Logistic regression models for 30 % eGFRCYS<eGFRCRE

univariate logistic regression for 30 % eGFRCYS<eGFRCRE

global P-value

  master %>%
  select( "year_10", "male","White",       
          "bmi_cat","smoking","htn","cad","dm",              
          "cirrhosis","hiv","Malnutrition","thyroid_dz","ace_arb","ppi","diur","steroids",     
          "alb_cat",          
          "hgb_cat","cre_cys_gfr_calc","eGFRCYS_lt_eGFRCRE"
  ) %>%
  tbl_uvregression(
    method = glm,
    y = eGFRCYS_lt_eGFRCRE,
    show_single_row=c("male","White",        
                      "smoking","htn","cad","dm",              
                      "cirrhosis","hiv","Malnutrition","ace_arb","ppi","diur",      
                      "thyroid_dz", "steroids"),
    method.args = list(family = binomial),
    exponentiate = TRUE,
    label = list(male = "Female"),
    pvalue_fun = ~style_pvalue(.x, digits = 3)
  )  %>%
add_global_p() %>%

  bold_p() %>%        # bold p-values under a given threshold (default 0.05)
  bold_labels() %>% 
  modify_spanning_header(
    c(estimate, ci, p.value) ~  
      "**30 %  eGFRCYS<eGFRCRE Univariate logistic regression  **")
Characteristic N **30 % eGFRCYS<eGFRCRE Univariate logistic regression **
OR1 95% CI1 p-value
year_10 1,674 1.05 0.97, 1.13 0.213
Female 1,674 1.12 0.92, 1.38 0.264
White 1,674 1.13 0.87, 1.47 0.372
bmi_cat 1,674 0.078
    Normal Range — —
    Obse 1.01 0.75, 1.36
    Overweight 1.0 0.77, 1.29
    Underweight 2.13 1.17, 3.88
smoking 1,674 1.29 1.05, 1.58 0.017
htn 1,674 1.80 1.35, 2.44 <0.001
cad 1,674 2.26 1.83, 2.80 <0.001
dm 1,674 2.48 2.01, 3.08 <0.001
cirrhosis 1,674 3.20 2.11, 4.89 <0.001
hiv 1,674 0.88 0.50, 1.49 0.629
Malnutrition 1,674 1.34 1.01, 1.78 0.046
thyroid_dz 1,674 1.47 1.17, 1.84 0.001
ace_arb 1,674 1.06 0.86, 1.31 0.571
ppi 1,674 1.84 1.46, 2.32 <0.001
diur 1,674 2.99 2.34, 3.84 <0.001
steroids 1,674 3.43 2.70, 4.37 <0.001
alb_cat 1,674 <0.001
    >=4 — —
    <3 11.6 8.55, 15.9
    3-3.99 4.45 3.45, 5.75
hgb_cat 1,674 <0.001
    >=12 — —
    <10 7.50 5.75, 9.83
    10-11.99 2.67 2.01, 3.56
cre_cys_gfr_calc 1,674 0.98 0.98, 0.98 <0.001
1 OR = Odds Ratio, CI = Confidence Interval

univariate logistic regression for 30 % eGFRCYS<eGFRCRE

  master %>%
  select( "year_10", "male","White",       
          "bmi_cat","smoking","htn","cad","dm",              
          "cirrhosis","hiv","Malnutrition","thyroid_dz","ace_arb","ppi","diur","steroids",     
          "alb_cat",          
          "hgb_cat","cre_cys_gfr_calc","eGFRCYS_lt_eGFRCRE"
  ) %>%
  tbl_uvregression(
    method = glm,
    y = eGFRCYS_lt_eGFRCRE,
    show_single_row=c("male","White",        
                      "smoking","htn","cad","dm",              
                      "cirrhosis","hiv","Malnutrition","ace_arb","ppi","diur",      
                      "thyroid_dz", "steroids"),
    method.args = list(family = binomial),
    exponentiate = TRUE,
    label = list(male = "Female"),
    pvalue_fun = ~style_pvalue(.x, digits = 3)
  )  %>%
  bold_p() %>%        # bold p-values under a given threshold (default 0.05)
  bold_labels() %>% 
  modify_spanning_header(
    c(estimate, ci, p.value) ~  
      "**30 %  eGFRCYS<eGFRCRE Univariate logistic regression  **")
Characteristic N **30 % eGFRCYS<eGFRCRE Univariate logistic regression **
OR1 95% CI1 p-value
year_10 1,674 1.05 0.97, 1.13 0.215
Female 1,674 1.12 0.92, 1.38 0.264
White 1,674 1.13 0.87, 1.47 0.374
bmi_cat 1,674
    Normal Range — —
    Obse 1.01 0.75, 1.36 0.961
    Overweight 1.0 0.77, 1.29 0.967
    Underweight 2.13 1.17, 3.88 0.013
smoking 1,674 1.29 1.05, 1.58 0.017
htn 1,674 1.80 1.35, 2.44 <0.001
cad 1,674 2.26 1.83, 2.80 <0.001
dm 1,674 2.48 2.01, 3.08 <0.001
cirrhosis 1,674 3.20 2.11, 4.89 <0.001
hiv 1,674 0.88 0.50, 1.49 0.632
Malnutrition 1,674 1.34 1.01, 1.78 0.044
thyroid_dz 1,674 1.47 1.17, 1.84 <0.001
ace_arb 1,674 1.06 0.86, 1.31 0.571
ppi 1,674 1.84 1.46, 2.32 <0.001
diur 1,674 2.99 2.34, 3.84 <0.001
steroids 1,674 3.43 2.70, 4.37 <0.001
alb_cat 1,674
    >=4 — —
    <3 11.6 8.55, 15.9 <0.001
    3-3.99 4.45 3.45, 5.75 <0.001
hgb_cat 1,674
    >=12 — —
    <10 7.50 5.75, 9.83 <0.001
    10-11.99 2.67 2.01, 3.56 <0.001
cre_cys_gfr_calc 1,674 0.98 0.98, 0.98 <0.001
1 OR = Odds Ratio, CI = Confidence Interval

multivariate logistic regression for 30 % eGFRCYS<eGFRCRE

m_eGFRCYS_lt_eGFRCRE <- glm(eGFRCYS_lt_eGFRCRE ~ year_10 + male + White   + bmi_cat + smoking + htn +cad + dm  + cirrhosis+ Malnutrition+thyroid_dz+ ppi+ diur+ steroids + alb_cat+ hgb_cat+cre_cys_gfr_calc, family = binomial(link = 'logit'),data=master) 

m_eGFRCYS_lt_eGFRCRE %>%  tbl_regression(exponentiate = TRUE,
                       show_single_row=c("male","White","smoking","htn","cad","dm","cirrhosis","Malnutrition","ppi","diur","thyroid_dz","steroids"),
                       label = list(male = "Female"),
                       pvalue_fun = ~style_pvalue(.x, digits = 3)) %>% bold_p() %>% # bold p-values under a given threshold (default 0.05)
  bold_labels()%>% 
  modify_spanning_header(
    c(estimate, ci, p.value) ~  
      "**30 %  eGFRCYS<eGFRCRE cre_gfr mutilvariate logistic regression**")
Characteristic 30 % eGFRCYS<eGFRCRE cre_gfr mutilvariate logistic regression
OR1 95% CI1 p-value
year_10 0.94 0.85, 1.04 0.210
Female 1.07 0.83, 1.37 0.612
White 1.43 1.05, 1.95 0.025
bmi_cat
    Normal Range — —
    Obse 1.41 0.98, 2.01 0.063
    Overweight 1.19 0.87, 1.62 0.281
    Underweight 1.74 0.86, 3.54 0.123
smoking 0.95 0.74, 1.22 0.684
htn 0.97 0.65, 1.46 0.886
cad 1.28 0.97, 1.69 0.080
dm 1.27 0.96, 1.66 0.092
cirrhosis 1.68 1.03, 2.77 0.041
Malnutrition 0.99 0.70, 1.40 0.965
thyroid_dz 1.21 0.92, 1.59 0.175
ppi 0.85 0.64, 1.14 0.280
diur 1.60 1.15, 2.24 0.005
steroids 1.65 1.23, 2.21 <0.001
alb_cat
    >=4 — —
    <3 6.09 4.16, 8.98 <0.001
    3-3.99 2.53 1.87, 3.42 <0.001
hgb_cat
    >=12 — —
    <10 1.98 1.38, 2.83 <0.001
    10-11.99 1.64 1.19, 2.26 0.002
cre_cys_gfr_calc 0.99 0.98, 1.0 <0.001
1 OR = Odds Ratio, CI = Confidence Interval

Logistic regression models for 30 % eGFRCYS>eGFRCRE

univariate logistic regression for 30 % eGFRCYS>eGFRCRE

global p value

master %>%
  select( "year_10", "male","White",       
          "bmi_cat","smoking","htn","cad","dm",              
          "cirrhosis","hiv","Malnutrition","thyroid_dz","ace_arb","ppi","diur","steroids",     
          "alb_cat",          
          "hgb_cat","cre_cys_gfr_calc","eGFRCYS_gt_eGFRCRE"
  ) %>%
  tbl_uvregression(
    method = glm,
    y = eGFRCYS_gt_eGFRCRE,
    show_single_row=c("male","White",        
                      "smoking","htn","cad","dm",              
                      "cirrhosis","hiv","Malnutrition","ace_arb","ppi","diur",      
                      "thyroid_dz", "steroids"),
    method.args = list(family = binomial),
    exponentiate = TRUE,
    label = list(male = "Female"),
    pvalue_fun = ~style_pvalue(.x, digits = 3)
  ) %>%
add_global_p() %>%
  bold_p() %>%        # bold p-values under a given threshold (default 0.05)
  bold_labels() %>% 
  modify_spanning_header(
    c(estimate, ci, p.value) ~  
      "**30 % eGFRCYS>eGFRCRE Univariate logistic regression  **")
Characteristic N **30 % eGFRCYS>eGFRCRE Univariate logistic regression **
OR1 95% CI1 p-value
year_10 1,326 0.73 0.65, 0.81 <0.001
Female 1,326 1.14 0.84, 1.54 0.412
White 1,326 0.61 0.43, 0.86 0.005
bmi_cat 1,326 0.247
    Normal Range — —
    Obse 0.66 0.42, 1.04
    Overweight 0.92 0.64, 1.34
    Underweight 0.60 0.14, 1.80
smoking 1,326 0.59 0.42, 0.82 0.001
htn 1,326 0.49 0.35, 0.68 <0.001
cad 1,326 0.64 0.47, 0.88 0.006
dm 1,326 0.48 0.34, 0.67 <0.001
cirrhosis 1,326 0.43 0.10, 1.19 0.111
hiv 1,326 1.87 0.97, 3.38 0.061
Malnutrition 1,326 0.77 0.46, 1.24 0.290
thyroid_dz 1,326 1.12 0.78, 1.58 0.524
ace_arb 1,326 0.55 0.40, 0.74 <0.001
ppi 1,326 0.58 0.43, 0.79 <0.001
diur 1,326 0.53 0.39, 0.71 <0.001
steroids 1,326 0.65 0.38, 1.05 0.079
alb_cat 1,326 0.178
    >=4 — —
    <3 1.10 0.60, 1.88
    3-3.99 0.68 0.43, 1.04
hgb_cat 1,326 0.332
    >=12 — —
    <10 1.14 0.75, 1.70
    10-11.99 1.30 0.92, 1.85
cre_cys_gfr_calc 1,326 1.01 1.00, 1.01 0.009
1 OR = Odds Ratio, CI = Confidence Interval

univariate logistic regression for 30 % eGFRCYS>eGFRCRE

master %>%
  select( "year_10", "male","White",       
          "bmi_cat","smoking","htn","cad","dm",              
          "cirrhosis","hiv","Malnutrition","thyroid_dz","ace_arb","ppi","diur","steroids",     
          "alb_cat",          
          "hgb_cat","cre_cys_gfr_calc","eGFRCYS_gt_eGFRCRE"
  ) %>%
  tbl_uvregression(
    method = glm,
    y = eGFRCYS_gt_eGFRCRE,
    show_single_row=c("male","White",        
                      "smoking","htn","cad","dm",              
                      "cirrhosis","hiv","Malnutrition","ace_arb","ppi","diur",      
                      "thyroid_dz", "steroids"),
    method.args = list(family = binomial),
    exponentiate = TRUE,
    label = list(male = "Female"),
    pvalue_fun = ~style_pvalue(.x, digits = 3)
  ) %>%
  bold_p() %>%        # bold p-values under a given threshold (default 0.05)
  bold_labels() %>% 
  modify_spanning_header(
    c(estimate, ci, p.value) ~  
      "**30 % eGFRCYS>eGFRCRE Univariate logistic regression  **")
Characteristic N **30 % eGFRCYS>eGFRCRE Univariate logistic regression **
OR1 95% CI1 p-value
year_10 1,326 0.73 0.65, 0.81 <0.001
Female 1,326 1.14 0.84, 1.54 0.412
White 1,326 0.61 0.43, 0.86 0.004
bmi_cat 1,326
    Normal Range — —
    Obse 0.66 0.42, 1.04 0.074
    Overweight 0.92 0.64, 1.34 0.656
    Underweight 0.60 0.14, 1.80 0.421
smoking 1,326 0.59 0.42, 0.82 0.002
htn 1,326 0.49 0.35, 0.68 <0.001
cad 1,326 0.64 0.47, 0.88 0.006
dm 1,326 0.48 0.34, 0.67 <0.001
cirrhosis 1,326 0.43 0.10, 1.19 0.158
hiv 1,326 1.87 0.97, 3.38 0.049
Malnutrition 1,326 0.77 0.46, 1.24 0.302
thyroid_dz 1,326 1.12 0.78, 1.58 0.521
ace_arb 1,326 0.55 0.40, 0.74 <0.001
ppi 1,326 0.58 0.43, 0.79 <0.001
diur 1,326 0.53 0.39, 0.71 <0.001
steroids 1,326 0.65 0.38, 1.05 0.093
alb_cat 1,326
    >=4 — —
    <3 1.10 0.60, 1.88 0.747
    3-3.99 0.68 0.43, 1.04 0.086
hgb_cat 1,326
    >=12 — —
    <10 1.14 0.75, 1.70 0.531
    10-11.99 1.30 0.92, 1.85 0.137
cre_cys_gfr_calc 1,326 1.01 1.00, 1.01 0.009
1 OR = Odds Ratio, CI = Confidence Interval

multivariate logistic regression

m_eGFRCYS_gt_eGFRCRE <- glm(eGFRCYS_gt_eGFRCRE ~year_10 + male + White  + bmi_cat +smoking+htn+ cad + dm+hiv+ace_arb+ppi  +  diur+ steroids + cre_cys_gfr_calc, family = binomial(link = 'logit'),data=master) 

m_eGFRCYS_gt_eGFRCRE %>%  tbl_regression(exponentiate = TRUE,
                       show_single_row=c("male","White","smoking","htn","cad","dm","hiv","ace_arb","ppi","diur","steroids"),
                       label = list(male = "Female"),
                       pvalue_fun = ~style_pvalue(.x, digits = 3)) %>% bold_p() %>% # bold p-values under a given threshold (default 0.05)
  bold_labels()%>% 
  modify_spanning_header(
    c(estimate, ci, p.value) ~  
      "**30 % eGFRCYS>eGFRCRE cre_gfr mutilvariate logistic regression**")
Characteristic 30 % eGFRCYS>eGFRCRE cre_gfr mutilvariate logistic regression
OR1 95% CI1 p-value
year_10 0.75 0.66, 0.86 <0.001
Female 1.10 0.79, 1.53 0.574
White 0.62 0.43, 0.89 0.010
bmi_cat
    Normal Range — —
    Obse 0.73 0.45, 1.17 0.193
    Overweight 0.83 0.56, 1.23 0.351
    Underweight 0.73 0.16, 2.30 0.626
smoking 0.78 0.54, 1.12 0.183
htn 0.81 0.51, 1.29 0.382
cad 1.15 0.78, 1.69 0.494
dm 0.60 0.40, 0.89 0.011
hiv 1.65 0.83, 3.12 0.137
ace_arb 0.90 0.59, 1.38 0.620
ppi 0.80 0.56, 1.13 0.201
diur 0.83 0.56, 1.23 0.350
steroids 0.64 0.37, 1.05 0.093
cre_cys_gfr_calc 0.99 0.99, 1.00 0.032
1 OR = Odds Ratio, CI = Confidence Interval

30 days survival analysis

KM

cre_fit1 <- survfit(Surv(length_of_followup,status_30_days==1) ~ new_ref_within_30, data = master)


cre_result <- ggsurvplot(
  cre_fit1,                     # survfit object with calculated statistics.
  data = master,
  legend.title = "Groups", # legend title
  legend.labs = c( "Reference","eGFRCYS<eGFRCRE","eGFRCYS>eGFRCRE"), # legend labels
  pval ="p < 0.001",             # show p-value of log-rank test.
  #pval.coord = c(20, 1),# pvalue  position 
  pval.size = 4, # pvalue size 
  conf.int = T,         # show confidence intervals for 
  xlim = c(0,30),  # present narrower X axis, but not affect survival estimates.
  xlab = "Time(days)", # Xlabel.
  break.time.by = 5,     # break X axis in time intervals by 500.
  #ggtheme = theme_minimal(), # customize plot and risk table with a theme.
  tables.theme = theme_cleantable(),
  risk.table = TRUE,       # show risk table.
  risk.table.y.text.col = T, # colour risk table text annotations.
  risk.table.y.text = T, # show bars instead of names in text annotations
  #risk.table.fontsize= 3,
  # in legend of risk table
)

cre_result

logrank_test <- pairwise_survdiff(Surv(length_of_followup,status_30_days==1) ~ new_ref_within_30, data = master, p.adjust.method = "none")
logrank_test
## 
##  Pairwise comparisons using Log-Rank test 
## 
## data:  master and new_ref_within_30 
## 
##                       eGFRCYS ~ eGFRCRE(within 30) eGFRCYS<eGFRCRE(<-30)
## eGFRCYS<eGFRCRE(<-30) <2e-16                       -                    
## eGFRCYS>eGFRCRE(>30)  0.89                         5e-07                
## 
## P value adjustment method: none

univariate cox regression

global p value

  master %>%
  select( "year_10","male","White","new_ref_within_30",        
          "bmi_cat","smoking","htn","cad","dm",              
          "cirrhosis","hiv","Malnutrition","ace_arb","ppi","diur",      
          "alb_cat","cre", "cre_cys_gfr_calc",      
          "hgb_cat","thyroid_dz", "steroids","length_of_followup","status_30_days"
  ) %>%
  tbl_uvregression(
    method = coxph,
    y = Surv(length_of_followup,status_30_days),
    exponentiate = TRUE,
    label = list(male = "Female"),
    show_single_row=c( "male","White",        
                       "smoking","htn","cad","dm",              
                       "cirrhosis","hiv","Malnutrition","ace_arb","ppi","diur",      
                       "thyroid_dz", "steroids"),
    pvalue_fun = ~style_pvalue(.x, digits = 3)
  ) %>%
add_global_p() %>%
  bold_p() %>%  
  bold_labels() %>% 
  modify_spanning_header(
    c(estimate, ci, p.value) ~  
      "**Univariate cox regression corhort N=1869**")
Characteristic N Univariate cox regression corhort N=1869
HR1 95% CI1 p-value
year_10 1,869 1.01 0.89, 1.15 0.844
Female 1,869 0.72 0.50, 1.02 0.061
White 1,869 1.61 0.98, 2.65 0.048
new_ref_within_30 1,869 <0.001
    eGFRCYS ~ eGFRCRE(within 30) — —
    eGFRCYS<eGFRCRE(<-30) 7.67 5.03, 11.7
    eGFRCYS>eGFRCRE(>30) 1.07 0.41, 2.77
bmi_cat 1,869 0.100
    Normal Range — —
    Obse 0.59 0.34, 1.02
    Overweight 1.03 0.68, 1.56
    Underweight 1.24 0.48, 3.19
smoking 1,869 1.18 0.84, 1.68 0.345
htn 1,869 0.83 0.55, 1.26 0.388
cad 1,869 2.01 1.39, 2.90 <0.001
dm 1,869 2.42 1.65, 3.55 <0.001
cirrhosis 1,869 2.77 1.66, 4.61 <0.001
hiv 1,869 0.92 0.38, 2.26 0.861
Malnutrition 1,869 0.58 0.31, 1.08 0.066
ace_arb 1,869 0.70 0.49, 0.99 0.044
ppi 1,869 2.07 1.34, 3.18 <0.001
diur 1,869 1.91 1.26, 2.91 0.001
alb_cat 1,869 <0.001
    >=4 — —
    <3 94.3 38.4, 232
    3-3.99 15.4 5.93, 39.8
cre 1,869 1.20 1.07, 1.36 0.007
cre_cys_gfr_calc 1,869 0.98 0.97, 0.99 <0.001
hgb_cat 1,869 <0.001
    >=12 — —
    <10 28.7 12.6, 65.3
    10-11.99 5.79 2.32, 14.4
thyroid_dz 1,869 1.23 0.84, 1.79 0.289
steroids 1,869 4.76 3.36, 6.73 <0.001
1 HR = Hazard Ratio, CI = Confidence Interval

univariate cox regression

  master %>%
  select( "year_10","male","White","new_ref_within_30",        
          "bmi_cat","smoking","htn","cad","dm",              
          "cirrhosis","hiv","Malnutrition","ace_arb","ppi","diur",      
          "alb_cat","cre", "cre_cys_gfr_calc",      
          "hgb_cat","thyroid_dz", "steroids","length_of_followup","status_30_days"
  ) %>%
  tbl_uvregression(
    method = coxph,
    y = Surv(length_of_followup,status_30_days),
    exponentiate = TRUE,
    label = list(male = "Female"),
    show_single_row=c( "male","White",        
                       "smoking","htn","cad","dm",              
                       "cirrhosis","hiv","Malnutrition","ace_arb","ppi","diur",      
                       "thyroid_dz", "steroids"),
    pvalue_fun = ~style_pvalue(.x, digits = 3)
  ) %>%
  bold_p() %>%  
  bold_labels() %>% 
  modify_spanning_header(
    c(estimate, ci, p.value) ~  
      "**Univariate cox regression corhort N=1869**")
Characteristic N Univariate cox regression corhort N=1869
HR1 95% CI1 p-value
year_10 1,869 1.01 0.89, 1.15 0.845
Female 1,869 0.72 0.50, 1.02 0.063
White 1,869 1.61 0.98, 2.65 0.062
new_ref_within_30 1,869
    eGFRCYS ~ eGFRCRE(within 30) — —
    eGFRCYS<eGFRCRE(<-30) 7.67 5.03, 11.7 <0.001
    eGFRCYS>eGFRCRE(>30) 1.07 0.41, 2.77 0.892
bmi_cat 1,869
    Normal Range — —
    Obse 0.59 0.34, 1.02 0.061
    Overweight 1.03 0.68, 1.56 0.900
    Underweight 1.24 0.48, 3.19 0.652
smoking 1,869 1.18 0.84, 1.68 0.344
htn 1,869 0.83 0.55, 1.26 0.380
cad 1,869 2.01 1.39, 2.90 <0.001
dm 1,869 2.42 1.65, 3.55 <0.001
cirrhosis 1,869 2.77 1.66, 4.61 <0.001
hiv 1,869 0.92 0.38, 2.26 0.862
Malnutrition 1,869 0.58 0.31, 1.08 0.088
ace_arb 1,869 0.70 0.49, 0.99 0.044
ppi 1,869 2.07 1.34, 3.18 <0.001
diur 1,869 1.91 1.26, 2.91 0.002
alb_cat 1,869
    >=4 — —
    <3 94.3 38.4, 232 <0.001
    3-3.99 15.4 5.93, 39.8 <0.001
cre 1,869 1.20 1.07, 1.36 0.002
cre_cys_gfr_calc 1,869 0.98 0.97, 0.99 <0.001
hgb_cat 1,869
    >=12 — —
    <10 28.7 12.6, 65.3 <0.001
    10-11.99 5.79 2.32, 14.4 <0.001
thyroid_dz 1,869 1.23 0.84, 1.79 0.282
steroids 1,869 4.76 3.36, 6.73 <0.001
1 HR = Hazard Ratio, CI = Confidence Interval

##multivariate cox regression

cox_multivariate <- coxph(Surv(length_of_followup,status_30_days) ~year_10+ male + White +new_ref_within_30 + bmi_cat+cad + dm  + cirrhosis+Malnutrition+ace_arb+ ppi+ diur+ alb_cat+ hgb_cat + steroids+thyroid_dz +cre_cys_gfr_calc, data =  master)

cox_multivariate %>%  tbl_regression(exponentiate = TRUE,
                                     show_single_row=c( "male","White","cad","dm","cirrhosis",
                                                        "ace_arb","ppi","diur","steroids","thyroid_dz"),
                                     label = list(male = "Female"),
                                     pvalue_fun = ~style_pvalue(.x, digits = 3)) %>% bold_p() %>%  
  bold_labels()%>% 
  modify_spanning_header(
    c(estimate, ci, p.value) ~  
      "** Multivariate cox regression N=1869 **")
Characteristic ** Multivariate cox regression N=1869 **
HR1 95% CI1 p-value
year_10 1.02 0.88, 1.18 0.780
Female 0.75 0.51, 1.08 0.124
White 1.55 0.92, 2.60 0.098
new_ref_within_30
    eGFRCYS ~ eGFRCRE(within 30) — —
    eGFRCYS<eGFRCRE(<-30) 1.97 1.25, 3.10 0.003
    eGFRCYS>eGFRCRE(>30) 0.91 0.35, 2.41 0.853
bmi_cat
    Normal Range — —
    Obse 1.07 0.60, 1.88 0.827
    Overweight 1.05 0.68, 1.62 0.828
    Underweight 1.26 0.47, 3.36 0.646
cad 1.01 0.67, 1.53 0.969
dm 1.04 0.67, 1.61 0.866
cirrhosis 1.23 0.71, 2.14 0.456
Malnutrition
    0 — —
    1 0.51 0.26, 1.00 0.048
ace_arb 0.88 0.58, 1.31 0.522
ppi 1.21 0.75, 1.97 0.434
diur 0.71 0.43, 1.19 0.200
alb_cat
    >=4 — —
    <3 29.7 10.9, 80.8 <0.001
    3-3.99 6.70 2.43, 18.5 <0.001
hgb_cat
    >=12 — —
    <10 2.96 1.19, 7.37 0.020
    10-11.99 1.93 0.74, 5.02 0.179
steroids 1.49 1.02, 2.18 0.041
thyroid_dz 1.09 0.74, 1.60 0.679
cre_cys_gfr_calc 0.99 0.98, 1.00 0.027
1 HR = Hazard Ratio, CI = Confidence Interval

#Predictors of Vancomycin level > 30 ## Vanco vs hgb_cat

vanco_updated_for_TO <- read.csv("vanco_updated_for_TO.csv")
vanco_30 <- vanco_updated_for_TO %>% select(EMPI,NEW30) %>% mutate(vanco_30_outcome=ifelse(is.na(NEW30),0,1)) %>%  select(-NEW30)


vanco_master <- master %>% merge(vanco_30,by="EMPI")


table(vanco_master$hgb_cat,vanco_master$vanco_outcome,useNA = "always")
##           
##              0   1 <NA>
##   >=12      17   0    0
##   <10      153  48    0
##   10-11.99  44   6    0
##   <NA>       0   0    0

vanco univariate logistic regression

 vanco_master%>%
  select( "year_10","male","White","new_ref_within_30",       
          "bmi","smoking","htn","cad","dm",              
          "cirrhosis","Malnutrition","thyroid_dz","ace_arb","ppi","diur", "steroids","cre",     
          "bl_alb","bl_hgb",       
          "vanco_30_outcome","cre_cys_gfr_calc"
  ) %>%
  tbl_uvregression(
    method = glm,
    y = vanco_30_outcome,
    show_single_row=c("male","White",        
                      "smoking","htn","cad","dm",              
                      "cirrhosis","Malnutrition","ace_arb","ppi","diur",      
                      "thyroid_dz", "steroids"),
    
    method.args = list(family = binomial),
    exponentiate = TRUE,
    label = list(male = "Female"),
    pvalue_fun = ~style_pvalue(.x, digits = 3)
  ) %>%
  bold_p() %>%        # bold p-values under a given threshold (default 0.05)
  bold_labels() %>% 
  modify_spanning_header(
    c(estimate, ci, p.value) ~  
      "**Univariate logistic regression Vancomycin **")
Characteristic N **Univariate logistic regression Vancomycin **
OR1 95% CI1 p-value
year_10 268 0.80 0.64, 0.98 0.033
Female 268 1.37 0.74, 2.53 0.317
White 268 1.75 0.74, 4.81 0.233
new_ref_within_30 268
    eGFRCYS ~ eGFRCRE(within 30) — —
    eGFRCYS<eGFRCRE(<-30) 3.16 1.43, 8.01 0.008
    eGFRCYS>eGFRCRE(>30) 0.91 0.05, 5.86 0.932
bmi 268 1.04 0.99, 1.10 0.080
smoking 268 0.87 0.47, 1.60 0.651
htn 268 1.35 0.57, 3.74 0.532
cad 268 1.21 0.62, 2.44 0.588
dm 268 3.20 1.32, 9.58 0.019
cirrhosis 268 1.07 0.41, 2.50 0.875
Malnutrition 268 0.42 0.10, 1.26 0.170
thyroid_dz 268 0.85 0.42, 1.64 0.632
ace_arb 268 0.87 0.47, 1.60 0.651
ppi 268 1.25 0.52, 3.48 0.639
diur 268 2.08 0.84, 6.28 0.145
steroids 268 3.48 1.80, 7.14 <0.001
cre 268 0.86 0.63, 1.09 0.277
bl_alb 268 0.84 0.51, 1.35 0.469
bl_hgb 268 0.80 0.64, 0.97 0.032
cre_cys_gfr_calc 268 0.99 0.98, 1.00 0.058
1 OR = Odds Ratio, CI = Confidence Interval

multivariate logistic regression for vanco

m1_vanco <- glm( vanco_30_outcome ~ year_10+ male + White+ new_ref_within_30 +bmi+ cad+ dm +steroids+bl_hgb+cre_cys_gfr_calc, family = binomial(link = 'logit'),data=vanco_master) 

m1_vanco %>%  tbl_regression(exponentiate = TRUE,
                             show_single_row=c("male","White","cad","dm","steroids"),
                             label = list(male = "Female"),
                             pvalue_fun = ~style_pvalue(.x, digits = 3)) %>% bold_p() %>% # bold p-values under a given threshold (default 0.05)
  bold_labels()%>% 
  modify_spanning_header(
    c(estimate, ci, p.value) ~  
      "**Multivariate logistic regression Vancomycin  **")
Characteristic **Multivariate logistic regression Vancomycin **
OR1 95% CI1 p-value
year_10 0.75 0.57, 0.98 0.038
Female 1.49 0.76, 2.96 0.245
White 1.66 0.66, 4.83 0.314
new_ref_within_30
    eGFRCYS ~ eGFRCRE(within 30) — —
    eGFRCYS<eGFRCRE(<-30) 2.58 1.08, 7.03 0.045
    eGFRCYS>eGFRCRE(>30) 0.97 0.05, 7.08 0.982
bmi 1.03 0.98, 1.09 0.210
cad 0.98 0.46, 2.17 0.965
dm 2.24 0.83, 7.21 0.137
steroids 2.81 1.38, 6.01 0.005
bl_hgb 0.91 0.71, 1.14 0.428
cre_cys_gfr_calc 0.99 0.97, 1.00 0.059
1 OR = Odds Ratio, CI = Confidence Interval