |
| Superficial Incisional SSI | 1,488 (5.8%) | 215 (2.6%) | 28 (4.0%) | 1,245 (7.6%) | |
|
1
Statistics presented: n (%); Median (IQR)
2
Statistical tests performed: Kruskal-Wallis test; chi-square test of independence; Fisher's exact test
|
|||||
ByProcedureLiver <-
subset(ByProcedure, PatientGroup == "Liver")
ByProcedureLiver %>%
tbl_summary(
by = PRNCPTX,
digits = all_continuous() ~ 2,) %>%
add_p(pvalue_fun = ~style_pvalue(.x, digits = 2))%>%
bold_p() %>%
add_overall() %>%
modify_header(label ~ "**Variable**") %>%
modify_spanning_header(c("stat_1", "stat_2") ~ "**Textbook Outcomes*") %>%
bold_labels() ## There was an error in 'add_p()' for variable 'PatientGroup', p-value omitted:
## Error in stats::chisq.test(data[[variable]], as.factor(data[[by]])): 'x' and 'y' must have at least 2 levels## There was an error in 'add_p()' for variable 'DYSPNEA', p-value omitted:
## Error in stats::fisher.test(data[[variable]], as.factor(data[[by]])): FEXACT error 6. LDKEY=512 is too small for this problem,
## (ii := key2[itp=59] = 531740699, ldstp=15360)
## Try increasing the size of the workspace and possibly 'mult'## There was an error in 'add_p()' for variable 'PAN_FISTULA', p-value omitted:
## Error in stats::chisq.test(data[[variable]], as.factor(data[[by]])): 'x' and 'y' must have at least 2 levels## There was an error in 'add_p()' for variable 'PAN_DELGASTRIC_20140315', p-value omitted:
## Error in stats::chisq.test(data[[variable]], as.factor(data[[by]])): 'x' and 'y' must have at least 2 levels## There was an error in 'add_p()' for variable 'PRSEPIS', p-value omitted:
## Error in stats::fisher.test(data[[variable]], as.factor(data[[by]])): FEXACT error 501.
## The hash table key cannot be computed because the largest key
## is larger than the largest representable int.
## The algorithm cannot proceed.
## Reduce the workspace, consider using 'simulate.p.value=TRUE' or another algorithm.Underweight <-
PanTextbookOutcomes %>%
select(PatientGroup, DOptoDis, Age, SEX, RACE_NEW, BMILabled, ASACLAS, DOpertoD, TextbookOutcomeHep, TextbookOutcomePan, TextbookOutcomeAll)Underweight1 <-
Underweight %>%
mutate(Underweight, BMILabled1 = ifelse(BMILabled == "Morbidly Obese", "Everyone",
ifelse(BMILabled == "Obese", "Everyone",
ifelse(BMILabled == "Overweight", "Everyone",
ifelse(BMILabled == "Normal Weight", "Everyone",
ifelse(BMILabled == "Underweight", "Underweight", NA))))))
UnderweightPanc <-
subset(Underweight1, PatientGroup == "Pancreas")
UnderweightHepatic <-
subset(Underweight1, PatientGroup == "Liver")Underweight1 %>%
tbl_summary(
by = BMILabled1,
digits = all_continuous() ~ 2,) %>%
add_p(pvalue_fun = ~style_pvalue(.x, digits = 2))%>%
bold_p() %>%
add_overall() %>%
modify_header(label ~ "**Variable**") %>%
modify_spanning_header(c("stat_1", "stat_2") ~ "**Textbook Outcomes*") %>%
bold_labels() ## 229 observations missing `BMILabled1` have been removed. To include these observations, use `forcats::fct_explicit_na()` on `BMILabled1` column before passing to `tbl_summary()`.| Variable | Overall, N = 44,0061 | *Textbook Outcomes | p-value2 | |
|---|---|---|---|---|
| Everyone, N = 43,1031 | Underweight, N = 9031 | |||
| PatientGroup | <0.001 | |||
| Liver | 17,343 (41%) | 17,075 (41%) | 268 (30%) | |
| Pancreas | 25,352 (59%) | 24,741 (59%) | 611 (70%) | |
| Unknown | 1,311 | 1,287 | 24 | |
| DOptoDis | 6.00 (5.00, 9.00) | 6.00 (5.00, 9.00) | 7.00 (5.00, 10.00) | <0.001 |
| Unknown | 247 | 241 | 6 | |
| Age | 0.28 | |||
| 0-65 | 23,251 (53%) | 22,780 (53%) | 471 (52%) | |
| 65-74 | 13,622 (31%) | 13,357 (31%) | 265 (29%) | |
| 75-84 | 6,525 (15%) | 6,372 (15%) | 153 (17%) | |
| 85+ | 608 (1.4%) | 594 (1.4%) | 14 (1.6%) | |
| SEX | <0.001 | |||
| female | 21,905 (50%) | 21,268 (49%) | 637 (71%) | |
| male | 22,101 (50%) | 21,835 (51%) | 266 (29%) | |
| RACE_NEW | <0.001 | |||
| Black | 3,752 (10%) | 3,648 (10%) | 104 (13%) | |
| Other | 2,542 (6.8%) | 2,440 (6.7%) | 102 (13%) | |
| White | 30,898 (83%) | 30,320 (83%) | 578 (74%) | |
| Unknown | 6,814 | 6,695 | 119 | |
| BMILabled | <0.001 | |||
| Morbidly Obese | 2,019 (4.6%) | 2,019 (4.7%) | 0 (0%) | |
| Normal Weight | 14,063 (32%) | 14,063 (33%) | 0 (0%) | |
| Obese | 11,988 (27%) | 11,988 (28%) | 0 (0%) | |
| Overweight | 15,033 (34%) | 15,033 (35%) | 0 (0%) | |
| Underweight | 903 (2.1%) | 0 (0%) | 903 (100%) | |
| ASACLAS | 0.002 | |||
| 1-No Disturb | 437 (1.0%) | 432 (1.0%) | 5 (0.6%) | |
| 2-Mild Disturb | 10,786 (25%) | 10,604 (25%) | 182 (20%) | |
| 3-Severe Disturb | 29,759 (68%) | 29,122 (68%) | 637 (71%) | |
| 4-Life Threat | 2,949 (6.7%) | 2,871 (6.7%) | 78 (8.6%) | |
| Unknown | 75 | 74 | 1 | |
| DOpertoD | 613 (1.4%) | 597 (1.4%) | 16 (1.8%) | 0.40 |
| TextbookOutcomeHep | 6,906 (26%) | 6,809 (26%) | 97 (19%) | <0.001 |
| Unknown | 17,111 | 16,722 | 389 | |
| TextbookOutcomePan | 9,141 (30%) | 8,892 (30%) | 249 (37%) | <0.001 |
| Unknown | 13,938 | 13,711 | 227 | |
| TextbookOutcomeAll | 16,047 (45%) | 15,701 (45%) | 346 (46%) | 0.71 |
| Unknown | 8,283 | 8,138 | 145 | |
|
1
Statistics presented: n (%); Median (IQR)
2
Statistical tests performed: chi-square test of independence; Wilcoxon rank-sum test
|
||||
UnderweightPanc %>%
tbl_summary(
by = BMILabled1,
digits = all_continuous() ~ 2,) %>%
add_p(pvalue_fun = ~style_pvalue(.x, digits = 2))%>%
bold_p() %>%
add_overall() %>%
modify_header(label ~ "**Variable**") %>%
modify_spanning_header(c("stat_1", "stat_2") ~ "**Textbook Outcomes*") %>%
bold_labels() ## 110 observations missing `BMILabled1` have been removed. To include these observations, use `forcats::fct_explicit_na()` on `BMILabled1` column before passing to `tbl_summary()`.## There was an error in 'add_p()' for variable 'PatientGroup', p-value omitted:
## Error in stats::chisq.test(data[[variable]], as.factor(data[[by]])): 'x' and 'y' must have at least 2 levels## There was an error in 'add_p()' for variable 'TextbookOutcomeHep', p-value omitted:
## Error in stats::chisq.test(data[[variable]], as.factor(data[[by]])): 'x' and 'y' must have at least 2 levels| Variable | Overall, N = 25,3521 | *Textbook Outcomes | p-value2 | |
|---|---|---|---|---|
| Everyone, N = 24,7411 | Underweight, N = 6111 | |||
| PatientGroup | ||||
| Pancreas | 25,352 (100%) | 24,741 (100%) | 611 (100%) | |
| DOptoDis | 7.00 (5.00, 10.00) | 7.00 (5.00, 10.00) | 7.00 (6.00, 11.00) | 0.027 |
| Unknown | 189 | 185 | 4 | |
| Age | 0.15 | |||
| 0-65 | 11,846 (47%) | 11,543 (47%) | 303 (50%) | |
| 65-74 | 8,609 (34%) | 8,428 (34%) | 181 (30%) | |
| 75-84 | 4,465 (18%) | 4,349 (18%) | 116 (19%) | |
| 85+ | 432 (1.7%) | 421 (1.7%) | 11 (1.8%) | |
| SEX | <0.001 | |||
| female | 12,455 (49%) | 12,025 (49%) | 430 (70%) | |
| male | 12,897 (51%) | 12,716 (51%) | 181 (30%) | |
| RACE_NEW | <0.001 | |||
| Black | 2,178 (9.8%) | 2,099 (9.7%) | 79 (14%) | |
| Other | 1,197 (5.4%) | 1,138 (5.3%) | 59 (11%) | |
| White | 18,778 (85%) | 18,370 (85%) | 408 (75%) | |
| Unknown | 3,199 | 3,134 | 65 | |
| BMILabled | <0.001 | |||
| Morbidly Obese | 1,018 (4.0%) | 1,018 (4.1%) | 0 (0%) | |
| Normal Weight | 8,497 (34%) | 8,497 (34%) | 0 (0%) | |
| Obese | 6,512 (26%) | 6,512 (26%) | 0 (0%) | |
| Overweight | 8,714 (34%) | 8,714 (35%) | 0 (0%) | |
| Underweight | 611 (2.4%) | 0 (0%) | 611 (100%) | |
| ASACLAS | 0.002 | |||
| 1-No Disturb | 173 (0.7%) | 171 (0.7%) | 2 (0.3%) | |
| 2-Mild Disturb | 6,000 (24%) | 5,881 (24%) | 119 (19%) | |
| 3-Severe Disturb | 17,503 (69%) | 17,072 (69%) | 431 (71%) | |
| 4-Life Threat | 1,642 (6.5%) | 1,583 (6.4%) | 59 (9.7%) | |
| Unknown | 34 | 34 | 0 | |
| DOpertoD | 347 (1.4%) | 335 (1.4%) | 12 (2.0%) | 0.27 |
| TextbookOutcomeHep | ||||
| No | 11,988 (100%) | 11,716 (100%) | 272 (100%) | |
| Unknown | 13,364 | 13,025 | 339 | |
| TextbookOutcomePan | 8,736 (40%) | 8,495 (40%) | 241 (46%) | 0.007 |
| Unknown | 3,471 | 3,387 | 84 | |
| TextbookOutcomeAll | 8,736 (42%) | 8,495 (42%) | 241 (47%) | 0.028 |
| Unknown | 4,628 | 4,530 | 98 | |
|
1
Statistics presented: n (%); Median (IQR)
2
Statistical tests performed: Wilcoxon rank-sum test; chi-square test of independence; Fisher's exact test
|
||||
UnderweightHepatic %>%
tbl_summary(
by = BMILabled1,
digits = all_continuous() ~ 2,) %>%
add_p(pvalue_fun = ~style_pvalue(.x, digits = 2))%>%
bold_p() %>%
add_overall() %>%
modify_header(label ~ "**Variable**") %>%
modify_spanning_header(c("stat_1", "stat_2") ~ "**Textbook Outcomes*") %>%
bold_labels() ## 114 observations missing `BMILabled1` have been removed. To include these observations, use `forcats::fct_explicit_na()` on `BMILabled1` column before passing to `tbl_summary()`.## There was an error in 'add_p()' for variable 'PatientGroup', p-value omitted:
## Error in stats::chisq.test(data[[variable]], as.factor(data[[by]])): 'x' and 'y' must have at least 2 levels## There was an error in 'add_p()' for variable 'TextbookOutcomePan', p-value omitted:
## Error in stats::chisq.test(data[[variable]], as.factor(data[[by]])): 'x' and 'y' must have at least 2 levels| Variable | Overall, N = 17,3431 | *Textbook Outcomes | p-value2 | |
|---|---|---|---|---|
| Everyone, N = 17,0751 | Underweight, N = 2681 | |||
| PatientGroup | ||||
| Liver | 17,343 (100%) | 17,075 (100%) | 268 (100%) | |
| DOptoDis | 5.00 (4.00, 7.00) | 5.00 (4.00, 7.00) | 6.00 (4.00, 8.00) | <0.001 |
| Unknown | 49 | 48 | 1 | |
| Age | 0.43 | |||
| 0-65 | 10,666 (62%) | 10,512 (62%) | 154 (57%) | |
| 65-74 | 4,633 (27%) | 4,557 (27%) | 76 (28%) | |
| 75-84 | 1,893 (11%) | 1,857 (11%) | 36 (13%) | |
| 85+ | 151 (0.9%) | 149 (0.9%) | 2 (0.7%) | |
| SEX | <0.001 | |||
| female | 8,704 (50%) | 8,513 (50%) | 191 (71%) | |
| male | 8,639 (50%) | 8,562 (50%) | 77 (29%) | |
| RACE_NEW | <0.001 | |||
| Black | 1,466 (11%) | 1,442 (11%) | 24 (11%) | |
| Other | 1,290 (9.3%) | 1,250 (9.2%) | 40 (18%) | |
| White | 11,089 (80%) | 10,936 (80%) | 153 (71%) | |
| Unknown | 3,498 | 3,447 | 51 | |
| BMILabled | <0.001 | |||
| Morbidly Obese | 908 (5.2%) | 908 (5.3%) | 0 (0%) | |
| Normal Weight | 5,229 (30%) | 5,229 (31%) | 0 (0%) | |
| Obese | 5,058 (29%) | 5,058 (30%) | 0 (0%) | |
| Overweight | 5,880 (34%) | 5,880 (34%) | 0 (0%) | |
| Underweight | 268 (1.5%) | 0 (0%) | 268 (100%) | |
| ASACLAS | 0.37 | |||
| 1-No Disturb | 250 (1.4%) | 248 (1.5%) | 2 (0.7%) | |
| 2-Mild Disturb | 4,370 (25%) | 4,313 (25%) | 57 (21%) | |
| 3-Severe Disturb | 11,429 (66%) | 11,240 (66%) | 189 (71%) | |
| 4-Life Threat | 1,255 (7.3%) | 1,236 (7.3%) | 19 (7.1%) | |
| Unknown | 39 | 38 | 1 | |
| DOpertoD | 256 (1.5%) | 252 (1.5%) | 4 (1.5%) | 0.80 |
| TextbookOutcomeHep | 6,906 (47%) | 6,809 (48%) | 97 (41%) | 0.051 |
| Unknown | 2,780 | 2,749 | 31 | |
| TextbookOutcomePan | ||||
| No | 7,344 (100%) | 7,209 (100%) | 135 (100%) | |
| Unknown | 9,999 | 9,866 | 133 | |
| TextbookOutcomeAll | 6,906 (48%) | 6,809 (49%) | 97 (42%) | 0.048 |
| Unknown | 3,093 | 3,057 | 36 | |
|
1
Statistics presented: n (%); Median (IQR)
2
Statistical tests performed: Wilcoxon rank-sum test; Fisher's exact test; chi-square test of independence
|
||||
TextbookOutcomeOR <-
PantextBookOutcomesDT %>%
select(PRNCPTX, PatientGroup, Age, SEX, RACE_NEW, BMILabled, ASACLAS, DOpertoD, OPTIME, ELOS, READMISSION1, STEROID, DIABETES, HYPERMED, ASACLAS, ASCITES, HXCHF, DYSPNEA, SMOKE, HXCOPD, DIALYSIS, OPRENAFL, FNSTATUS2, AnyComplication, TextbookOutcomeHep, TextbookOutcomePan, TextbookOutcomeAll, PanComplications, HepComplications)TextbookOutcomeOR <- TextbookOutcomeOR %>%
mutate(TextbookOutcomeOR, TextbookOutcomePanOR = ifelse(TextbookOutcomePan =="Yes", "1",
ifelse(TextbookOutcomePan =="No", "0", NA)))
TextbookOutcomeOR$TextbookOutcomePanOR <- as.numeric(TextbookOutcomeOR$TextbookOutcomePanOR)
TextbookOutcomeOR <- TextbookOutcomeOR %>%
mutate(TextbookOutcomeOR, TextbookOutcomeHepOR = ifelse(TextbookOutcomeHep =="Yes", "1",
ifelse(TextbookOutcomeHep =="No", "0", NA)))
TextbookOutcomeOR <- TextbookOutcomeOR %>%
mutate(TextbookOutcomeOR, TextbookOutcomeAllOR = ifelse(TextbookOutcomeAll =="Yes", "1",
ifelse(TextbookOutcomeAll =="No", "0", NA)))
TextbookOutcomeOR$TextbookOutcomeHepOR <- as.numeric(TextbookOutcomeOR$TextbookOutcomeHepOR)
TextbookOutcomeOR$TextbookOutcomeAllOR <- as.numeric(TextbookOutcomeOR$TextbookOutcomeAllOR)
TextbookOutcomeOR$TextbookOutcomePanOR <- as.numeric(TextbookOutcomeOR$TextbookOutcomePanOR)PanTextbookOutcomesSubset <-
subset(TextbookOutcomeOR, PatientGroup == "Pancreas")HepTextbookOutcomesSubset <-
subset(TextbookOutcomeOR, PatientGroup == "Liver")
table(PanTextbookOutcomesSubset$TextbookOutcomePanOR)##
## 0 1
## 13214 8759PanTextbookOutcomesSubset$PRNCPTX <- as.factor(PanTextbookOutcomesSubset$PRNCPTX)
PanTextbookOutcomesSubset$SEX <- as.factor(PanTextbookOutcomesSubset$SEX)
PanTextbookOutcomesSubset$ASACLAS <- as.factor(PanTextbookOutcomesSubset$ASACLAS)
PanTextbookOutcomesSubset$BMILabled <- as.factor(PanTextbookOutcomesSubset$BMILabled)
PanTextbookOutcomesSubset$STEROID <- as.factor(PanTextbookOutcomesSubset$STEROID)
PanTextbookOutcomesSubset$DIABETES <- as.factor(PanTextbookOutcomesSubset$DIABETES)
PanTextbookOutcomesSubset$HYPERMED <- as.factor(PanTextbookOutcomesSubset$HYPERMED)
PanTextbookOutcomesSubset$ASCITES <- as.factor(PanTextbookOutcomesSubset$ASCITES)
PanTextbookOutcomesSubset$HXCOPD <- as.factor(PanTextbookOutcomesSubset$HXCOPD)
PanTextbookOutcomesSubset$DIALYSIS <- as.factor(PanTextbookOutcomesSubset$DIALYSIS)
PanTextbookOutcomesSubset$HXCHF <- as.factor(PanTextbookOutcomesSubset$HXCHF)
PanTextbookOutcomesSubset$Age <- as.factor(PanTextbookOutcomesSubset$Age)
PanTextbookOutcomesSubset$FNSTATUS2 <- as.factor(PanTextbookOutcomesSubset$FNSTATUS2)
PanTextbookOutcomesSubset$SMOKE <- as.factor(PanTextbookOutcomesSubset$SMOKE)
PanTextbookOutcomesSubset$PatientGroup <- as.factor(PanTextbookOutcomesSubset$PatientGroup)
PanTextbookOutcomesSubset$PRNCPTX = relevel(PanTextbookOutcomesSubset$PRNCPTX, ref="Distal Pancreatectomy")
PanTextbookOutcomesSubset$SEX = relevel(PanTextbookOutcomesSubset$SEX, ref="male")
PanTextbookOutcomesSubset$BMILabled = relevel(PanTextbookOutcomesSubset$BMILabled, ref="Overweight")
PanTextbookOutcomesSubset$ASACLAS = relevel(PanTextbookOutcomesSubset$ASACLAS, ref="4-Life Threat")
PanTextbookOutcomesSubset$DIABETES = relevel(PanTextbookOutcomesSubset$DIABETES, ref="NO")
PanTextbookOutcomesSubset$HYPERMED = relevel(PanTextbookOutcomesSubset$HYPERMED, ref="Yes")
PanTextbookOutcomesSubset$STEROID = relevel(PanTextbookOutcomesSubset$STEROID, ref="Yes")
PanTextbookOutcomesSubset$ASCITES = relevel(PanTextbookOutcomesSubset$ASCITES, ref="Yes")
PanTextbookOutcomesSubset$HXCOPD = relevel(PanTextbookOutcomesSubset$HXCOPD, ref="Yes")
PanTextbookOutcomesSubset$DIALYSIS = relevel(PanTextbookOutcomesSubset$DIALYSIS, ref="Yes")
PanTextbookOutcomesSubset$SMOKE = relevel(PanTextbookOutcomesSubset$SMOKE, ref="No")
PanTextbookOutcomesSubset$HXCHF = relevel(PanTextbookOutcomesSubset$HXCHF, ref="Yes")
PanTextbookOutcomesSubset$Age = relevel(PanTextbookOutcomesSubset$Age, ref="85+")
PanTextbookOutcomesSubset$FNSTATUS2 = relevel(PanTextbookOutcomesSubset$FNSTATUS2, ref="Totally Dependent")UVPan <- PanTextbookOutcomesSubset %>%
select(PRNCPTX, Age, SEX, RACE_NEW, BMILabled, ASACLAS, STEROID, ASCITES, DYSPNEA, SMOKE, DIALYSIS, DIABETES, HYPERMED, HXCOPD, HXCHF, TextbookOutcomePanOR) %>%
tbl_uvregression(
method = glm,
y = TextbookOutcomePanOR,
method.args = list(family = binomial),
exponentiate = TRUE,
pvalue_fun = ~style_pvalue(.x, digits = 2)
) %>%
bold_p(t = 0.10) %>%
bold_labels() %>%
italicize_levels()###changling the referent level
PanTextbookOutcomesSubset$PRNCPTX <- as.factor(PanTextbookOutcomesSubset$PRNCPTX)
PanTextbookOutcomesSubset$SEX <- as.factor(PanTextbookOutcomesSubset$SEX)
PanTextbookOutcomesSubset$ASACLAS <- as.factor(PanTextbookOutcomesSubset$ASACLAS)
PanTextbookOutcomesSubset$BMILabled <- as.factor(PanTextbookOutcomesSubset$BMILabled)
PanTextbookOutcomesSubset$STEROID <- as.factor(PanTextbookOutcomesSubset$STEROID)
PanTextbookOutcomesSubset$DIABETES <- as.factor(PanTextbookOutcomesSubset$DIABETES)
PanTextbookOutcomesSubset$HYPERMED <- as.factor(PanTextbookOutcomesSubset$HYPERMED)
PanTextbookOutcomesSubset$ASCITES <- as.factor(PanTextbookOutcomesSubset$ASCITES)
PanTextbookOutcomesSubset$HXCOPD <- as.factor(PanTextbookOutcomesSubset$HXCOPD)
PanTextbookOutcomesSubset$DIALYSIS <- as.factor(PanTextbookOutcomesSubset$DIALYSIS)
PanTextbookOutcomesSubset$HXCHF <- as.factor(PanTextbookOutcomesSubset$HXCHF)
PanTextbookOutcomesSubset$Age <- as.factor(PanTextbookOutcomesSubset$Age)
PanTextbookOutcomesSubset$FNSTATUS2 <- as.factor(PanTextbookOutcomesSubset$FNSTATUS2)
PanTextbookOutcomesSubset$PatientGroup <- as.factor(PanTextbookOutcomesSubset$PatientGroup)
PanTextbookOutcomesSubset$PRNCPTX = relevel(PanTextbookOutcomesSubset$PRNCPTX, ref="Distal Pancreatectomy")
PanTextbookOutcomesSubset$SEX = relevel(PanTextbookOutcomesSubset$SEX, ref="male")
PanTextbookOutcomesSubset$ASACLAS = relevel(PanTextbookOutcomesSubset$ASACLAS, ref="4-Life Threat")
PanTextbookOutcomesSubset$BMILabled = relevel(PanTextbookOutcomesSubset$BMILabled, ref="Normal Weight")
PanTextbookOutcomesSubset$DIABETES = relevel(PanTextbookOutcomesSubset$DIABETES, ref="NO")
PanTextbookOutcomesSubset$HYPERMED = relevel(PanTextbookOutcomesSubset$HYPERMED, ref="Yes")
PanTextbookOutcomesSubset$STEROID = relevel(PanTextbookOutcomesSubset$STEROID, ref="Yes")
PanTextbookOutcomesSubset$ASCITES = relevel(PanTextbookOutcomesSubset$ASCITES, ref="Yes")
PanTextbookOutcomesSubset$HXCOPD = relevel(PanTextbookOutcomesSubset$HXCOPD, ref="Yes")
PanTextbookOutcomesSubset$DIALYSIS = relevel(PanTextbookOutcomesSubset$DIALYSIS, ref="Yes")
PanTextbookOutcomesSubset$HXCHF = relevel(PanTextbookOutcomesSubset$HXCHF, ref="Yes")
PanTextbookOutcomesSubset$Age = relevel(PanTextbookOutcomesSubset$Age, ref="85+")
PanTextbookOutcomesSubset$FNSTATUS2 = relevel(PanTextbookOutcomesSubset$FNSTATUS2, ref="Totally Dependent")####Multivariant analysis serious complications
MVORPan <-
glm(TextbookOutcomePanOR ~ PRNCPTX + Age + SEX + RACE_NEW + BMILabled + ASACLAS + STEROID + ASCITES + DYSPNEA + SMOKE + DIALYSIS + DIABETES + HYPERMED + HXCOPD + HXCHF,
data = PanTextbookOutcomesSubset,
family = binomial("logit"),
na.action =na.omit)###continue on to merge the table sets
MVORTablePan <-
tbl_regression(MVORPan, exponentiate = T,
pvalue_fun = ~style_pvalue(.x, digits = 2),
) %>%
bold_p(t = 0.10) %>%
bold_labels() %>%
italicize_levels() tbl_merge(
list(UVPan, MVORTablePan),
tab_spanner = c("**Univariable**", "**Multivariable**")
) %>%
bold_labels() %>%
italicize_levels() | Characteristic | Univariable | Multivariable | |||||
|---|---|---|---|---|---|---|---|
| N | OR1 | 95% CI1 | p-value | OR1 | 95% CI1 | p-value | |
| PRNCPTX | 21,973 | ||||||
| Distal Pancreatectomy | — | — | — | — | |||
| Total Pancreatectomy | 1.07 | 0.90, 1.26 | 0.45 | 1.01 | 0.84, 1.21 | 0.91 | |
| Whipple | 1.03 | 0.97, 1.09 | 0.36 | 1.05 | 0.99, 1.12 | 0.10 | |
| Age | 21,973 | ||||||
| 85+ | — | — | — | — | |||
| 0-65 | 1.48 | 1.19, 1.84 | <0.001 | 1.48 | 1.17, 1.87 | <0.001 | |
| 65-74 | 1.40 | 1.13, 1.75 | 0.003 | 1.43 | 1.14, 1.81 | 0.002 | |
| 75-84 | 1.22 | 0.98, 1.54 | 0.075 | 1.26 | 1.00, 1.60 | 0.055 | |
| SEX | 21,973 | ||||||
| male | — | — | — | — | |||
| female | 1.21 | 1.15, 1.28 | <0.001 | 1.21 | 1.14, 1.28 | <0.001 | |
| RACE_NEW | 19,104 | ||||||
| Black | — | — | — | — | |||
| Other | 0.83 | 0.70, 0.97 | 0.017 | 0.72 | 0.61, 0.84 | <0.001 | |
| White | 1.09 | 0.99, 1.20 | 0.082 | 1.09 | 0.98, 1.20 | 0.11 | |
| BMILabled | 21,881 | ||||||
| Overweight | — | — | 0.87 | 0.81, 0.94 | <0.001 | ||
| Morbidly Obese | 0.70 | 0.61, 0.81 | <0.001 | 0.61 | 0.52, 0.71 | <0.001 | |
| Normal Weight | 1.16 | 1.09, 1.24 | <0.001 | — | — | ||
| Obese | 0.85 | 0.80, 0.92 | <0.001 | 0.73 | 0.67, 0.79 | <0.001 | |
| Underweight | 1.27 | 1.06, 1.52 | 0.008 | 1.12 | 0.93, 1.36 | 0.24 | |
| ASACLAS | 21,937 | ||||||
| 4-Life Threat | — | — | — | — | |||
| 1-No Disturb | 2.17 | 1.55, 3.03 | <0.001 | 1.93 | 1.29, 2.88 | 0.001 | |
| 2-Mild Disturb | 1.93 | 1.71, 2.19 | <0.001 | 1.78 | 1.52, 2.08 | <0.001 | |
| 3-Severe Disturb | 1.53 | 1.36, 1.72 | <0.001 | 1.48 | 1.28, 1.71 | <0.001 | |
| STEROID | 21,973 | ||||||
| Yes | — | — | — | — | |||
| No | 1.28 | 1.09, 1.52 | 0.003 | 1.23 | 1.03, 1.47 | 0.022 | |
| ASCITES | 21,973 | ||||||
| Yes | — | — | — | — | |||
| No | 1.55 | 0.94, 2.64 | 0.094 | 1.59 | 0.92, 2.84 | 0.10 | |
| DYSPNEA | 21,973 | ||||||
| AT REST | — | — | — | — | |||
| MODERATE EXERTION | 0.94 | 0.43, 2.20 | 0.87 | 0.99 | 0.41, 2.64 | >0.99 | |
| No | 1.35 | 0.62, 3.16 | 0.46 | 1.20 | 0.50, 3.17 | 0.69 | |
| SMOKE | 21,973 | ||||||
| No | — | — | — | — | |||
| Yes | 1.04 | 0.97, 1.12 | 0.25 | 1.03 | 0.95, 1.11 | 0.54 | |
| DIALYSIS | 21,973 | ||||||
| Yes | — | — | — | — | |||
| No | 2.55 | 1.55, 4.41 | <0.001 | 2.05 | 1.18, 3.77 | 0.015 | |
| DIABETES | 21,973 | ||||||
| NO | — | — | — | — | |||
| Yes | 0.94 | 0.88, 1.00 | 0.047 | 1.08 | 1.01, 1.16 | 0.026 | |
| HYPERMED | 21,973 | ||||||
| Yes | — | — | — | — | |||
| No | 1.19 | 1.13, 1.26 | <0.001 | 1.04 | 0.98, 1.11 | 0.21 | |
| HXCOPD | 21,973 | ||||||
| Yes | — | — | — | — | |||
| No | 1.62 | 1.40, 1.87 | <0.001 | 1.47 | 1.25, 1.72 | <0.001 | |
| HXCHF | 21,973 | ||||||
| Yes | — | — | — | — | |||
| No | 2.38 | 1.49, 3.95 | <0.001 | 1.59 | 0.96, 2.76 | 0.083 | |
|
1
OR = Odds Ratio, CI = Confidence Interval
|
|||||||
###Subset Distal Pancreatectomy
SubsetDistalPanc <-
subset(PanTextbookOutcomesSubset, PRNCPTX == "Distal Pancreatectomy")
UVPan1 <- SubsetDistalPanc %>%
select(Age, SEX, RACE_NEW, BMILabled, ASACLAS, STEROID, ASCITES, DYSPNEA, SMOKE, DIALYSIS, DIABETES, HYPERMED, HXCOPD, HXCHF, TextbookOutcomePanOR) %>%
tbl_uvregression(
method = glm,
y = TextbookOutcomePanOR,
method.args = list(family = binomial),
exponentiate = TRUE,
pvalue_fun = ~style_pvalue(.x, digits = 2)
) %>%
bold_p(t = 0.10) %>%
bold_labels() %>%
italicize_levels()###changling the referent level
SubsetDistalPanc$PRNCPTX <- as.factor(SubsetDistalPanc$PRNCPTX)
SubsetDistalPanc$SEX <- as.factor(SubsetDistalPanc$SEX)
SubsetDistalPanc$ASACLAS <- as.factor(SubsetDistalPanc$ASACLAS)
SubsetDistalPanc$BMILabled <- as.factor(SubsetDistalPanc$BMILabled)
SubsetDistalPanc$STEROID <- as.factor(SubsetDistalPanc$STEROID)
SubsetDistalPanc$DIABETES <- as.factor(SubsetDistalPanc$DIABETES)
SubsetDistalPanc$HYPERMED <- as.factor(SubsetDistalPanc$HYPERMED)
SubsetDistalPanc$ASCITES <- as.factor(SubsetDistalPanc$ASCITES)
SubsetDistalPanc$HXCOPD <- as.factor(SubsetDistalPanc$HXCOPD)
SubsetDistalPanc$DIALYSIS <- as.factor(SubsetDistalPanc$DIALYSIS)
SubsetDistalPanc$HXCHF <- as.factor(SubsetDistalPanc$HXCHF)
SubsetDistalPanc$Age <- as.factor(SubsetDistalPanc$Age)
SubsetDistalPanc$FNSTATUS2 <- as.factor(SubsetDistalPanc$FNSTATUS2)
SubsetDistalPanc$PatientGroup <- as.factor(SubsetDistalPanc$PatientGroup)
SubsetDistalPanc$PRNCPTX = relevel(SubsetDistalPanc$PRNCPTX, ref="Distal Pancreatectomy")
SubsetDistalPanc$SEX = relevel(SubsetDistalPanc$SEX, ref="male")
SubsetDistalPanc$ASACLAS = relevel(SubsetDistalPanc$ASACLAS, ref="4-Life Threat")
SubsetDistalPanc$BMILabled = relevel(SubsetDistalPanc$BMILabled, ref="Normal Weight")
SubsetDistalPanc$DIABETES = relevel(SubsetDistalPanc$DIABETES, ref="NO")
SubsetDistalPanc$HYPERMED = relevel(SubsetDistalPanc$HYPERMED, ref="Yes")
SubsetDistalPanc$STEROID = relevel(SubsetDistalPanc$STEROID, ref="Yes")
SubsetDistalPanc$ASCITES = relevel(SubsetDistalPanc$ASCITES, ref="Yes")
SubsetDistalPanc$HXCOPD = relevel(SubsetDistalPanc$HXCOPD, ref="Yes")
SubsetDistalPanc$DIALYSIS = relevel(SubsetDistalPanc$DIALYSIS, ref="Yes")
SubsetDistalPanc$HXCHF = relevel(SubsetDistalPanc$HXCHF, ref="Yes")
SubsetDistalPanc$Age = relevel(SubsetDistalPanc$Age, ref="85+")
SubsetDistalPanc$FNSTATUS2 = relevel(SubsetDistalPanc$FNSTATUS2, ref="Totally Dependent")####Multivariant analysis serious complications
MVORPan1 <-
glm(TextbookOutcomePanOR ~ Age + SEX + RACE_NEW + BMILabled + ASACLAS + STEROID + ASCITES + DYSPNEA + SMOKE + DIALYSIS + DIABETES + HYPERMED + HXCOPD + HXCHF,
data = SubsetDistalPanc,
family = binomial("logit"),
na.action =na.omit)###continue on to merge the table sets
MVORTablePan1 <-
tbl_regression(MVORPan1, exponentiate = T,
pvalue_fun = ~style_pvalue(.x, digits = 2),
) %>%
bold_p(t = 0.10) %>%
bold_labels() %>%
italicize_levels() tbl_merge(
list(UVPan1, MVORTablePan1),
tab_spanner = c("**Univariable**", "**Multivariable**")
) %>%
bold_labels() %>%
italicize_levels() | Characteristic | Univariable | Multivariable | |||||
|---|---|---|---|---|---|---|---|
| N | OR1 | 95% CI1 | p-value | OR1 | 95% CI1 | p-value | |
| Age | 7,115 | ||||||
| 85+ | — | — | — | — | |||
| 0-65 | 1.63 | 1.07, 2.56 | 0.027 | 1.51 | 0.97, 2.43 | 0.077 | |
| 65-74 | 1.74 | 1.14, 2.74 | 0.013 | 1.72 | 1.10, 2.77 | 0.020 | |
| 75-84 | 1.52 | 0.98, 2.41 | 0.067 | 1.50 | 0.95, 2.43 | 0.089 | |
| SEX | 7,115 | ||||||
| male | — | — | — | — | |||
| female | 1.29 | 1.17, 1.42 | <0.001 | 1.28 | 1.15, 1.43 | <0.001 | |
| RACE_NEW | 6,221 | ||||||
| Black | — | — | — | — | |||
| Other | 1.05 | 0.81, 1.38 | 0.70 | 0.90 | 0.68, 1.18 | 0.43 | |
| White | 1.11 | 0.95, 1.31 | 0.19 | 1.06 | 0.90, 1.26 | 0.47 | |
| BMILabled | 7,094 | ||||||
| Normal Weight | — | — | — | — | |||
| Overweight | 1.01 | 0.90, 1.14 | 0.86 | 1.07 | 0.94, 1.22 | 0.32 | |
| Morbidly Obese | 0.66 | 0.52, 0.82 | <0.001 | 0.75 | 0.59, 0.96 | 0.026 | |
| Obese | 0.84 | 0.75, 0.95 | 0.007 | 0.89 | 0.78, 1.03 | 0.11 | |
| Underweight | 1.18 | 0.83, 1.67 | 0.36 | 1.31 | 0.90, 1.92 | 0.16 | |
| ASACLAS | 7,098 | ||||||
| 4-Life Threat | — | — | — | — | |||
| 1-No Disturb | 2.90 | 1.83, 4.63 | <0.001 | 2.48 | 1.43, 4.33 | 0.001 | |
| 2-Mild Disturb | 2.41 | 1.91, 3.06 | <0.001 | 2.09 | 1.57, 2.81 | <0.001 | |
| 3-Severe Disturb | 1.66 | 1.33, 2.09 | <0.001 | 1.56 | 1.19, 2.06 | 0.002 | |
| STEROID | 7,115 | ||||||
| Yes | — | — | — | — | |||
| No | 1.20 | 0.93, 1.57 | 0.17 | 1.03 | 0.77, 1.38 | 0.85 | |
| ASCITES | 7,115 | ||||||
| Yes | — | — | — | — | |||
| No | 3.26 | 1.07, 14.1 | 0.062 | 2.69 | 0.87, 11.8 | 0.12 | |
| DYSPNEA | 7,115 | ||||||
| AT REST | — | — | — | — | |||
| MODERATE EXERTION | 0.86 | 0.22, 4.12 | 0.83 | 0.83 | 0.17, 5.95 | 0.83 | |
| No | 1.33 | 0.35, 6.32 | 0.68 | 1.10 | 0.23, 7.84 | 0.91 | |
| SMOKE | 7,115 | ||||||
| No | — | — | — | — | |||
| Yes | 0.80 | 0.70, 0.91 | <0.001 | 0.85 | 0.73, 0.98 | 0.028 | |
| DIALYSIS | 7,115 | ||||||
| Yes | — | — | — | — | |||
| No | 1.74 | 0.92, 3.52 | 0.10 | 1.68 | 0.81, 3.83 | 0.18 | |
| DIABETES | 7,115 | ||||||
| NO | — | — | — | — | |||
| Yes | 0.81 | 0.73, 0.91 | <0.001 | 0.94 | 0.82, 1.06 | 0.31 | |
| HYPERMED | 7,115 | ||||||
| Yes | — | — | — | — | |||
| No | 1.17 | 1.06, 1.29 | 0.001 | 0.99 | 0.88, 1.11 | 0.84 | |
| HXCOPD | 7,115 | ||||||
| Yes | — | — | — | — | |||
| No | 1.65 | 1.29, 2.12 | <0.001 | 1.33 | 1.01, 1.77 | 0.043 | |
| HXCHF | 7,115 | ||||||
| Yes | — | — | — | — | |||
| No | 1.76 | 0.88, 3.83 | 0.13 | 0.91 | 0.43, 2.07 | 0.81 | |
|
1
OR = Odds Ratio, CI = Confidence Interval
|
|||||||
###Subset Total Pancreatectomy
SubsettotalPanc <-
subset(PanTextbookOutcomesSubset, PRNCPTX == "Total Pancreatectomy")
UVPan2 <- SubsettotalPanc %>%
select(Age, SEX, RACE_NEW, BMILabled, ASACLAS, STEROID, ASCITES, DYSPNEA, SMOKE, DIALYSIS, DIABETES, HYPERMED, HXCOPD, HXCHF, TextbookOutcomePanOR) %>%
tbl_uvregression(
method = glm,
y = TextbookOutcomePanOR,
method.args = list(family = binomial),
exponentiate = TRUE,
pvalue_fun = ~style_pvalue(.x, digits = 2)
) %>%
bold_p(t = 0.10) %>%
bold_labels() %>%
italicize_levels()## Warning: Problem with `mutate()` input `tbl`.
## x glm.fit: fitted probabilities numerically 0 or 1 occurred
## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
## x glm.fit: fitted probabilities numerically 0 or 1 occurred
## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
## x glm.fit: fitted probabilities numerically 0 or 1 occurred
## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
## x glm.fit: fitted probabilities numerically 0 or 1 occurred
## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
## x glm.fit: fitted probabilities numerically 0 or 1 occurred
## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
## x glm.fit: fitted probabilities numerically 0 or 1 occurred
## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
## x glm.fit: fitted probabilities numerically 0 or 1 occurred
## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
## x glm.fit: fitted probabilities numerically 0 or 1 occurred
## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
## x glm.fit: fitted probabilities numerically 0 or 1 occurred
## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
## x glm.fit: fitted probabilities numerically 0 or 1 occurred
## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
## x glm.fit: fitted probabilities numerically 0 or 1 occurred
## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
## x glm.fit: fitted probabilities numerically 0 or 1 occurred
## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
## x glm.fit: fitted probabilities numerically 0 or 1 occurred
## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
## x glm.fit: fitted probabilities numerically 0 or 1 occurred
## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
## x glm.fit: fitted probabilities numerically 0 or 1 occurred
## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
## x glm.fit: fitted probabilities numerically 0 or 1 occurred
## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
## x glm.fit: fitted probabilities numerically 0 or 1 occurred
## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
## x glm.fit: fitted probabilities numerically 0 or 1 occurred
## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
## x glm.fit: fitted probabilities numerically 0 or 1 occurred
## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
## x glm.fit: fitted probabilities numerically 0 or 1 occurred
## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
## x glm.fit: fitted probabilities numerically 0 or 1 occurred
## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
## x glm.fit: fitted probabilities numerically 0 or 1 occurred
## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
## x glm.fit: fitted probabilities numerically 0 or 1 occurred
## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
## x glm.fit: fitted probabilities numerically 0 or 1 occurred
## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
## x glm.fit: fitted probabilities numerically 0 or 1 occurred
## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
## x glm.fit: fitted probabilities numerically 0 or 1 occurred
## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
## x glm.fit: fitted probabilities numerically 0 or 1 occurred
## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
## x glm.fit: fitted probabilities numerically 0 or 1 occurred
## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
## x glm.fit: fitted probabilities numerically 0 or 1 occurred
## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
## x glm.fit: fitted probabilities numerically 0 or 1 occurred
## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
## x glm.fit: fitted probabilities numerically 0 or 1 occurred
## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
## x glm.fit: fitted probabilities numerically 0 or 1 occurred
## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
## x glm.fit: fitted probabilities numerically 0 or 1 occurred
## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
## x glm.fit: fitted probabilities numerically 0 or 1 occurred
## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
## x glm.fit: fitted probabilities numerically 0 or 1 occurred
## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
## x glm.fit: fitted probabilities numerically 0 or 1 occurred
## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
## x glm.fit: fitted probabilities numerically 0 or 1 occurred
## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
## x glm.fit: fitted probabilities numerically 0 or 1 occurred
## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
## x glm.fit: fitted probabilities numerically 0 or 1 occurred
## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
## x glm.fit: fitted probabilities numerically 0 or 1 occurred
## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred###changling the referent level
SubsettotalPanc$PRNCPTX <- as.factor(SubsettotalPanc$PRNCPTX)
SubsettotalPanc$SEX <- as.factor(SubsettotalPanc$SEX)
SubsettotalPanc$ASACLAS <- as.factor(SubsettotalPanc$ASACLAS)
SubsettotalPanc$BMILabled <- as.factor(SubsettotalPanc$BMILabled)
SubsettotalPanc$STEROID <- as.factor(SubsettotalPanc$STEROID)
SubsettotalPanc$DIABETES <- as.factor(SubsettotalPanc$DIABETES)
SubsettotalPanc$HYPERMED <- as.factor(SubsettotalPanc$HYPERMED)
SubsettotalPanc$ASCITES <- as.factor(SubsettotalPanc$ASCITES)
SubsettotalPanc$HXCOPD <- as.factor(SubsettotalPanc$HXCOPD)
SubsettotalPanc$DIALYSIS <- as.factor(SubsettotalPanc$DIALYSIS)
SubsettotalPanc$HXCHF <- as.factor(SubsettotalPanc$HXCHF)
SubsettotalPanc$Age <- as.factor(SubsettotalPanc$Age)
SubsettotalPanc$FNSTATUS2 <- as.factor(SubsettotalPanc$FNSTATUS2)
SubsettotalPanc$PatientGroup <- as.factor(SubsettotalPanc$PatientGroup)
SubsettotalPanc$PRNCPTX = relevel(SubsettotalPanc$PRNCPTX, ref="Distal Pancreatectomy")
SubsettotalPanc$SEX = relevel(SubsettotalPanc$SEX, ref="male")
SubsettotalPanc$ASACLAS = relevel(SubsettotalPanc$ASACLAS, ref="4-Life Threat")
SubsettotalPanc$BMILabled = relevel(SubsettotalPanc$BMILabled, ref="Normal Weight")
SubsettotalPanc$DIABETES = relevel(SubsettotalPanc$DIABETES, ref="NO")
SubsettotalPanc$HYPERMED = relevel(SubsettotalPanc$HYPERMED, ref="Yes")
SubsettotalPanc$STEROID = relevel(SubsettotalPanc$STEROID, ref="Yes")
SubsettotalPanc$ASCITES = relevel(SubsettotalPanc$ASCITES, ref="Yes")
SubsettotalPanc$HXCOPD = relevel(SubsettotalPanc$HXCOPD, ref="Yes")
SubsettotalPanc$DIALYSIS = relevel(SubsettotalPanc$DIALYSIS, ref="Yes")
SubsettotalPanc$HXCHF = relevel(SubsettotalPanc$HXCHF, ref="Yes")
SubsettotalPanc$Age = relevel(SubsettotalPanc$Age, ref="85+")
SubsettotalPanc$FNSTATUS2 = relevel(SubsettotalPanc$FNSTATUS2, ref="Totally Dependent")####Multivariant analysis serious complications
MVORPan2 <-
glm(TextbookOutcomePanOR ~ Age + SEX + RACE_NEW + BMILabled + ASACLAS + STEROID + ASCITES + DYSPNEA + SMOKE + DIALYSIS + DIABETES + HYPERMED + HXCOPD + HXCHF,
data = SubsettotalPanc,
family = binomial("logit"),
na.action =na.omit)###continue on to merge the table sets
MVORTablePan2 <-
tbl_regression(MVORPan2, exponentiate = T,
pvalue_fun = ~style_pvalue(.x, digits = 2),
) %>%
bold_p(t = 0.10) %>%
bold_labels() %>%
italicize_levels() ## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: algorithm did not converge## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: algorithm did not converge## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' valuestbl_merge(
list(UVPan2, MVORTablePan2),
tab_spanner = c("**Univariable**", "**Multivariable**")
) %>%
bold_labels() %>%
italicize_levels() | Characteristic | Univariable | Multivariable | |||||
|---|---|---|---|---|---|---|---|
| N | OR1 | 95% CI1 | p-value | OR1 | 95% CI1 | p-value | |
| Age | 628 | ||||||
| 85+ | — | — | — | — | |||
| 0-65 | 1.12 | 0.27, 5.55 | 0.88 | 1.09 | 0.25, 5.56 | 0.91 | |
| 65-74 | 1.15 | 0.27, 5.72 | 0.85 | 1.08 | 0.24, 5.59 | 0.92 | |
| 75-84 | 1.31 | 0.30, 6.68 | 0.72 | 1.48 | 0.32, 7.98 | 0.62 | |
| SEX | 628 | ||||||
| male | — | — | — | — | |||
| female | 1.08 | 0.78, 1.48 | 0.64 | 0.96 | 0.67, 1.38 | 0.83 | |
| RACE_NEW | 535 | ||||||
| Black | — | — | — | — | |||
| Other | 0.56 | 0.21, 1.39 | 0.22 | 0.49 | 0.22, 1.29 | 0.15 | |
| White | 0.89 | 0.47, 1.68 | 0.71 | 0.80 | 0.41, 1.57 | 0.50 | |
| BMILabled | 625 | ||||||
| Normal Weight | — | — | — | — | |||
| Overweight | 0.78 | 0.53, 1.14 | 0.20 | 0.88 | 0.57, 1.36 | 0.56 | |
| Morbidly Obese | 0.82 | 0.33, 1.97 | 0.66 | 1.03 | 0.38, 2.71 | 0.95 | |
| Obese | 0.72 | 0.47, 1.09 | 0.13 | 0.77 | 0.48, 1.25 | 0.30 | |
| Underweight | 0.22 | 0.05, 0.69 | 0.019 | 0.21 | 0.03, 0.81 | 0.047 | |
| ASACLAS | 626 | ||||||
| 4-Life Threat | — | — | — | — | |||
| 1-No Disturb | 2.93 | 0.11, 77.3 | 0.46 | 0.00 | 0.99 | ||
| 2-Mild Disturb | 3.16 | 1.64, 6.38 | <0.001 | 2.98 | 1.25, 7.59 | 0.017 | |
| 3-Severe Disturb | 1.94 | 1.07, 3.70 | 0.035 | 2.04 | 0.93, 4.85 | 0.088 | |
| STEROID | 628 | ||||||
| Yes | — | — | — | — | |||
| No | 1.41 | 0.64, 3.33 | 0.41 | 1.34 | 0.55, 3.64 | 0.54 | |
| ASCITES | 628 | ||||||
| Yes | — | — | — | — | |||
| No | 1.39 | 0.27, 10.1 | 0.71 | 1.60 | 0.15, 35.8 | 0.71 | |
| DYSPNEA | 628 | ||||||
| MODERATE EXERTION | — | — | — | — | |||
| No | 1.41 | 0.71, 2.97 | 0.34 | 1.65 | 0.73, 4.00 | 0.24 | |
| SMOKE | 628 | ||||||
| No | — | — | — | — | |||
| Yes | 1.26 | 0.84, 1.90 | 0.26 | 1.25 | 0.77, 2.04 | 0.37 | |
| DIALYSIS | 628 | ||||||
| Yes | — | — | — | — | |||
| No | 1,475,264 | 0.00, NA | 0.98 | 1,675,257 | 0.00, NA | 0.98 | |
| DIABETES | 628 | ||||||
| NO | — | — | — | — | |||
| Yes | 0.90 | 0.65, 1.25 | 0.55 | 0.95 | 0.65, 1.40 | 0.81 | |
| HYPERMED | 628 | ||||||
| Yes | — | — | — | — | |||
| No | 1.14 | 0.83, 1.56 | 0.43 | 1.01 | 0.68, 1.52 | 0.94 | |
| HXCOPD | 628 | ||||||
| Yes | — | — | — | — | |||
| No | 1.04 | 0.42, 2.69 | 0.93 | 0.74 | 0.23, 2.56 | 0.63 | |
| HXCHF | 628 | ||||||
| Yes | — | — | — | — | |||
| No | 1,475,264 | 0.00, NA | 0.98 | 791,289 | 0.00, NA | 0.99 | |
|
1
OR = Odds Ratio, CI = Confidence Interval
|
|||||||
###Whipple
SubsetWhipple <-
subset(PanTextbookOutcomesSubset, PRNCPTX == "Whipple")
UVPan3 <- SubsetWhipple %>%
select(Age, SEX, RACE_NEW, BMILabled, ASACLAS, STEROID, ASCITES, DYSPNEA, SMOKE, DIALYSIS, DIABETES, HYPERMED, HXCOPD, HXCHF, TextbookOutcomePanOR) %>%
tbl_uvregression(
method = glm,
y = TextbookOutcomePanOR,
method.args = list(family = binomial),
exponentiate = TRUE,
pvalue_fun = ~style_pvalue(.x, digits = 2)
) %>%
bold_p(t = 0.10) %>%
bold_labels() %>%
italicize_levels()###changling the referent level
SubsetWhipple$PRNCPTX <- as.factor(SubsetWhipple$PRNCPTX)
SubsetWhipple$SEX <- as.factor(SubsetWhipple$SEX)
SubsetWhipple$ASACLAS <- as.factor(SubsetWhipple$ASACLAS)
SubsetWhipple$BMILabled <- as.factor(SubsetWhipple$BMILabled)
SubsetWhipple$STEROID <- as.factor(SubsetWhipple$STEROID)
SubsetWhipple$DIABETES <- as.factor(SubsetWhipple$DIABETES)
SubsetWhipple$HYPERMED <- as.factor(SubsetWhipple$HYPERMED)
SubsetWhipple$ASCITES <- as.factor(SubsetWhipple$ASCITES)
SubsetWhipple$HXCOPD <- as.factor(SubsetWhipple$HXCOPD)
SubsetWhipple$DIALYSIS <- as.factor(SubsetWhipple$DIALYSIS)
SubsetWhipple$HXCHF <- as.factor(SubsetWhipple$HXCHF)
SubsetWhipple$Age <- as.factor(SubsetWhipple$Age)
SubsetWhipple$FNSTATUS2 <- as.factor(SubsetWhipple$FNSTATUS2)
SubsetWhipple$PatientGroup <- as.factor(SubsetWhipple$PatientGroup)
SubsetWhipple$PRNCPTX = relevel(SubsetWhipple$PRNCPTX, ref="Distal Pancreatectomy")
SubsetWhipple$SEX = relevel(SubsetWhipple$SEX, ref="male")
SubsetWhipple$ASACLAS = relevel(SubsetWhipple$ASACLAS, ref="4-Life Threat")
SubsetWhipple$BMILabled = relevel(SubsetWhipple$BMILabled, ref="Normal Weight")
SubsetWhipple$DIABETES = relevel(SubsetWhipple$DIABETES, ref="NO")
SubsetWhipple$HYPERMED = relevel(SubsetWhipple$HYPERMED, ref="Yes")
SubsetWhipple$STEROID = relevel(SubsetWhipple$STEROID, ref="Yes")
SubsetWhipple$ASCITES = relevel(SubsetWhipple$ASCITES, ref="Yes")
SubsetWhipple$HXCOPD = relevel(SubsetWhipple$HXCOPD, ref="Yes")
SubsetWhipple$DIALYSIS = relevel(SubsetWhipple$DIALYSIS, ref="Yes")
SubsetWhipple$HXCHF = relevel(SubsetWhipple$HXCHF, ref="Yes")
SubsetWhipple$Age = relevel(SubsetWhipple$Age, ref="85+")
SubsetWhipple$FNSTATUS2 = relevel(SubsetWhipple$FNSTATUS2, ref="Totally Dependent")####Multivariant analysis serious complications
MVORPan3 <-
glm(TextbookOutcomePanOR ~ Age + SEX + RACE_NEW + BMILabled + ASACLAS + STEROID + ASCITES + DYSPNEA + SMOKE + DIALYSIS + DIABETES + HYPERMED + HXCOPD + HXCHF,
data = SubsetWhipple,
family = binomial("logit"),
na.action =na.omit)###continue on to merge the table sets
MVORTablePan3 <-
tbl_regression(MVORPan3, exponentiate = T,
pvalue_fun = ~style_pvalue(.x, digits = 2),
) %>%
bold_p(t = 0.10) %>%
bold_labels() %>%
italicize_levels() tbl_merge(
list(UVPan3, MVORTablePan3),
tab_spanner = c("**Univariable**", "**Multivariable**")
) %>%
bold_labels() %>%
italicize_levels() | Characteristic | Univariable | Multivariable | |||||
|---|---|---|---|---|---|---|---|
| N | OR1 | 95% CI1 | p-value | OR1 | 95% CI1 | p-value | |
| Age | 14,230 | ||||||
| 85+ | — | — | — | — | |||
| 0-65 | 1.47 | 1.14, 1.90 | 0.003 | 1.52 | 1.16, 2.00 | 0.003 | |
| 65-74 | 1.30 | 1.01, 1.69 | 0.045 | 1.35 | 1.03, 1.78 | 0.030 | |
| 75-84 | 1.13 | 0.87, 1.47 | 0.36 | 1.18 | 0.90, 1.57 | 0.24 | |
| SEX | 14,230 | ||||||
| male | — | — | — | — | |||
| female | 1.19 | 1.11, 1.27 | <0.001 | 1.18 | 1.10, 1.27 | <0.001 | |
| RACE_NEW | 12,348 | ||||||
| Black | — | — | — | — | |||
| Other | 0.74 | 0.60, 0.90 | 0.003 | 0.65 | 0.53, 0.79 | <0.001 | |
| White | 1.08 | 0.95, 1.22 | 0.24 | 1.11 | 0.98, 1.27 | 0.11 | |
| BMILabled | 14,162 | ||||||
| Normal Weight | — | — | — | — | |||
| Overweight | 0.80 | 0.74, 0.87 | <0.001 | 0.79 | 0.72, 0.86 | <0.001 | |
| Morbidly Obese | 0.58 | 0.47, 0.71 | <0.001 | 0.53 | 0.43, 0.66 | <0.001 | |
| Obese | 0.69 | 0.63, 0.75 | <0.001 | 0.65 | 0.59, 0.72 | <0.001 | |
| Underweight | 1.13 | 0.91, 1.39 | 0.26 | 1.10 | 0.87, 1.37 | 0.43 | |
| ASACLAS | 14,213 | ||||||
| 4-Life Threat | — | — | — | — | |||
| 1-No Disturb | 1.62 | 0.94, 2.76 | 0.075 | 1.50 | 0.79, 2.83 | 0.21 | |
| 2-Mild Disturb | 1.70 | 1.46, 1.98 | <0.001 | 1.61 | 1.34, 1.96 | <0.001 | |
| 3-Severe Disturb | 1.47 | 1.28, 1.69 | <0.001 | 1.45 | 1.22, 1.73 | <0.001 | |
| STEROID | 14,230 | ||||||
| Yes | — | — | — | — | |||
| No | 1.33 | 1.07, 1.66 | 0.010 | 1.38 | 1.10, 1.75 | 0.007 | |
| ASCITES | 14,230 | ||||||
| Yes | — | — | — | — | |||
| No | 1.25 | 0.69, 2.36 | 0.47 | 1.27 | 0.67, 2.54 | 0.47 | |
| DYSPNEA | 14,230 | ||||||
| AT REST | — | — | — | — | |||
| MODERATE EXERTION | 0.98 | 0.38, 2.84 | 0.97 | 1.05 | 0.37, 3.43 | 0.93 | |
| No | 1.36 | 0.53, 3.90 | 0.54 | 1.20 | 0.42, 3.90 | 0.74 | |
| SMOKE | 14,230 | ||||||
| No | — | — | — | — | |||
| Yes | 1.17 | 1.07, 1.28 | <0.001 | 1.12 | 1.01, 1.23 | 0.032 | |
| DIALYSIS | 14,230 | ||||||
| Yes | — | — | — | — | |||
| No | 3.91 | 1.77, 10.3 | 0.002 | 2.33 | 1.02, 6.31 | 0.064 | |
| DIABETES | 14,230 | ||||||
| NO | — | — | — | — | |||
| Yes | 1.01 | 0.93, 1.09 | 0.88 | 1.17 | 1.07, 1.28 | <0.001 | |
| HYPERMED | 14,230 | ||||||
| Yes | — | — | — | — | |||
| No | 1.21 | 1.13, 1.29 | <0.001 | 1.07 | 0.99, 1.16 | 0.095 | |
| HXCOPD | 14,230 | ||||||
| Yes | — | — | — | — | |||
| No | 1.63 | 1.36, 1.95 | <0.001 | 1.58 | 1.30, 1.93 | <0.001 | |
| HXCHF | 14,230 | ||||||
| Yes | — | — | — | — | |||
| No | 2.80 | 1.51, 5.70 | 0.002 | 2.34 | 1.17, 5.21 | 0.024 | |
|
1
OR = Odds Ratio, CI = Confidence Interval
|
|||||||
###changling the referent level
HepTextbookOutcomesSubset$PRNCPTX <- as.factor(HepTextbookOutcomesSubset$PRNCPTX)
HepTextbookOutcomesSubset$SEX <- as.factor(HepTextbookOutcomesSubset$SEX)
HepTextbookOutcomesSubset$BMILabled <- as.factor(HepTextbookOutcomesSubset$BMILabled)
HepTextbookOutcomesSubset$ASACLAS <- as.factor(HepTextbookOutcomesSubset$ASACLAS)
HepTextbookOutcomesSubset$STEROID <- as.factor(HepTextbookOutcomesSubset$STEROID)
HepTextbookOutcomesSubset$DIABETES <- as.factor(HepTextbookOutcomesSubset$DIABETES)
HepTextbookOutcomesSubset$HYPERMED <- as.factor(HepTextbookOutcomesSubset$HYPERMED)
HepTextbookOutcomesSubset$ASCITES <- as.factor(HepTextbookOutcomesSubset$ASCITES)
HepTextbookOutcomesSubset$HXCOPD <- as.factor(HepTextbookOutcomesSubset$HXCOPD)
HepTextbookOutcomesSubset$DIALYSIS <- as.factor(HepTextbookOutcomesSubset$DIALYSIS)
HepTextbookOutcomesSubset$HXCHF <- as.factor(HepTextbookOutcomesSubset$HXCHF)
HepTextbookOutcomesSubset$SMOKE <- as.factor(HepTextbookOutcomesSubset$SMOKE)
HepTextbookOutcomesSubset$Age <- as.factor(HepTextbookOutcomesSubset$Age)
HepTextbookOutcomesSubset$PRNCPTX = relevel(HepTextbookOutcomesSubset$PRNCPTX, ref="Trisegmentectomy")
HepTextbookOutcomesSubset$SEX = relevel(HepTextbookOutcomesSubset$SEX, ref="male")
HepTextbookOutcomesSubset$BMILabled = relevel(HepTextbookOutcomesSubset$BMILabled, ref="Normal Weight")
HepTextbookOutcomesSubset$ASACLAS = relevel(HepTextbookOutcomesSubset$ASACLAS, ref="4-Life Threat")
HepTextbookOutcomesSubset$DIABETES = relevel(HepTextbookOutcomesSubset$DIABETES, ref="Yes")
HepTextbookOutcomesSubset$HYPERMED = relevel(HepTextbookOutcomesSubset$HYPERMED, ref="Yes")
HepTextbookOutcomesSubset$STEROID = relevel(HepTextbookOutcomesSubset$STEROID, ref="Yes")
HepTextbookOutcomesSubset$ASCITES = relevel(HepTextbookOutcomesSubset$ASCITES, ref="Yes")
HepTextbookOutcomesSubset$HXCOPD = relevel(HepTextbookOutcomesSubset$HXCOPD, ref="Yes")
HepTextbookOutcomesSubset$DIALYSIS = relevel(HepTextbookOutcomesSubset$DIALYSIS, ref="Yes")
HepTextbookOutcomesSubset$HXCHF = relevel(HepTextbookOutcomesSubset$HXCHF, ref="Yes")
HepTextbookOutcomesSubset$SMOKE = relevel(HepTextbookOutcomesSubset$SMOKE, ref="Yes")
HepTextbookOutcomesSubset$Age = relevel(HepTextbookOutcomesSubset$Age, ref="85+")UVHep <- HepTextbookOutcomesSubset %>%
select(PRNCPTX, Age, SEX, RACE_NEW, BMILabled, ASACLAS, STEROID, ASCITES, DYSPNEA, SMOKE, DIALYSIS, DIABETES, HYPERMED, HXCOPD, HXCHF, TextbookOutcomeHepOR) %>%
tbl_uvregression(
method = glm,
y = TextbookOutcomeHepOR,
method.args = list(family = binomial),
exponentiate = TRUE,
pvalue_fun = ~style_pvalue(.x, digits = 2)
) %>%
bold_p(t = 0.10) %>%
bold_labels() %>%
italicize_levels()HepTextbookOutcomesSubset$PRNCPTX <- as.factor(HepTextbookOutcomesSubset$PRNCPTX)
HepTextbookOutcomesSubset$BMILabled <- as.factor(HepTextbookOutcomesSubset$BMILabled)
HepTextbookOutcomesSubset$ASACLAS <- as.factor(HepTextbookOutcomesSubset$ASACLAS)
HepTextbookOutcomesSubset$STEROID <- as.factor(HepTextbookOutcomesSubset$STEROID)
HepTextbookOutcomesSubset$DIABETES <- as.factor(HepTextbookOutcomesSubset$DIABETES)
HepTextbookOutcomesSubset$HYPERMED <- as.factor(HepTextbookOutcomesSubset$HYPERMED)
HepTextbookOutcomesSubset$ASCITES <- as.factor(HepTextbookOutcomesSubset$ASCITES)
HepTextbookOutcomesSubset$HXCOPD <- as.factor(HepTextbookOutcomesSubset$HXCOPD)
HepTextbookOutcomesSubset$DIALYSIS <- as.factor(HepTextbookOutcomesSubset$DIALYSIS)
HepTextbookOutcomesSubset$HXCHF <- as.factor(HepTextbookOutcomesSubset$HXCHF)
HepTextbookOutcomesSubset$SMOKE <- as.factor(HepTextbookOutcomesSubset$SMOKE)
HepTextbookOutcomesSubset$Age <- as.factor(HepTextbookOutcomesSubset$Age)
HepTextbookOutcomesSubset$PRNCPTX = relevel(HepTextbookOutcomesSubset$PRNCPTX, ref="Trisegmentectomy")
HepTextbookOutcomesSubset$BMILabled = relevel(HepTextbookOutcomesSubset$BMILabled, ref="Normal Weight")
HepTextbookOutcomesSubset$ASACLAS = relevel(HepTextbookOutcomesSubset$ASACLAS, ref="4-Life Threat")
HepTextbookOutcomesSubset$DIABETES = relevel(HepTextbookOutcomesSubset$DIABETES, ref="Yes")
HepTextbookOutcomesSubset$HYPERMED = relevel(HepTextbookOutcomesSubset$HYPERMED, ref="Yes")
HepTextbookOutcomesSubset$STEROID = relevel(HepTextbookOutcomesSubset$STEROID, ref="Yes")
HepTextbookOutcomesSubset$ASCITES = relevel(HepTextbookOutcomesSubset$ASCITES, ref="Yes")
HepTextbookOutcomesSubset$HXCOPD = relevel(HepTextbookOutcomesSubset$HXCOPD, ref="Yes")
HepTextbookOutcomesSubset$DIALYSIS = relevel(HepTextbookOutcomesSubset$DIALYSIS, ref="Yes")
HepTextbookOutcomesSubset$HXCHF = relevel(HepTextbookOutcomesSubset$HXCHF, ref="Yes")
HepTextbookOutcomesSubset$SMOKE = relevel(HepTextbookOutcomesSubset$SMOKE, ref="Yes")
HepTextbookOutcomesSubset$Age = relevel(HepTextbookOutcomesSubset$Age, ref="85+")####Multivariant analysis serious complications
MVORHep <-
glm(TextbookOutcomeHepOR ~ PRNCPTX + Age + SEX + RACE_NEW + BMILabled + ASACLAS + STEROID + ASCITES + DYSPNEA + SMOKE + DIALYSIS + DIABETES + HYPERMED + HXCOPD + HXCHF,
data = HepTextbookOutcomesSubset,
family = binomial("logit"),
na.action =na.omit)###continue on to merge the table sets
MVORTableHep <-
tbl_regression(MVORHep, exponentiate = T,
pvalue_fun = ~style_pvalue(.x, digits = 2),
) %>%
bold_p(t = 0.10) %>%
bold_labels() %>%
italicize_levels() tbl_merge(
list(UVHep, MVORTableHep),
tab_spanner = c("**Univariable**", "**Multivariable**")
) %>%
bold_labels() %>%
italicize_levels() | Characteristic | Univariable | Multivariable | |||||
|---|---|---|---|---|---|---|---|
| N | OR1 | 95% CI1 | p-value | OR1 | 95% CI1 | p-value | |
| PRNCPTX | 14,656 | ||||||
| Trisegmentectomy | — | — | — | — | |||
| Lobectomy | 1.30 | 1.14, 1.49 | <0.001 | 1.28 | 1.10, 1.50 | 0.002 | |
| Partial Lobectomy | 1.36 | 1.20, 1.54 | <0.001 | 1.36 | 1.18, 1.57 | <0.001 | |
| Age | 14,656 | ||||||
| 85+ | — | — | — | — | |||
| 0-65 | 1.94 | 1.36, 2.82 | <0.001 | 1.83 | 1.21, 2.80 | 0.004 | |
| 65-74 | 1.40 | 0.98, 2.04 | 0.071 | 1.48 | 0.98, 2.27 | 0.066 | |
| 75-84 | 1.09 | 0.75, 1.60 | 0.65 | 1.17 | 0.77, 1.81 | 0.46 | |
| SEX | 14,656 | ||||||
| male | — | — | — | — | |||
| female | 1.36 | 1.27, 1.45 | <0.001 | 1.27 | 1.18, 1.37 | <0.001 | |
| RACE_NEW | 11,659 | ||||||
| Black | — | — | — | — | |||
| Other | 0.96 | 0.82, 1.14 | 0.67 | 0.95 | 0.80, 1.13 | 0.56 | |
| White | 1.11 | 0.99, 1.25 | 0.084 | 1.07 | 0.95, 1.21 | 0.25 | |
| BMILabled | 14,563 | ||||||
| Normal Weight | — | — | — | — | |||
| Morbidly Obese | 0.96 | 0.82, 1.12 | 0.59 | 0.96 | 0.81, 1.15 | 0.67 | |
| Obese | 1.03 | 0.95, 1.12 | 0.51 | 1.09 | 0.99, 1.21 | 0.089 | |
| Overweight | 1.08 | 0.99, 1.17 | 0.068 | 1.15 | 1.05, 1.27 | 0.003 | |
| Underweight | 0.79 | 0.60, 1.03 | 0.082 | 0.73 | 0.54, 0.99 | 0.042 | |
| ASACLAS | 14,617 | ||||||
| 4-Life Threat | — | — | — | — | |||
| 1-No Disturb | 3.24 | 2.40, 4.39 | <0.001 | 2.10 | 1.31, 3.43 | 0.002 | |
| 2-Mild Disturb | 2.62 | 2.28, 3.02 | <0.001 | 1.84 | 1.52, 2.23 | <0.001 | |
| 3-Severe Disturb | 1.76 | 1.55, 2.01 | <0.001 | 1.37 | 1.15, 1.64 | <0.001 | |
| STEROID | 14,656 | ||||||
| Yes | — | — | — | — | |||
| No | 1.27 | 1.05, 1.54 | 0.013 | 1.17 | 0.95, 1.44 | 0.15 | |
| ASCITES | 14,656 | ||||||
| Yes | — | — | — | — | |||
| No | 3.34 | 2.06, 5.71 | <0.001 | 3.01 | 1.78, 5.37 | <0.001 | |
| DYSPNEA | 14,656 | ||||||
| AT REST | — | — | — | — | |||
| MODERATE EXERTION | 1.36 | 0.67, 2.93 | 0.40 | 1.21 | 0.57, 2.69 | 0.62 | |
| No | 2.01 | 1.01, 4.28 | 0.055 | 1.47 | 0.71, 3.22 | 0.31 | |
| SMOKE | 14,656 | ||||||
| Yes | — | — | — | — | |||
| No | 1.10 | 1.01, 1.20 | 0.038 | 1.03 | 0.93, 1.15 | 0.56 | |
| DIALYSIS | 14,656 | ||||||
| Yes | — | — | — | — | |||
| No | 2.55 | 1.32, 5.31 | 0.008 | 1.75 | 0.85, 3.88 | 0.14 | |
| DIABETES | 14,656 | ||||||
| Yes | — | — | — | — | |||
| NO | 1.46 | 1.34, 1.59 | <0.001 | 1.23 | 1.11, 1.37 | <0.001 | |
| HYPERMED | 14,656 | ||||||
| Yes | — | — | — | — | |||
| No | 1.34 | 1.26, 1.43 | <0.001 | 1.10 | 1.01, 1.19 | 0.032 | |
| HXCOPD | 14,656 | ||||||
| Yes | — | — | — | — | |||
| No | 1.64 | 1.38, 1.96 | <0.001 | 1.24 | 1.01, 1.51 | 0.040 | |
| HXCHF | 14,656 | ||||||
| Yes | — | — | — | — | |||
| No | 2.85 | 1.57, 5.55 | <0.001 | 2.01 | 1.04, 4.19 | 0.048 | |
|
1
OR = Odds Ratio, CI = Confidence Interval
|
|||||||
####Partiallobectomy
SubsetPartialLobectomy <-
subset(HepTextbookOutcomesSubset, PRNCPTX == "Partial Lobectomy")
UVHep1 <- HepTextbookOutcomesSubset %>%
select(Age, SEX, RACE_NEW, BMILabled, ASACLAS, STEROID, ASCITES, DYSPNEA, SMOKE, DIALYSIS, DIABETES, HYPERMED, HXCOPD, HXCHF, TextbookOutcomeHepOR) %>%
tbl_uvregression(
method = glm,
y = TextbookOutcomeHepOR,
method.args = list(family = binomial),
exponentiate = TRUE,
pvalue_fun = ~style_pvalue(.x, digits = 2)
) %>%
bold_p(t = 0.10) %>%
bold_labels() %>%
italicize_levels()SubsetPartialLobectomy$PRNCPTX <- as.factor(SubsetPartialLobectomy$PRNCPTX)
SubsetPartialLobectomy$BMILabled <- as.factor(SubsetPartialLobectomy$BMILabled)
SubsetPartialLobectomy$ASACLAS <- as.factor(SubsetPartialLobectomy$ASACLAS)
SubsetPartialLobectomy$STEROID <- as.factor(SubsetPartialLobectomy$STEROID)
SubsetPartialLobectomy$DIABETES <- as.factor(SubsetPartialLobectomy$DIABETES)
SubsetPartialLobectomy$HYPERMED <- as.factor(SubsetPartialLobectomy$HYPERMED)
SubsetPartialLobectomy$ASCITES <- as.factor(SubsetPartialLobectomy$ASCITES)
SubsetPartialLobectomy$HXCOPD <- as.factor(SubsetPartialLobectomy$HXCOPD)
SubsetPartialLobectomy$DIALYSIS <- as.factor(SubsetPartialLobectomy$DIALYSIS)
SubsetPartialLobectomy$HXCHF <- as.factor(SubsetPartialLobectomy$HXCHF)
SubsetPartialLobectomy$SMOKE <- as.factor(SubsetPartialLobectomy$SMOKE)
SubsetPartialLobectomy$Age <- as.factor(SubsetPartialLobectomy$Age)
SubsetPartialLobectomy$PRNCPTX = relevel(SubsetPartialLobectomy$PRNCPTX, ref="Trisegmentectomy")
SubsetPartialLobectomy$BMILabled = relevel(SubsetPartialLobectomy$BMILabled, ref="Normal Weight")
SubsetPartialLobectomy$ASACLAS = relevel(SubsetPartialLobectomy$ASACLAS, ref="4-Life Threat")
SubsetPartialLobectomy$DIABETES = relevel(SubsetPartialLobectomy$DIABETES, ref="Yes")
SubsetPartialLobectomy$HYPERMED = relevel(SubsetPartialLobectomy$HYPERMED, ref="Yes")
SubsetPartialLobectomy$STEROID = relevel(SubsetPartialLobectomy$STEROID, ref="Yes")
SubsetPartialLobectomy$ASCITES = relevel(SubsetPartialLobectomy$ASCITES, ref="Yes")
SubsetPartialLobectomy$HXCOPD = relevel(SubsetPartialLobectomy$HXCOPD, ref="Yes")
SubsetPartialLobectomy$DIALYSIS = relevel(SubsetPartialLobectomy$DIALYSIS, ref="Yes")
SubsetPartialLobectomy$HXCHF = relevel(SubsetPartialLobectomy$HXCHF, ref="Yes")
SubsetPartialLobectomy$SMOKE = relevel(SubsetPartialLobectomy$SMOKE, ref="Yes")
SubsetPartialLobectomy$Age = relevel(SubsetPartialLobectomy$Age, ref="85+")####Multivariant analysis serious complications
MVORHep1 <-
glm(TextbookOutcomeHepOR ~ Age + SEX + RACE_NEW + BMILabled + ASACLAS + STEROID + ASCITES + DYSPNEA + SMOKE + DIALYSIS + DIABETES + HYPERMED + HXCOPD + HXCHF,
data = SubsetPartialLobectomy,
family = binomial("logit"),
na.action =na.omit)###continue on to merge the table sets
MVORTableHep1 <-
tbl_regression(MVORHep1, exponentiate = T,
pvalue_fun = ~style_pvalue(.x, digits = 2),
) %>%
bold_p(t = 0.10) %>%
bold_labels() %>%
italicize_levels() tbl_merge(
list(UVHep1, MVORTableHep1),
tab_spanner = c("**Univariable**", "**Multivariable**")
) %>%
bold_labels() %>%
italicize_levels() | Characteristic | Univariable | Multivariable | |||||
|---|---|---|---|---|---|---|---|
| N | OR1 | 95% CI1 | p-value | OR1 | 95% CI1 | p-value | |
| Age | 14,656 | ||||||
| 85+ | — | — | — | — | |||
| 0-65 | 1.94 | 1.36, 2.82 | <0.001 | 1.97 | 1.24, 3.18 | 0.005 | |
| 65-74 | 1.40 | 0.98, 2.04 | 0.071 | 1.64 | 1.03, 2.65 | 0.039 | |
| 75-84 | 1.09 | 0.75, 1.60 | 0.65 | 1.25 | 0.78, 2.05 | 0.36 | |
| SEX | 14,656 | ||||||
| male | — | — | — | — | |||
| female | 1.36 | 1.27, 1.45 | <0.001 | 1.28 | 1.17, 1.41 | <0.001 | |
| RACE_NEW | 11,659 | ||||||
| Black | — | — | — | — | |||
| Other | 0.96 | 0.82, 1.14 | 0.67 | 0.99 | 0.80, 1.22 | 0.89 | |
| White | 1.11 | 0.99, 1.25 | 0.084 | 1.06 | 0.92, 1.23 | 0.40 | |
| BMILabled | 14,563 | ||||||
| Normal Weight | — | — | — | — | |||
| Morbidly Obese | 0.96 | 0.82, 1.12 | 0.59 | 0.97 | 0.79, 1.18 | 0.75 | |
| Obese | 1.03 | 0.95, 1.12 | 0.51 | 1.07 | 0.95, 1.21 | 0.28 | |
| Overweight | 1.08 | 0.99, 1.17 | 0.068 | 1.11 | 0.99, 1.25 | 0.072 | |
| Underweight | 0.79 | 0.60, 1.03 | 0.082 | 0.78 | 0.53, 1.14 | 0.20 | |
| ASACLAS | 14,617 | ||||||
| 4-Life Threat | — | — | — | — | |||
| 1-No Disturb | 3.24 | 2.40, 4.39 | <0.001 | 2.17 | 1.26, 3.81 | 0.006 | |
| 2-Mild Disturb | 2.62 | 2.28, 3.02 | <0.001 | 2.01 | 1.60, 2.55 | <0.001 | |
| 3-Severe Disturb | 1.76 | 1.55, 2.01 | <0.001 | 1.39 | 1.12, 1.73 | 0.003 | |
| STEROID | 14,656 | ||||||
| Yes | — | — | — | — | |||
| No | 1.27 | 1.05, 1.54 | 0.013 | 1.15 | 0.90, 1.47 | 0.27 | |
| ASCITES | 14,656 | ||||||
| Yes | — | — | — | — | |||
| No | 3.34 | 2.06, 5.71 | <0.001 | 3.80 | 2.02, 7.80 | <0.001 | |
| DYSPNEA | 14,656 | ||||||
| AT REST | — | — | — | — | |||
| MODERATE EXERTION | 1.36 | 0.67, 2.93 | 0.40 | 0.76 | 0.31, 1.86 | 0.53 | |
| No | 2.01 | 1.01, 4.28 | 0.055 | 0.92 | 0.39, 2.23 | 0.85 | |
| SMOKE | 14,656 | ||||||
| Yes | — | — | — | — | |||
| No | 1.10 | 1.01, 1.20 | 0.038 | 1.00 | 0.88, 1.14 | 0.97 | |
| DIALYSIS | 14,656 | ||||||
| Yes | — | — | — | — | |||
| No | 2.55 | 1.32, 5.31 | 0.008 | 1.65 | 0.73, 4.08 | 0.25 | |
| DIABETES | 14,656 | ||||||
| Yes | — | — | — | — | |||
| NO | 1.46 | 1.34, 1.59 | <0.001 | 1.20 | 1.06, 1.36 | 0.003 | |
| HYPERMED | 14,656 | ||||||
| Yes | — | — | — | — | |||
| No | 1.34 | 1.26, 1.43 | <0.001 | 1.10 | 0.99, 1.21 | 0.073 | |
| HXCOPD | 14,656 | ||||||
| Yes | — | — | — | — | |||
| No | 1.64 | 1.38, 1.96 | <0.001 | 1.27 | 1.00, 1.61 | 0.050 | |
| HXCHF | 14,656 | ||||||
| Yes | — | — | — | — | |||
| No | 2.85 | 1.57, 5.55 | <0.001 | 1.58 | 0.77, 3.50 | 0.23 | |
|
1
OR = Odds Ratio, CI = Confidence Interval
|
|||||||
####Trisegmentectomy
SubsetTrisegmentectomy<-
subset(HepTextbookOutcomesSubset, PRNCPTX == "Trisegmentectomy")
UVHep2 <- SubsetTrisegmentectomy %>%
select(Age, SEX, RACE_NEW, BMILabled, ASACLAS, STEROID, ASCITES, DYSPNEA, SMOKE, DIALYSIS, DIABETES, HYPERMED, HXCOPD, HXCHF, TextbookOutcomeHepOR) %>%
tbl_uvregression(
method = glm,
y = TextbookOutcomeHepOR,
method.args = list(family = binomial),
exponentiate = TRUE,
pvalue_fun = ~style_pvalue(.x, digits = 2)
) %>%
bold_p(t = 0.10) %>%
bold_labels() %>%
italicize_levels()## Warning: Problem with `mutate()` input `tbl`.
## x glm.fit: fitted probabilities numerically 0 or 1 occurred
## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
## x glm.fit: fitted probabilities numerically 0 or 1 occurred
## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
## x glm.fit: fitted probabilities numerically 0 or 1 occurred
## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
## x glm.fit: fitted probabilities numerically 0 or 1 occurred
## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
## x glm.fit: fitted probabilities numerically 0 or 1 occurred
## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
## x glm.fit: fitted probabilities numerically 0 or 1 occurred
## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
## x glm.fit: fitted probabilities numerically 0 or 1 occurred
## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
## x glm.fit: fitted probabilities numerically 0 or 1 occurred
## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
## x glm.fit: fitted probabilities numerically 0 or 1 occurred
## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
## x glm.fit: fitted probabilities numerically 0 or 1 occurred
## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
## x glm.fit: fitted probabilities numerically 0 or 1 occurred
## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
## x glm.fit: fitted probabilities numerically 0 or 1 occurred
## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
## x glm.fit: fitted probabilities numerically 0 or 1 occurred
## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
## x glm.fit: fitted probabilities numerically 0 or 1 occurred
## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
## x glm.fit: fitted probabilities numerically 0 or 1 occurred
## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
## x glm.fit: fitted probabilities numerically 0 or 1 occurred
## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
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## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
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## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
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## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
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## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
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## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
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## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
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## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
## x glm.fit: fitted probabilities numerically 0 or 1 occurred
## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
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## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
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## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
## x glm.fit: fitted probabilities numerically 0 or 1 occurred
## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
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## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
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## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
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## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
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## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
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## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
## x glm.fit: fitted probabilities numerically 0 or 1 occurred
## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
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## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
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## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
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## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
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## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
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## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
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## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
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## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
## x glm.fit: fitted probabilities numerically 0 or 1 occurred
## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
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## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
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## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
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## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
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## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
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## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
## x glm.fit: fitted probabilities numerically 0 or 1 occurred
## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
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## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
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## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
## x glm.fit: fitted probabilities numerically 0 or 1 occurred
## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
## x glm.fit: fitted probabilities numerically 0 or 1 occurred
## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
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## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
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## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
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## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
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## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
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## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
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## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
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## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
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## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
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## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
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## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
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## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
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## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
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## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
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## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
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## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
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## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
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## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
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## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
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## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
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## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
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## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
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## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
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## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
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## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
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## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
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## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
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## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
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## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
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## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
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## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
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## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
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## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
## x glm.fit: fitted probabilities numerically 0 or 1 occurred
## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
## x glm.fit: fitted probabilities numerically 0 or 1 occurred
## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
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## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
## x glm.fit: fitted probabilities numerically 0 or 1 occurred
## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
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## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
## x glm.fit: fitted probabilities numerically 0 or 1 occurred
## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
## x glm.fit: fitted probabilities numerically 0 or 1 occurred
## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
## x glm.fit: fitted probabilities numerically 0 or 1 occurred
## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
## x glm.fit: fitted probabilities numerically 0 or 1 occurred
## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
## x glm.fit: fitted probabilities numerically 0 or 1 occurred
## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
## x glm.fit: fitted probabilities numerically 0 or 1 occurred
## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
## x glm.fit: fitted probabilities numerically 0 or 1 occurred
## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
## x glm.fit: fitted probabilities numerically 0 or 1 occurred
## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
## x glm.fit: fitted probabilities numerically 0 or 1 occurred
## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
## x glm.fit: fitted probabilities numerically 0 or 1 occurred
## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
## x glm.fit: fitted probabilities numerically 0 or 1 occurred
## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
## x glm.fit: fitted probabilities numerically 0 or 1 occurred
## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
## x glm.fit: fitted probabilities numerically 0 or 1 occurred
## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
## x glm.fit: fitted probabilities numerically 0 or 1 occurred
## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
## x glm.fit: fitted probabilities numerically 0 or 1 occurred
## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: Problem with `mutate()` input `tbl`.
## x glm.fit: fitted probabilities numerically 0 or 1 occurred
## ℹ Input `tbl` is `map2(...)`.## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurredSubsetTrisegmentectomy$PRNCPTX <- as.factor(SubsetTrisegmentectomy$PRNCPTX)
SubsetTrisegmentectomy$BMILabled <- as.factor(SubsetTrisegmentectomy$BMILabled)
SubsetTrisegmentectomy$ASACLAS <- as.factor(SubsetTrisegmentectomy$ASACLAS)
SubsetTrisegmentectomy$STEROID <- as.factor(SubsetTrisegmentectomy$STEROID)
SubsetTrisegmentectomy$DIABETES <- as.factor(SubsetTrisegmentectomy$DIABETES)
SubsetTrisegmentectomy$HYPERMED <- as.factor(SubsetTrisegmentectomy$HYPERMED)
SubsetTrisegmentectomy$ASCITES <- as.factor(SubsetTrisegmentectomy$ASCITES)
SubsetTrisegmentectomy$HXCOPD <- as.factor(SubsetTrisegmentectomy$HXCOPD)
SubsetTrisegmentectomy$DIALYSIS <- as.factor(SubsetTrisegmentectomy$DIALYSIS)
SubsetTrisegmentectomy$HXCHF <- as.factor(SubsetTrisegmentectomy$HXCHF)
SubsetTrisegmentectomy$SMOKE <- as.factor(SubsetTrisegmentectomy$SMOKE)
SubsetTrisegmentectomy$Age <- as.factor(SubsetTrisegmentectomy$Age)
SubsetTrisegmentectomy$PRNCPTX = relevel(SubsetTrisegmentectomy$PRNCPTX, ref="Trisegmentectomy")
SubsetTrisegmentectomy$BMILabled = relevel(SubsetTrisegmentectomy$BMILabled, ref="Normal Weight")
SubsetTrisegmentectomy$ASACLAS = relevel(SubsetTrisegmentectomy$ASACLAS, ref="4-Life Threat")
SubsetTrisegmentectomy$DIABETES = relevel(SubsetTrisegmentectomy$DIABETES, ref="Yes")
SubsetTrisegmentectomy$HYPERMED = relevel(SubsetTrisegmentectomy$HYPERMED, ref="Yes")
SubsetTrisegmentectomy$STEROID = relevel(SubsetTrisegmentectomy$STEROID, ref="Yes")
SubsetTrisegmentectomy$ASCITES = relevel(SubsetTrisegmentectomy$ASCITES, ref="Yes")
SubsetTrisegmentectomy$HXCOPD = relevel(SubsetTrisegmentectomy$HXCOPD, ref="Yes")
SubsetTrisegmentectomy$DIALYSIS = relevel(SubsetTrisegmentectomy$DIALYSIS, ref="Yes")
SubsetTrisegmentectomy$HXCHF = relevel(SubsetTrisegmentectomy$HXCHF, ref="Yes")
SubsetTrisegmentectomy$SMOKE = relevel(SubsetTrisegmentectomy$SMOKE, ref="Yes")
SubsetTrisegmentectomy$Age = relevel(SubsetTrisegmentectomy$Age, ref="85+")####Multivariant analysis serious complications
MVORHep2 <-
glm(TextbookOutcomeHepOR ~ Age + SEX + RACE_NEW + BMILabled + ASACLAS + STEROID + ASCITES + DYSPNEA + SMOKE + DIALYSIS + DIABETES + HYPERMED + HXCOPD + HXCHF,
data = SubsetTrisegmentectomy,
family = binomial("logit"),
na.action =na.omit)###continue on to merge the table sets
MVORTableHep2 <-
tbl_regression(MVORHep2, exponentiate = T,
pvalue_fun = ~style_pvalue(.x, digits = 2),
) %>%
bold_p(t = 0.10) %>%
bold_labels() %>%
italicize_levels() ## Warning: glm.fit: algorithm did not converge## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: algorithm did not converge## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: algorithm did not converge## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: algorithm did not converge## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
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## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurredtbl_merge(
list(UVHep2, MVORTableHep2),
tab_spanner = c("**Univariable**", "**Multivariable**")
) %>%
bold_labels() %>%
italicize_levels() | Characteristic | Univariable | Multivariable | |||||
|---|---|---|---|---|---|---|---|
| N | OR1 | 95% CI1 | p-value | OR1 | 95% CI1 | p-value | |
| Age | 1,172 | ||||||
| 85+ | — | — | |||||
| 0-65 | 632,023 | 0.00, NA | 0.97 | — | — | ||
| 65-74 | 389,617 | 0.00, NA | 0.97 | 0.71 | 0.51, 0.97 | 0.035 | |
| 75-84 | 465,376 | 0.00, NA | 0.97 | 0.87 | 0.52, 1.46 | 0.60 | |
| SEX | 1,172 | ||||||
| male | — | — | — | — | |||
| female | 1.24 | 0.98, 1.56 | 0.074 | 1.26 | 0.95, 1.66 | 0.11 | |
| RACE_NEW | 888 | ||||||
| Black | — | — | — | — | |||
| Other | 1.04 | 0.54, 2.01 | 0.90 | 1.17 | 0.71, 2.30 | 0.66 | |
| White | 1.20 | 0.76, 1.92 | 0.44 | 1.16 | 0.72, 1.89 | 0.53 | |
| BMILabled | 1,165 | ||||||
| Normal Weight | — | — | — | — | |||
| Morbidly Obese | 0.75 | 0.36, 1.51 | 0.44 | 0.62 | 0.23, 1.53 | 0.32 | |
| Obese | 1.15 | 0.84, 1.57 | 0.37 | 1.27 | 0.87, 1.86 | 0.21 | |
| Overweight | 1.30 | 0.98, 1.72 | 0.066 | 1.42 | 1.02, 1.99 | 0.037 | |
| Underweight | 0.73 | 0.31, 1.61 | 0.46 | 0.66 | 0.26, 1.54 | 0.35 | |
| ASACLAS | 1,169 | ||||||
| 4-Life Threat | — | — | — | — | |||
| 1-No Disturb | 2.26 | 0.51, 10.0 | 0.27 | 0.88 | 0.18, 2.69 | 0.87 | |
| 2-Mild Disturb | 1.95 | 1.24, 3.11 | 0.005 | 0.90 | 0.46, 1.78 | 0.77 | |
| 3-Severe Disturb | 1.51 | 0.99, 2.32 | 0.058 | 0.76 | 0.41, 1.44 | 0.40 | |
| STEROID | 1,172 | ||||||
| Yes | — | — | — | — | |||
| No | 0.68 | 0.33, 1.41 | 0.30 | 0.82 | 0.35, 1.94 | 0.65 | |
| ASCITES | 1,172 | ||||||
| Yes | — | — | — | — | |||
| No | 1.14 | 0.28, 5.60 | 0.85 | 0.54 | 0.07, 3.33 | 0.50 | |
| DYSPNEA | 1,172 | ||||||
| AT REST | — | — | — | — | |||
| MODERATE EXERTION | 406,557 | 0.00, NA | 0.97 | 1,257,424 | 0.00, NA | 0.98 | |
| No | 546,429 | 0.00, NA | 0.97 | 1,614,950 | 0.00, NA | 0.98 | |
| SMOKE | 1,172 | ||||||
| Yes | — | — | — | — | |||
| No | 0.90 | 0.67, 1.22 | 0.50 | 0.86 | 0.59, 1.25 | 0.42 | |
| DIALYSIS | 1,172 | ||||||
| Yes | — | — | — | — | |||
| No | 0.00 | 0.97 | 0.00 | 0.99 | |||
| DIABETES | 1,172 | ||||||
| Yes | — | — | — | — | |||
| NO | 1.43 | 1.03, 1.99 | 0.036 | 1.30 | 0.86, 1.99 | 0.21 | |
| HYPERMED | 1,172 | ||||||
| Yes | — | — | — | — | |||
| No | 1.14 | 0.90, 1.45 | 0.26 | 1.03 | 0.76, 1.40 | 0.84 | |
| HXCOPD | 1,172 | ||||||
| Yes | — | — | — | — | |||
| No | 1.61 | 0.85, 3.23 | 0.16 | 1.08 | 0.50, 2.44 | 0.84 | |
| HXCHF | 1,172 | ||||||
| Yes | — | — | — | — | |||
| No | 536,356 | 0.00, NA | 0.97 | 1,231,713 | 0.00, NA | 0.98 | |
|
1
OR = Odds Ratio, CI = Confidence Interval
|
|||||||
###Lobectomy
SubsetLobectomy<-
subset(HepTextbookOutcomesSubset, PRNCPTX == "Lobectomy")
UVHep3 <- SubsetLobectomy %>%
select(Age, SEX, RACE_NEW, BMILabled, ASACLAS, STEROID, ASCITES, DYSPNEA, SMOKE, DIALYSIS, DIABETES, HYPERMED, HXCOPD, HXCHF, TextbookOutcomeHepOR) %>%
tbl_uvregression(
method = glm,
y = TextbookOutcomeHepOR,
method.args = list(family = binomial),
exponentiate = TRUE,
pvalue_fun = ~style_pvalue(.x, digits = 2)
) %>%
bold_p(t = 0.10) %>%
bold_labels() %>%
italicize_levels()SubsetLobectomy$PRNCPTX <- as.factor(SubsetLobectomy$PRNCPTX)
SubsetLobectomy$BMILabled <- as.factor(SubsetLobectomy$BMILabled)
SubsetLobectomy$ASACLAS <- as.factor(SubsetLobectomy$ASACLAS)
SubsetLobectomy$STEROID <- as.factor(SubsetLobectomy$STEROID)
SubsetLobectomy$DIABETES <- as.factor(SubsetLobectomy$DIABETES)
SubsetLobectomy$HYPERMED <- as.factor(SubsetLobectomy$HYPERMED)
SubsetLobectomy$ASCITES <- as.factor(SubsetLobectomy$ASCITES)
SubsetLobectomy$HXCOPD <- as.factor(SubsetLobectomy$HXCOPD)
SubsetLobectomy$DIALYSIS <- as.factor(SubsetLobectomy$DIALYSIS)
SubsetLobectomy$HXCHF <- as.factor(SubsetLobectomy$HXCHF)
SubsetLobectomy$SMOKE <- as.factor(SubsetLobectomy$SMOKE)
SubsetLobectomy$Age <- as.factor(SubsetLobectomy$Age)
SubsetLobectomy$PRNCPTX = relevel(SubsetLobectomy$PRNCPTX, ref="Trisegmentectomy")
SubsetLobectomy$BMILabled = relevel(SubsetLobectomy$BMILabled, ref="Normal Weight")
SubsetLobectomy$ASACLAS = relevel(SubsetLobectomy$ASACLAS, ref="4-Life Threat")
SubsetLobectomy$DIABETES = relevel(SubsetLobectomy$DIABETES, ref="Yes")
SubsetLobectomy$HYPERMED = relevel(SubsetLobectomy$HYPERMED, ref="Yes")
SubsetLobectomy$STEROID = relevel(SubsetLobectomy$STEROID, ref="Yes")
SubsetLobectomy$ASCITES = relevel(SubsetLobectomy$ASCITES, ref="Yes")
SubsetLobectomy$HXCOPD = relevel(SubsetLobectomy$HXCOPD, ref="Yes")
SubsetLobectomy$DIALYSIS = relevel(SubsetLobectomy$DIALYSIS, ref="Yes")
SubsetLobectomy$HXCHF = relevel(SubsetLobectomy$HXCHF, ref="Yes")
SubsetLobectomy$SMOKE = relevel(SubsetLobectomy$SMOKE, ref="Yes")
SubsetLobectomy$Age = relevel(SubsetLobectomy$Age, ref="85+")####Multivariant analysis serious complications
MVORHep3 <-
glm(TextbookOutcomeHepOR ~ Age + SEX + RACE_NEW + BMILabled + ASACLAS + STEROID + ASCITES + DYSPNEA + SMOKE + DIALYSIS + DIABETES + HYPERMED + HXCOPD + HXCHF,
data = SubsetLobectomy,
family = binomial("logit"),
na.action =na.omit)###continue on to merge the table sets
MVORTableHep3 <-
tbl_regression(MVORHep3, exponentiate = T,
pvalue_fun = ~style_pvalue(.x, digits = 2),
) %>%
bold_p(t = 0.10) %>%
bold_labels() %>%
italicize_levels() tbl_merge(
list(UVHep3, MVORTableHep3),
tab_spanner = c("**Univariable**", "**Multivariable**")
) %>%
bold_labels() %>%
italicize_levels() | Characteristic | Univariable | Multivariable | |||||
|---|---|---|---|---|---|---|---|
| N | OR1 | 95% CI1 | p-value | OR1 | 95% CI1 | p-value | |
| Age | 3,626 | ||||||
| 85+ | — | — | — | — | |||
| 0-65 | 1.69 | 0.78, 3.88 | 0.19 | 1.23 | 0.49, 3.25 | 0.66 | |
| 65-74 | 1.21 | 0.55, 2.78 | 0.65 | 0.99 | 0.39, 2.60 | 0.98 | |
| 75-84 | 0.85 | 0.38, 2.00 | 0.71 | 0.76 | 0.30, 2.05 | 0.58 | |
| SEX | 3,626 | ||||||
| male | — | — | — | — | |||
| female | 1.32 | 1.16, 1.50 | <0.001 | 1.24 | 1.05, 1.45 | 0.009 | |
| RACE_NEW | 2,738 | ||||||
| Black | — | — | — | — | |||
| Other | 0.83 | 0.59, 1.16 | 0.28 | 0.81 | 0.57, 1.14 | 0.23 | |
| White | 1.12 | 0.88, 1.42 | 0.37 | 1.05 | 0.82, 1.36 | 0.69 | |
| BMILabled | 3,602 | ||||||
| Normal Weight | — | — | — | — | |||
| Morbidly Obese | 0.96 | 0.68, 1.36 | 0.83 | 1.01 | 0.68, 1.51 | 0.95 | |
| Obese | 1.03 | 0.87, 1.22 | 0.73 | 1.11 | 0.90, 1.36 | 0.34 | |
| Overweight | 1.19 | 1.01, 1.40 | 0.035 | 1.19 | 0.98, 1.45 | 0.079 | |
| Underweight | 0.74 | 0.44, 1.23 | 0.25 | 0.66 | 0.36, 1.19 | 0.18 | |
| ASACLAS | 3,613 | ||||||
| 4-Life Threat | — | — | — | — | |||
| 1-No Disturb | 3.57 | 2.18, 5.89 | <0.001 | 2.92 | 0.76, 14.2 | 0.14 | |
| 2-Mild Disturb | 2.66 | 2.01, 3.55 | <0.001 | 1.76 | 1.20, 2.62 | 0.004 | |
| 3-Severe Disturb | 2.07 | 1.59, 2.70 | <0.001 | 1.58 | 1.10, 2.29 | 0.014 | |
| STEROID | 3,626 | ||||||
| Yes | — | — | — | — | |||
| No | 1.46 | 0.97, 2.23 | 0.072 | 1.37 | 0.86, 2.22 | 0.18 | |
| ASCITES | 3,626 | ||||||
| Yes | — | — | — | — | |||
| No | 3.59 | 1.14, 15.8 | 0.048 | 2.49 | 0.74, 11.3 | 0.17 | |
| DYSPNEA | 3,626 | ||||||
| AT REST | — | — | — | — | |||
| MODERATE EXERTION | 3.63 | 0.60, 69.2 | 0.24 | 4.00 | 0.63, 78.1 | 0.21 | |
| No | 5.50 | 0.94, 104 | 0.11 | 4.76 | 0.76, 91.9 | 0.16 | |
| SMOKE | 3,626 | ||||||
| Yes | — | — | — | — | |||
| No | 1.17 | 0.98, 1.39 | 0.085 | 1.20 | 0.97, 1.48 | 0.10 | |
| DIALYSIS | 3,626 | ||||||
| Yes | — | — | — | — | |||
| No | 8.07 | 1.52, 149 | 0.048 | 4.37 | 0.72, 83.7 | 0.18 | |
| DIABETES | 3,626 | ||||||
| Yes | — | — | — | — | |||
| NO | 1.61 | 1.36, 1.92 | <0.001 | 1.30 | 1.05, 1.62 | 0.015 | |
| HYPERMED | 3,626 | ||||||
| Yes | — | — | — | — | |||
| No | 1.40 | 1.23, 1.60 | <0.001 | 1.12 | 0.94, 1.33 | 0.19 | |
| HXCOPD | 3,626 | ||||||
| Yes | — | — | — | — | |||
| No | 1.94 | 1.33, 2.89 | <0.001 | 1.22 | 0.78, 1.91 | 0.39 | |
| HXCHF | 3,626 | ||||||
| Yes | — | — | — | — | |||
| No | 4.94 | 1.32, 31.9 | 0.038 | 5.37 | 0.94, 101 | 0.12 | |
|
1
OR = Odds Ratio, CI = Confidence Interval
|
|||||||
###changing the referent level
TextbookOutcomeOR$PatientGroup <- as.factor(TextbookOutcomeOR$PatientGroup)
TextbookOutcomeOR$BMILabled <- as.factor(TextbookOutcomeOR$BMILabled)
TextbookOutcomeOR$ASACLAS <- as.factor(TextbookOutcomeOR$ASACLAS)
TextbookOutcomeOR$STEROID <- as.factor(TextbookOutcomeOR$STEROID)
TextbookOutcomeOR$DIABETES <- as.factor(TextbookOutcomeOR$DIABETES)
TextbookOutcomeOR$HYPERMED <- as.factor(TextbookOutcomeOR$HYPERMED)
TextbookOutcomeOR$ASCITES <- as.factor(TextbookOutcomeOR$ASCITES)
TextbookOutcomeOR$HXCOPD <- as.factor(TextbookOutcomeOR$HXCOPD)
TextbookOutcomeOR$DIALYSIS <- as.factor(TextbookOutcomeOR$DIALYSIS)
TextbookOutcomeOR$HXCHF <- as.factor(TextbookOutcomeOR$HXCHF)
TextbookOutcomeOR$SMOKE <- as.factor(TextbookOutcomeOR$SMOKE)
TextbookOutcomeOR$Age <- as.factor(TextbookOutcomeOR$Age)
TextbookOutcomeOR$SEX <- as.factor(TextbookOutcomeOR$SEX)
TextbookOutcomeOR$BMILabled = relevel(TextbookOutcomeOR$BMILabled, ref="Normal Weight")
TextbookOutcomeOR$ASACLAS = relevel(TextbookOutcomeOR$ASACLAS, ref="4-Life Threat")
TextbookOutcomeOR$DIABETES = relevel(TextbookOutcomeOR$DIABETES, ref="Yes")
TextbookOutcomeOR$SEX = relevel(TextbookOutcomeOR$SEX, ref="male")
TextbookOutcomeOR$SMOKE = relevel(TextbookOutcomeOR$SMOKE, ref="Yes")
TextbookOutcomeOR$STEROID = relevel(TextbookOutcomeOR$STEROID, ref="Yes")
TextbookOutcomeOR$ASCITES = relevel(TextbookOutcomeOR$ASCITES, ref="Yes")
TextbookOutcomeOR$HXCOPD = relevel(TextbookOutcomeOR$HXCOPD, ref="Yes")
TextbookOutcomeOR$DIALYSIS = relevel(TextbookOutcomeOR$DIALYSIS, ref="Yes")
TextbookOutcomeOR$HXCHF = relevel(TextbookOutcomeOR$HXCHF, ref="Yes")
TextbookOutcomeOR$Age = relevel(TextbookOutcomeOR$Age, ref="85+")###Univariant, multivariant for everyone
UVAll <- TextbookOutcomeOR %>%
select(PatientGroup, Age, SEX, RACE_NEW, BMILabled, ASACLAS, STEROID, ASCITES, DYSPNEA, SMOKE, DIALYSIS, DIABETES, HYPERMED, HXCOPD, HXCHF, TextbookOutcomeAllOR) %>%
tbl_uvregression(
method = glm,
y = TextbookOutcomeAllOR,
method.args = list(family = binomial),
exponentiate = TRUE,
pvalue_fun = ~style_pvalue(.x, digits = 2)
) %>%
bold_p(t = 0.10) %>%
bold_labels() %>%
italicize_levels()###changling the referent level
TextbookOutcomeOR$PatientGroup <- as.factor(TextbookOutcomeOR$PatientGroup)
TextbookOutcomeOR$SEX <- as.factor(TextbookOutcomeOR$SEX)
TextbookOutcomeOR$ASACLAS <- as.factor(TextbookOutcomeOR$ASACLAS)
TextbookOutcomeOR$STEROID <- as.factor(TextbookOutcomeOR$STEROID)
TextbookOutcomeOR$DIABETES <- as.factor(TextbookOutcomeOR$DIABETES)
TextbookOutcomeOR$SMOKE <- as.factor(TextbookOutcomeOR$SMOKE)
TextbookOutcomeOR$ASCITES <- as.factor(TextbookOutcomeOR$ASCITES)
TextbookOutcomeOR$HXCOPD <- as.factor(TextbookOutcomeOR$HXCOPD)
TextbookOutcomeOR$DIALYSIS <- as.factor(TextbookOutcomeOR$DIALYSIS)
TextbookOutcomeOR$HXCHF <- as.factor(TextbookOutcomeOR$HXCHF)
TextbookOutcomeOR$Age <- as.factor(TextbookOutcomeOR$Age)
TextbookOutcomeOR$SEX = relevel(TextbookOutcomeOR$SEX, ref="male")
TextbookOutcomeOR$BMILabled = relevel(TextbookOutcomeOR$BMILabled, ref="Normal Weight")
TextbookOutcomeOR$ASACLAS = relevel(TextbookOutcomeOR$ASACLAS, ref="4-Life Threat")
TextbookOutcomeOR$DIABETES = relevel(TextbookOutcomeOR$DIABETES, ref="Yes")
TextbookOutcomeOR$SMOKE = relevel(TextbookOutcomeOR$SMOKE, ref="Yes")
TextbookOutcomeOR$STEROID = relevel(TextbookOutcomeOR$STEROID, ref="Yes")
TextbookOutcomeOR$ASCITES = relevel(TextbookOutcomeOR$ASCITES, ref="Yes")
TextbookOutcomeOR$HXCOPD = relevel(TextbookOutcomeOR$HXCOPD, ref="Yes")
TextbookOutcomeOR$DIALYSIS = relevel(TextbookOutcomeOR$DIALYSIS, ref="Yes")
TextbookOutcomeOR$HXCHF = relevel(TextbookOutcomeOR$HXCHF, ref="Yes")
TextbookOutcomeOR$Age = relevel(TextbookOutcomeOR$Age, ref="85+")####Multivariant analysis serious complications
MVORAll <-
glm(TextbookOutcomeAllOR ~ PatientGroup + Age + SEX + RACE_NEW + BMILabled + ASACLAS + STEROID + ASCITES + DYSPNEA + SMOKE + DIALYSIS + DIABETES + HYPERMED + HXCOPD + HXCHF,
data = TextbookOutcomeOR,
family = binomial("logit"),
na.action =na.omit)###continue on to merge the table sets
MVORTableAll <-
tbl_regression(MVORAll, exponentiate = T,
pvalue_fun = ~style_pvalue(.x, digits = 2),
) %>%
bold_p(t = 0.10) %>%
bold_labels() %>%
italicize_levels() tbl_merge(
list(UVAll, MVORTableAll),
tab_spanner = c("**Univariable**", "**Multivariable**")
) %>%
bold_labels() %>%
italicize_levels() | Characteristic | Univariable | Multivariable | |||||
|---|---|---|---|---|---|---|---|
| N | OR1 | 95% CI1 | p-value | OR1 | 95% CI1 | p-value | |
| PatientGroup | 35,154 | ||||||
| Liver | — | — | — | — | |||
| Pancreas | 0.77 | 0.74, 0.81 | <0.001 | 0.78 | 0.74, 0.81 | <0.001 | |
| Age | 35,905 | ||||||
| 85+ | — | — | — | — | |||
| 0-65 | 1.76 | 1.47, 2.12 | <0.001 | 1.60 | 1.30, 1.96 | <0.001 | |
| 65-74 | 1.42 | 1.18, 1.72 | <0.001 | 1.42 | 1.16, 1.74 | <0.001 | |
| 75-84 | 1.19 | 0.98, 1.44 | 0.075 | 1.22 | 0.99, 1.51 | 0.059 | |
| SEX | 35,905 | ||||||
| male | — | — | — | — | |||
| female | 1.28 | 1.23, 1.33 | <0.001 | 1.23 | 1.17, 1.29 | <0.001 | |
| RACE_NEW | 30,147 | ||||||
| Black | — | — | — | — | |||
| Other | 0.93 | 0.83, 1.04 | 0.20 | 0.81 | 0.72, 0.92 | <0.001 | |
| White | 1.11 | 1.03, 1.19 | 0.009 | 1.10 | 1.01, 1.19 | 0.022 | |
| BMILabled | 35,723 | ||||||
| Normal Weight | — | — | — | — | |||
| Morbidly Obese | 0.79 | 0.71, 0.87 | <0.001 | 0.75 | 0.67, 0.84 | <0.001 | |
| Obese | 0.87 | 0.82, 0.92 | <0.001 | 0.86 | 0.81, 0.92 | <0.001 | |
| Overweight | 0.95 | 0.90, 1.00 | 0.056 | 0.97 | 0.91, 1.02 | 0.26 | |
| Underweight | 0.96 | 0.83, 1.12 | 0.61 | 0.98 | 0.83, 1.15 | 0.80 | |
| ASACLAS | 35,830 | ||||||
| 4-Life Threat | — | — | — | — | |||
| 1-No Disturb | 2.97 | 2.37, 3.73 | <0.001 | 2.11 | 1.54, 2.90 | <0.001 | |
| 2-Mild Disturb | 2.33 | 2.13, 2.56 | <0.001 | 1.86 | 1.65, 2.10 | <0.001 | |
| 3-Severe Disturb | 1.65 | 1.52, 1.81 | <0.001 | 1.45 | 1.30, 1.63 | <0.001 | |
| STEROID | 35,905 | ||||||
| Yes | — | — | — | — | |||
| No | 1.31 | 1.16, 1.49 | <0.001 | 1.22 | 1.06, 1.40 | 0.004 | |
| ASCITES | 35,905 | ||||||
| Yes | — | — | — | — | |||
| No | 2.45 | 1.73, 3.55 | <0.001 | 2.36 | 1.62, 3.54 | <0.001 | |
| DYSPNEA | 35,905 | ||||||
| AT REST | — | — | — | — | |||
| MODERATE EXERTION | 1.20 | 0.71, 2.09 | 0.52 | 1.13 | 0.64, 2.08 | 0.68 | |
| No | 1.79 | 1.07, 3.11 | 0.032 | 1.40 | 0.80, 2.55 | 0.25 | |
| SMOKE | 35,905 | ||||||
| Yes | — | — | — | — | |||
| No | 1.03 | 0.97, 1.09 | 0.36 | 1.00 | 0.94, 1.07 | 0.91 | |
| DIALYSIS | 35,905 | ||||||
| Yes | — | — | — | — | |||
| No | 2.71 | 1.84, 4.12 | <0.001 | 2.06 | 1.33, 3.31 | 0.002 | |
| DIABETES | 35,905 | ||||||
| Yes | — | — | — | — | |||
| NO | 1.24 | 1.18, 1.30 | <0.001 | 1.02 | 0.96, 1.08 | 0.57 | |
| HYPERMED | 35,905 | ||||||
| No | — | — | — | — | |||
| Yes | 0.78 | 0.75, 0.81 | <0.001 | 0.93 | 0.88, 0.98 | 0.007 | |
| HXCOPD | 35,905 | ||||||
| Yes | — | — | — | — | |||
| No | 1.71 | 1.53, 1.91 | <0.001 | 1.40 | 1.24, 1.59 | <0.001 | |
| HXCHF | 35,905 | ||||||
| Yes | — | — | — | — | |||
| No | 2.73 | 1.89, 4.04 | <0.001 | 1.76 | 1.17, 2.73 | 0.008 | |
|
1
OR = Odds Ratio, CI = Confidence Interval
|
|||||||
PanOddsPlotty <-
PanTextbookOutcomesSubset %>%
select(PRNCPTX, SEX, RACE_NEW, BMILabled, STEROID, DIALYSIS, HXCOPD, HXCHF, TextbookOutcomePanOR)PanOddsPlotty2 <- PanOddsPlotty[complete.cases(PanOddsPlotty), ] #Create a copyhead(PanOddsPlotty2, 10)## PRNCPTX SEX RACE_NEW BMILabled STEROID DIALYSIS
## 17458 Whipple male White Obese Yes No
## 17459 Whipple male White Normal Weight No No
## 17460 Whipple female White Obese No No
## 17461 Whipple female White Obese No No
## 17462 Whipple male White Overweight No No
## 17463 Whipple female Black Obese No No
## 17464 Distal Pancreatectomy female White Normal Weight Yes No
## 17465 Distal Pancreatectomy male White Obese Yes No
## 17466 Distal Pancreatectomy male White Obese No No
## 17467 Distal Pancreatectomy female White Obese No No
## HXCOPD HXCHF TextbookOutcomePanOR
## 17458 No No 0
## 17459 No No 1
## 17460 No No 0
## 17461 No No 0
## 17462 No No 0
## 17463 No No 0
## 17464 No No 0
## 17465 No No 1
## 17466 No No 1
## 17467 No No 0PanOddsPlotty2$TextbookOutcomePanOR <- factor(PanOddsPlotty2$TextbookOutcomePanOR)
PanOddsPlotty2$RACE_NEW <- factor(PanOddsPlotty2$RACE_NEW)
str(PanOddsPlotty2)## 'data.frame': 19072 obs. of 9 variables:
## $ PRNCPTX : Factor w/ 3 levels "Distal Pancreatectomy",..: 3 3 3 3 3 3 1 1 1 1 ...
## $ SEX : Factor w/ 2 levels "male","female": 1 1 2 2 1 2 2 1 1 2 ...
## $ RACE_NEW : Factor w/ 3 levels "Black","Other",..: 3 3 3 3 3 1 3 3 3 3 ...
## $ BMILabled : Factor w/ 5 levels "Normal Weight",..: 4 1 4 4 2 4 1 4 4 4 ...
## $ STEROID : Factor w/ 2 levels "Yes","No": 1 2 2 2 2 2 1 1 2 2 ...
## $ DIALYSIS : Factor w/ 2 levels "Yes","No": 2 2 2 2 2 2 2 2 2 2 ...
## $ HXCOPD : Factor w/ 2 levels "Yes","No": 2 2 2 2 2 2 2 2 2 2 ...
## $ HXCHF : Factor w/ 2 levels "Yes","No": 2 2 2 2 2 2 2 2 2 2 ...
## $ TextbookOutcomePanOR: Factor w/ 2 levels "0","1": 1 2 1 1 1 1 1 2 2 1 ...for(i in 1:8) {
PanOddsPlotty2[, i] <- as.numeric(as.factor(PanOddsPlotty2[, i]))
}###renaming variables
PanOddsPlotty2 <- rename(PanOddsPlotty2, 'No Dialysis' = DIALYSIS)
PanOddsPlotty2 <- rename(PanOddsPlotty2, 'Female' = SEX)
PanOddsPlotty2 <- rename(PanOddsPlotty2, 'White' = RACE_NEW)
PanOddsPlotty2 <- rename(PanOddsPlotty2, 'No Steroid' = STEROID)
PanOddsPlotty2 <- rename(PanOddsPlotty2, 'No COPD' = HXCOPD)
PanOddsPlotty2 <- rename(PanOddsPlotty2, 'No CHF' = HXCHF)
PanOddsPlotty2 <- rename(PanOddsPlotty2, 'Total Pancreatectomy' = PRNCPTX)
PanOddsPlotty2 <- rename(PanOddsPlotty2, 'Underweight' = BMILabled)glm_model <- train(TextbookOutcomePanOR ~ .,
data = PanOddsPlotty2,
method = "glm",
family = "binomial")
summary(glm_model)##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.1376 -1.0364 -0.9599 1.3069 2.0774
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -5.40283 0.81631 -6.619 3.63e-11 ***
## `\\`Total Pancreatectomy\\`` 0.02087 0.01599 1.305 0.19189
## Female 0.20488 0.02975 6.887 5.71e-12 ***
## White 0.07510 0.02421 3.102 0.00192 **
## Underweight -0.07883 0.01199 -6.572 4.96e-11 ***
## `\\`No Steroid\\`` 0.24694 0.08968 2.753 0.00590 **
## `\\`No Dialysis\\`` 0.88736 0.29118 3.047 0.00231 **
## `\\`No COPD\\`` 0.47938 0.07734 6.198 5.71e-10 ***
## `\\`No CHF\\`` 0.73109 0.26413 2.768 0.00564 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 25810 on 19071 degrees of freedom
## Residual deviance: 25635 on 19063 degrees of freedom
## AIC: 25653
##
## Number of Fisher Scoring iterations: 4## Number of Fisher Scoring iterations: 4
plotty <- OddsPlotty::odds_plot(glm_model$finalModel,
title = "Pancreas Group",
subtitle = "Independent Associations for TO")## Waiting for profiling to be done...## Waiting for profiling to be done...
plot <- plotty$odds_plot
plot <- plot + ggthemes::theme_economist() + theme(legend.position = "NULL")
plot + geom_text(label=round(plotty$odds_plot$data$OR, digits = 2), hjust=0, vjust=1.8)
HepOddsPlotty <-
HepTextbookOutcomesSubset %>%
select(PRNCPTX, SEX, RACE_NEW, BMILabled, STEROID, DIALYSIS, DIABETES, HXCOPD, HXCHF, TextbookOutcomeHepOR)HepOddsPlotty2 <- HepOddsPlotty[complete.cases(HepOddsPlotty), ] #Create a copyhead(HepOddsPlotty2, 10)## PRNCPTX SEX RACE_NEW BMILabled STEROID DIALYSIS DIABETES
## 1 Partial Lobectomy male White Overweight No No Yes
## 2 Partial Lobectomy female White Obese No No NO
## 3 Partial Lobectomy female Black Obese No No NO
## 4 Partial Lobectomy female White Normal Weight No No NO
## 5 Partial Lobectomy male White Overweight No No NO
## 6 Partial Lobectomy male White Overweight No No NO
## 7 Partial Lobectomy female White Obese No No NO
## 8 Partial Lobectomy female White Obese No No NO
## 9 Partial Lobectomy male White Overweight No No Yes
## 10 Partial Lobectomy male White Overweight No No NO
## HXCOPD HXCHF TextbookOutcomeHepOR
## 1 No No 0
## 2 No No 1
## 3 No No 1
## 4 No No 1
## 5 No No 1
## 6 Yes No 0
## 7 No No 1
## 8 No No 1
## 9 No No 0
## 10 No No 1HepOddsPlotty2$TextbookOutcomeHepOR <- factor(HepOddsPlotty2$TextbookOutcomeHepOR)
HepOddsPlotty2$RACE_NEW <- factor(HepOddsPlotty2$RACE_NEW)
str(HepOddsPlotty2)## 'data.frame': 11617 obs. of 10 variables:
## $ PRNCPTX : Factor w/ 3 levels "Trisegmentectomy",..: 3 3 3 3 3 3 3 3 3 3 ...
## $ SEX : Factor w/ 2 levels "male","female": 1 2 2 2 1 1 2 2 1 1 ...
## $ RACE_NEW : Factor w/ 3 levels "Black","Other",..: 3 3 1 3 3 3 3 3 3 3 ...
## $ BMILabled : Factor w/ 5 levels "Normal Weight",..: 4 3 3 1 4 4 3 3 4 4 ...
## $ STEROID : Factor w/ 2 levels "Yes","No": 2 2 2 2 2 2 2 2 2 2 ...
## $ DIALYSIS : Factor w/ 2 levels "Yes","No": 2 2 2 2 2 2 2 2 2 2 ...
## $ DIABETES : Factor w/ 2 levels "Yes","NO": 1 2 2 2 2 2 2 2 1 2 ...
## $ HXCOPD : Factor w/ 2 levels "Yes","No": 2 2 2 2 2 1 2 2 2 2 ...
## $ HXCHF : Factor w/ 2 levels "Yes","No": 2 2 2 2 2 2 2 2 2 2 ...
## $ TextbookOutcomeHepOR: Factor w/ 2 levels "0","1": 1 2 2 2 2 1 2 2 1 2 ...for(i in 1:9) {
HepOddsPlotty2[, i] <- as.numeric(as.factor(HepOddsPlotty2[, i]))
}###renaming variables
HepOddsPlotty2 <- rename(HepOddsPlotty2, 'No Dialysis' = DIALYSIS)
HepOddsPlotty2 <- rename(HepOddsPlotty2, 'Female' = SEX)
HepOddsPlotty2 <- rename(HepOddsPlotty2, 'White' = RACE_NEW)
HepOddsPlotty2 <- rename(HepOddsPlotty2, 'No Steroid' = STEROID)
HepOddsPlotty2 <- rename(HepOddsPlotty2, 'No COPD' = HXCOPD)
HepOddsPlotty2 <- rename(HepOddsPlotty2, 'No CHF' = HXCHF)
HepOddsPlotty2 <- rename(HepOddsPlotty2, 'No Diabetes' = DIABETES)
HepOddsPlotty2 <- rename(HepOddsPlotty2, 'Lobectomy' = PRNCPTX)
HepOddsPlotty2 <- rename(HepOddsPlotty2, 'Obese' = BMILabled)glm_modelhep <- train(TextbookOutcomeHepOR ~ .,
data = HepOddsPlotty2,
method = "glm",
family = "binomial")
summary(glm_modelhep)##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.3185 -1.1638 -0.8909 1.1726 1.9965
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.49386 1.04277 -6.227 4.74e-10 ***
## Lobectomy 0.11848 0.03004 3.944 8.01e-05 ***
## Female 0.28058 0.03775 7.432 1.07e-13 ***
## White 0.04367 0.02887 1.512 0.13043
## Obese 0.03377 0.01508 2.240 0.02512 *
## `\\`No Steroid\\`` 0.19966 0.10526 1.897 0.05786 .
## `\\`No Dialysis\\`` 0.83174 0.37348 2.227 0.02595 *
## `\\`No Diabetes\\`` 0.33922 0.04961 6.838 8.03e-12 ***
## `\\`No COPD\\`` 0.43853 0.09746 4.500 6.81e-06 ***
## `\\`No CHF\\`` 0.99246 0.34857 2.847 0.00441 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 16100 on 11616 degrees of freedom
## Residual deviance: 15918 on 11607 degrees of freedom
## AIC: 15938
##
## Number of Fisher Scoring iterations: 4plottyhep <- OddsPlotty::odds_plot(glm_modelhep$finalModel,
title = "Liver Group",
subtitle = "Independent Associations for TO")## Waiting for profiling to be done...## Waiting for profiling to be done...
plothep <- plottyhep$odds_plot
plothep <- plothep + ggthemes::theme_economist() + theme(legend.position = "NULL")
plothep + geom_text(label=round(plottyhep$odds_plot$data$OR, digits = 2), hjust=0, vjust=1.8)
BothOddsPlotty <-
TextbookOutcomeOR %>%
select(PatientGroup, SEX, STEROID, DIALYSIS, DIABETES, HXCOPD, HXCHF, TextbookOutcomeAllOR)BothOddsPlotty2 <- BothOddsPlotty[complete.cases(BothOddsPlotty), ] #Create a copyhead(BothOddsPlotty2, 10)## PatientGroup SEX STEROID DIALYSIS DIABETES HXCOPD HXCHF
## 1 Liver male No No Yes No No
## 2 Liver female No No NO No No
## 3 Liver female No No NO No No
## 4 Liver female No No NO No No
## 5 Liver male No No NO No No
## 6 Liver male No No NO Yes No
## 7 Liver female No No NO No No
## 8 Liver female No No NO No No
## 9 Liver male No No Yes No No
## 10 Liver male No No NO No No
## TextbookOutcomeAllOR
## 1 0
## 2 1
## 3 1
## 4 1
## 5 1
## 6 0
## 7 1
## 8 1
## 9 0
## 10 1BothOddsPlotty2$TextbookOutcomeAllOR <- factor(BothOddsPlotty2$TextbookOutcomeAllOR)
str(BothOddsPlotty2)## 'data.frame': 35154 obs. of 8 variables:
## $ PatientGroup : Factor w/ 2 levels "Liver","Pancreas": 1 1 1 1 1 1 1 1 1 1 ...
## $ SEX : Factor w/ 2 levels "male","female": 1 2 2 2 1 1 2 2 1 1 ...
## $ STEROID : Factor w/ 2 levels "Yes","No": 2 2 2 2 2 2 2 2 2 2 ...
## $ DIALYSIS : Factor w/ 2 levels "Yes","No": 2 2 2 2 2 2 2 2 2 2 ...
## $ DIABETES : Factor w/ 2 levels "Yes","NO": 1 2 2 2 2 2 2 2 1 2 ...
## $ HXCOPD : Factor w/ 2 levels "Yes","No": 2 2 2 2 2 1 2 2 2 2 ...
## $ HXCHF : Factor w/ 2 levels "Yes","No": 2 2 2 2 2 2 2 2 2 2 ...
## $ TextbookOutcomeAllOR: Factor w/ 2 levels "0","1": 1 2 2 2 2 1 2 2 1 2 ...for(i in 1:7) {
BothOddsPlotty2[, i] <- as.numeric(as.factor(BothOddsPlotty2[, i]))
}###renaming variables
BothOddsPlotty2 <- rename(BothOddsPlotty2, 'No Dialysis' = DIALYSIS)
BothOddsPlotty2 <- rename(BothOddsPlotty2, 'Female' = SEX)
BothOddsPlotty2 <- rename(BothOddsPlotty2, 'No Steroid' = STEROID)
BothOddsPlotty2 <- rename(BothOddsPlotty2, 'No COPD' = HXCOPD)
BothOddsPlotty2 <- rename(BothOddsPlotty2, 'No CHF' = HXCHF)
BothOddsPlotty2 <- rename(BothOddsPlotty2, 'No Diabetes' = DIABETES)
BothOddsPlotty2 <- rename(BothOddsPlotty2, 'Pancreatic' = PatientGroup)glm_modelboth <- train(TextbookOutcomeAllOR ~ .,
data = BothOddsPlotty2,
method = "glm",
family = "binomial")
summary(glm_modelboth)##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.2248 -1.1220 -0.9696 1.2338 2.0334
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -5.36928 0.59904 -8.963 < 2e-16 ***
## Pancreatic -0.24256 0.02201 -11.022 < 2e-16 ***
## Female 0.22728 0.02167 10.488 < 2e-16 ***
## `\\`No Steroid\\`` 0.24218 0.06422 3.771 0.000162 ***
## `\\`No Dialysis\\`` 0.90930 0.21287 4.272 1.94e-05 ***
## `\\`No Diabetes\\`` 0.15174 0.02612 5.809 6.27e-09 ***
## `\\`No COPD\\`` 0.48508 0.05738 8.453 < 2e-16 ***
## `\\`No CHF\\`` 0.84574 0.19760 4.280 1.87e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 48336 on 35153 degrees of freedom
## Residual deviance: 47891 on 35146 degrees of freedom
## AIC: 47907
##
## Number of Fisher Scoring iterations: 4plottyboth <- OddsPlotty::odds_plot(glm_modelboth$finalModel,
title = "Liver & Pancreatic Group",
subtitle = "Independent Associations for TO")## Waiting for profiling to be done...## Waiting for profiling to be done...
plotboth <- plottyboth$odds_plot
plotboth <- plotboth + ggthemes::theme_economist() + theme(legend.position = "NULL")
plotboth + geom_text(label=round(plottyboth$odds_plot$data$OR, digits = 2), hjust=0, vjust=1.8)