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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## 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$TextbookOutcomeAll)
PanTextbookOutcomes1 <- PanTextbookOutcomes %>% mutate(PanTextbookOutcomes, ModifiedFrailtyIndex5 = ifelse(MFI5 == 0, “0”, ifelse(MFI5 == 1, “1”, ifelse(MFI5 > 2, “>2”, NA))))
PantextBookOutcomesDT <-
PanTextbookOutcomes %>%
select(PRNCPTX, PatientGroup, Age, SEX, RACE_NEW, BMILabled, ASACLAS, DOpertoD, OPTIME, ELOS, READMISSION1, STEROID, DIABETES, HYPERMED, ASACLAS, ASCITES, HXCHF, DYSPNEA, SMOKE, HXCOPD, DIALYSIS, OPRENAFL, FNSTATUS2, AnyComplication, TextbookOutcomeHep, TextbookOutcomePan, TextbookOutcomeAll, PanComplications, HepComplications)
table(PantextBookOutcomesDT$PatientGroup)##
## Liver Pancreas
## 17457 25462LOSTable <-
PanTextbookOutcomes %>%
select(PRNCPTX, DOptoDis)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 | |
|
1
Statistics presented: Median (IQR)
2
Statistical tests performed: Kruskal-Wallis test
|
||||||||
PantextBookOutcomesDT %>%
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 'PanComplications', 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 'HepComplications', 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 | |||
| 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%) | |
| 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 | |
| AnyComplication | 10,522 (25%) | 3,269 (19%) | 7,253 (28%) | <0.001 |
| TextbookOutcomeHep | 6,950 (26%) | 6,950 (47%) | 0 (0%) | <0.001 |
| Unknown | 16,209 | 2,801 | 13,408 | |
| TextbookOutcomePan | 8,759 (30%) | 0 (0%) | 8,759 (40%) | <0.001 |
| Unknown | 13,555 | 10,066 | 3,489 | |
| TextbookOutcomeAll | 15,709 (45%) | 6,950 (48%) | 8,759 (42%) | <0.001 |
| Unknown | 7,765 | 3,116 | 4,649 | |
| PanComplications | 6,370 (25%) | 0 (NA%) | 6,370 (25%) | |
| Unknown | 17,737 | 17,457 | 280 | |
| HepComplications | 1,842 (11%) | 1,842 (11%) | 0 (NA%) | |
| Unknown | 25,567 | 105 | 25,462 | |
|
1
Statistics presented: n (%); Median (IQR)
2
Statistical tests performed: chi-square test of independence; Wilcoxon rank-sum test
|
||||
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
|
|||||||
###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
|
|||||||
###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$Age, ref="85+")####Multivariant analysis serious complications
MVORAll <-
glm(TextbookOutcomeAllOR ~ PatientGroup + Age + SEX + RACE_NEW + BMILabled + ASACLAS + STEROID + ASCITES + DYSPNEA + SMOKE + DIALYSIS + DIABETES + HYPERMED + HXCOPD + HXCHF,
data = TextbookOutcomeOR,
family = binomial("logit"),
na.action =na.omit)###continue on to merge the table sets
MVORTableAll <-
tbl_regression(MVORAll, exponentiate = T,
pvalue_fun = ~style_pvalue(.x, digits = 2),
) %>%
bold_p(t = 0.10) %>%
bold_labels() %>%
italicize_levels() tbl_merge(
list(UVAll, MVORTableAll),
tab_spanner = c("**Univariable**", "**Multivariable**")
) %>%
bold_labels() %>%
italicize_levels() PanOddsPlotty <-
PanTextbookOutcomesSubset %>%
select(PRNCPTX, SEX, RACE_NEW, BMILabled, STEROID, DIALYSIS, HXCOPD, HXCHF, TextbookOutcomePanOR)PanOddsPlotty2 <- PanOddsPlotty[complete.cases(PanOddsPlotty), ] #Create a copyhead(PanOddsPlotty2, 10)## PRNCPTX SEX RACE_NEW BMILabled STEROID DIALYSIS
## 17458 Whipple male White Obese Yes No
## 17459 Whipple male White Normal Weight No No
## 17460 Whipple female White Obese No No
## 17461 Whipple female White Obese No No
## 17462 Whipple male White Overweight No No
## 17463 Whipple female Black Obese No No
## 17464 Distal Pancreatectomy female White Normal Weight Yes No
## 17465 Distal Pancreatectomy male White Obese Yes No
## 17466 Distal Pancreatectomy male White Obese No No
## 17467 Distal Pancreatectomy female White Obese No No
## HXCOPD HXCHF TextbookOutcomePanOR
## 17458 No No 0
## 17459 No No 1
## 17460 No No 0
## 17461 No No 0
## 17462 No No 0
## 17463 No No 0
## 17464 No No 0
## 17465 No No 1
## 17466 No No 1
## 17467 No No 0PanOddsPlotty2$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)