Table 1

Table 2

Additonal table

#Kaplan Meier HRS vs Non-HRS

#MODEL 1 Multivariate Cox regression model for 90 days alive or dead

Characteristic N Model 1 mutilvariate cox ph regression
HR1 95% CI1 p-value
age_admission 332 1.01 1.00, 1.02 0.154
sex 332
    1 — —
    2 0.97 0.73, 1.28 0.816
White 332
    0 — —
    1 1.43 1.03, 1.98 0.034
hispanic_race 332
    0 — —
    1 0.67 0.42, 1.09 0.105
hrs 332
    Non-HRS — —
    HRS 1.34 0.89, 2.03 0.166
liver_transplant_listed 332
    0 — —
    1 0.20 0.13, 0.32 <0.001
study_site 332
    High_volume_RRT — —
    Low_volume_RRT 1.23 0.91, 1.66 0.171
MELD_Na_baseline 332 1.04 1.02, 1.05 <0.001
pressor 332
    0 — —
    1 1.59 1.12, 2.26 0.010
initial_rrt 332
    1 — —
    2 2.74 1.88, 4.00 <0.001
1 HR = Hazard Ratio, CI = Confidence Interval

#MODEL 1 Sensitivity: Sensitivity Multivariate cox regression model for 90 days alive or dead

Characteristic Sensitivity model 1 mutilvariate cox ph regression
HR1 95% CI1 p-value
hrs_atn_group
    ATN — —
    HRS 1.11 0.71, 1.73 0.642
age_admission 1.00 0.99, 1.02 0.494
sex
    1 — —
    2 0.94 0.69, 1.29 0.706
White
    0 — —
    1 1.21 0.84, 1.74 0.309
hispanic_race
    0 — —
    1 0.73 0.41, 1.28 0.267
liver_transplant_listed
    0 — —
    1 0.23 0.14, 0.37 <0.001
study_site
    High_volume_RRT — —
    Low_volume_RRT 1.39 0.99, 1.93 0.055
MELD_Na_baseline 1.04 1.02, 1.06 <0.001
pressor
    0 — —
    1 1.22 0.82, 1.82 0.320
initial_rrt
    1 — —
    2 2.68 1.75, 4.09 <0.001
1 HR = Hazard Ratio, CI = Confidence Interval

#MODEL 2 Test interaction

Characteristic N Interaction cox ph regression
HR1 95% CI1 p-value
hrs 332
    Non-HRS — —
    HRS 1.29 0.71, 2.33 0.400
study_site 332
    High_volume_RRT — —
    Low_volume_RRT 1.22 0.88, 1.69 0.237
age_admission 332 1.01 1.00, 1.02 0.157
sex 332
    1 — —
    2 0.97 0.73, 1.28 0.823
White 332
    0 — —
    1 1.43 1.03, 1.98 0.034
hispanic_race 332
    0 — —
    1 0.67 0.42, 1.09 0.107
liver_transplant_listed 332
    0 — —
    1 0.20 0.13, 0.32 <0.001
MELD_Na_baseline 332 1.04 1.02, 1.05 <0.001
pressor 332
    0 — —
    1 1.59 1.12, 2.26 0.009
initial_rrt 332
    1 — —
    2 2.74 1.88, 4.00 <0.001
hrs * study_site 332
    HRS * Low_volume_RRT 1.08 0.49, 2.39 0.855
1 HR = Hazard Ratio, CI = Confidence Interval

#MODEL 3 Multivariate cox regression model for 90 days alive or dead

Characteristic Model 3 mutilvariate cox ph regression
HR1 95% CI1 p-value
hrs
    Non-HRS — —
    HRS 0.80 0.54, 1.18 0.261
study_site
    High_volume_RRT — —
    Low_volume_RRT 1.02 0.76, 1.35 0.919
encephalopathy_admission
    0 — —
    1 1.32 0.97, 1.79 0.077
MELD_Na_baseline 1.02 1.01, 1.04 0.009
pressor
    0 — —
    1 1.58 1.12, 2.23 0.010
initial_rrt
    1 — —
    2 1.99 1.38, 2.87 <0.001
1 HR = Hazard Ratio, CI = Confidence Interval

#Supplementary Table S1

#Supplementary Table S2

#Supplementary Table S3

#addtional 1

#addtional 2

#addtional 1

Revision

master_rrt$status_90days <- as.factor(master_rrt$status_90days)


tidycmprsk::crr(Surv(time_90days,status_90days) ~ hrs,failcode=1,cencode=0, data = master_rrt) %>% tbl_regression(exp = TRUE)
Characteristic HR1 95% CI1 p-value
hrs
    Non-HRS — —
    HRS 0.72 0.50, 1.04 0.077
1 HR = Hazard Ratio, CI = Confidence Interval 
tidycmprsk::cuminc(Surv(time_90days,status_90days) ~ hrs , data = master_rrt ) %>%
  ggcuminc() +
  add_confidence_interval() +
  add_risktable()+
  scale_x_continuous(breaks = seq(0, 90, by = 30), limits = c(0, 90))+
  theme(panel.background = element_rect(fill = "white", color = NA),
        plot.background = element_rect(fill = "white", color = NA),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank())+
  annotate("text", x = 85, y = 0.35, label = paste("p =", 0.077), size = 4)
## Plotting outcome "1".

pressor=1

master_rrt_pressor_1 <- master_rrt %>% filter(pressor==1)
master_rrt_pressor_0 <- master_rrt %>% filter(pressor==0)

gmodels::CrossTable(master_rrt_pressor_1$final_type_of_aki,master_rrt_pressor_1$death_in_90days_status, prop.chisq=F,chisq = T,prop.r=F, prop.c=F,
           prop.t=F)
## Registered S3 method overwritten by 'gdata':
##   method         from     
##   reorder.factor DescTools
## Warning in chisq.test(t, correct = FALSE, ...): Chi-squared approximation may
## be incorrect
## 
##  
##    Cell Contents
## |-------------------------|
## |                       N |
## |-------------------------|
## 
##  
## Total Observations in Table:  258 
## 
##  
##                                        | master_rrt_pressor_1$death_in_90days_status 
## master_rrt_pressor_1$final_type_of_aki |         0 |         1 | Row Total | 
## ---------------------------------------|-----------|-----------|-----------|
##                                    ATN |        52 |       122 |       174 | 
## ---------------------------------------|-----------|-----------|-----------|
##                                HRS-AKI |        16 |        23 |        39 | 
## ---------------------------------------|-----------|-----------|-----------|
##                                  Other |         2 |         3 |         5 | 
## ---------------------------------------|-----------|-----------|-----------|
##                               Prerenal |         7 |         9 |        16 | 
## ---------------------------------------|-----------|-----------|-----------|
##                    Unable to diagnosis |         8 |        16 |        24 | 
## ---------------------------------------|-----------|-----------|-----------|
##                           Column Total |        85 |       173 |       258 | 
## ---------------------------------------|-----------|-----------|-----------|
## 
##  
## Statistics for All Table Factors
## 
## 
## Pearson's Chi-squared test 
## ------------------------------------------------------------
## Chi^2 =  2.850067     d.f. =  4     p =  0.5832215 
## 
## 
##

pressor=0

gmodels::CrossTable(master_rrt_pressor_0$final_type_of_aki,master_rrt_pressor_0$death_in_90days_status, prop.chisq=F,chisq = T,prop.r=F, prop.c=F,
           prop.t=F)
## Warning in chisq.test(t, correct = FALSE, ...): Chi-squared approximation may
## be incorrect
## 
##  
##    Cell Contents
## |-------------------------|
## |                       N |
## |-------------------------|
## 
##  
## Total Observations in Table:  114 
## 
##  
##                                        | master_rrt_pressor_0$death_in_90days_status 
## master_rrt_pressor_0$final_type_of_aki |         0 |         1 | Row Total | 
## ---------------------------------------|-----------|-----------|-----------|
##                                    ATN |        28 |        32 |        60 | 
## ---------------------------------------|-----------|-----------|-----------|
##                                HRS-AKI |        15 |         9 |        24 | 
## ---------------------------------------|-----------|-----------|-----------|
##                                  Other |         3 |         0 |         3 | 
## ---------------------------------------|-----------|-----------|-----------|
##                               Prerenal |         6 |         3 |         9 | 
## ---------------------------------------|-----------|-----------|-----------|
##                    Unable to diagnosis |        12 |         6 |        18 | 
## ---------------------------------------|-----------|-----------|-----------|
##                           Column Total |        64 |        50 |       114 | 
## ---------------------------------------|-----------|-----------|-----------|
## 
##  
## Statistics for All Table Factors
## 
## 
## Pearson's Chi-squared test 
## ------------------------------------------------------------
## Chi^2 =  6.139969     d.f. =  4     p =  0.1889363 
## 
## 
##