• 90-day survival curve respiratory_failure

#Table 2 ##aki_stage_4

## # A tibble: 5 × 5
##   aki_stage_4 death_discharge event_rate_30 event_rate_60 event_rate_90
##   <fct>                 <dbl>         <dbl>         <dbl>         <dbl>
## 1 1                     0.182         0.232         0.303         0.343
## 2 2                     0.191         0.261         0.351         0.405
## 3 3                     0.341         0.434         0.478         0.527
## 4 4                     0.573         0.530         0.624         0.658
## 5 <NA>                  0.5           1             1             1

##icu_admission

## # A tibble: 3 × 5
##   icu_admission death_discharge event_rate_30 event_rate_60 event_rate_90
##   <fct>                   <dbl>         <dbl>         <dbl>         <dbl>
## 1 0                      0.0613         0.147         0.227         0.270
## 2 1                      0.488          0.502         0.575         0.619
## 3 <NA>                   0              0             0             0
## # A tibble: 4 × 5
##   AKI_responders    death_discharge event_rate_30 event_rate_60 event_rate_90
##   <fct>                       <dbl>         <dbl>         <dbl>         <dbl>
## 1 complete response           0.06         0.0728         0.139         0.212
## 2 no response                 0.551        0.577          0.654         0.684
## 3 partial response            0.192        0.273          0.354         0.404
## 4 <NA>                        0.5          1              1             1

##respiratory_failure_cat

## `summarise()` has grouped output by 'respiratory_failure_cat'. You can override
## using the `.groups` argument.
## # A tibble: 2 × 6
## # Groups:   respiratory_failure_cat [2]
##   respiratory_failure_cat fig.width death_discharge event_rate_30 event_rate_60
##   <fct>                       <dbl>           <dbl>         <dbl>         <dbl>
## 1 Ordinal 1-2                    10           0.625         0.599         0.673
## 2 Ordinal 3-5                    10           0.159         0.243         0.317
##   event_rate_90
##           <dbl>
## 1         0.704
## 2         0.371

##liver_transplant_listed

## # A tibble: 2 × 5
##   liver_transplant_listed death_discharge event_rate_30 event_rate_60
##   <fct>                             <dbl>         <dbl>         <dbl>
## 1 0                                 0.400         0.441         0.518
## 2 1                                 0.220         0.195         0.256
##   event_rate_90
##           <dbl>
## 1         0.564
## 2         0.280

Fine and Gray event: death competing risk: liver transplant multivariate

## 15 cases omitted due to missing values
Characteristic N Fine and Gray model 90-day mortality
HR1 95% CI1 p-value
age_admission 613 1.01 1.00, 1.02 0.14
sex 613
    Female — —
    Male 1.01 0.80, 1.27 >0.9
White 613 0.84 0.64, 1.10 0.2
hispanic_race 613 0.60 0.36, 0.99 0.046
MELD_Na_baseline 613 1.04 1.03, 1.06 <0.001
hrs_vasoconstrictor 613 1.06 0.83, 1.35 0.7
icu_admission 613 2.94 2.14, 4.02 <0.001
liver_transplant_listed 613 0.28 0.17, 0.44 <0.001
1 HR = Hazard Ratio, CI = Confidence Interval

Model AKI_responders

## # weights:  30 (18 variable)
## initial  value 671.252108 
## iter  10 value 537.344090
## iter  20 value 534.890596
## final  value 534.889628 
## converged
## ℹ Multinomial models have a different underlying structure than the models
## gtsummary was designed for. Other gtsummary functions designed to work with
## tbl_regression objects may yield unexpected results.
Characteristic N Multinominal model AKI_responders
OR1 95% CI1 p-value
complete response
age_admission 611 1.00 0.98, 1.01 0.8
sex 611
    Female — —
    Male 1.30 0.86, 1.98 0.2
White 611 1.38 0.83, 2.30 0.2
hispanic_race 611 1.34 0.60, 3.01 0.5
MELD_Na_baseline 611 0.98 0.96, 1.00 0.065
hrs_vasoconstrictor 611 0.69 0.45, 1.06 0.091
icu_admission 611 0.28 0.18, 0.44 <0.001
liver_transplant_listed 611 0.85 0.46, 1.57 0.6
partial response
age_admission 611 1.01 0.99, 1.03 0.3
sex 611
    Female — —
    Male 0.92 0.57, 1.48 0.7
White 611 2.01 1.04, 3.89 0.038
hispanic_race 611 1.03 0.35, 3.03 >0.9
MELD_Na_baseline 611 0.99 0.96, 1.02 0.5
hrs_vasoconstrictor 611 0.74 0.45, 1.22 0.2
icu_admission 611 0.22 0.13, 0.38 <0.001
liver_transplant_listed 611 0.68 0.32, 1.46 0.3
1 OR = Odds Ratio, CI = Confidence Interval

Model ICU_admission

Characteristic N Logistic model ICU_admission
OR1 95% CI1 p-value
age_admission 613 0.99 0.98, 1.00 0.11
sex 613
    Female — —
    Male 1.34 0.92, 1.96 0.13
White 613 1.25 0.78, 1.96 0.3
hispanic_race 613 0.60 0.29, 1.30 0.2
MELD_Na_baseline 613 1.02 1.00, 1.04 0.094
hrs_vasoconstrictor 613 2.63 1.77, 3.95 <0.001
liver_transplant_listed 613 0.58 0.34, 1.02 0.055
1 OR = Odds Ratio, CI = Confidence Interval

Model aki_stage_4

## # weights:  40 (27 variable)
## initial  value 847.025855 
## iter  10 value 747.584744
## iter  20 value 713.079384
## iter  30 value 711.979106
## final  value 711.979031 
## converged
## ℹ Multinomial models have a different underlying structure than the models
## gtsummary was designed for. Other gtsummary functions designed to work with
## tbl_regression objects may yield unexpected results.
Characteristic N Multinominal model aki_stage_4
OR1 95% CI1 p-value
Stage 2
age_admission 611 0.99 0.97, 1.02 0.5
sex 611
    Female — —
    Male 1.01 0.56, 1.81 >0.9
White 611 1.80 0.86, 3.77 0.12
hispanic_race 611 2.70 0.75, 9.72 0.13
MELD_Na_baseline 611 1.00 0.97, 1.04 0.8
hrs_vasoconstrictor 611 1.41 0.76, 2.64 0.3
icu_admission 611 1.50 0.84, 2.67 0.2
liver_transplant_listed 611 0.61 0.25, 1.48 0.3
Stage 3
age_admission 611 0.98 0.96, 1.00 0.018
sex 611
    Female — —
    Male 0.68 0.40, 1.15 0.2
White 611 1.37 0.71, 2.62 0.3
hispanic_race 611 1.79 0.51, 6.27 0.4
MELD_Na_baseline 611 1.04 1.01, 1.07 0.021
hrs_vasoconstrictor 611 1.63 0.92, 2.88 0.10
icu_admission 611 2.16 1.26, 3.72 0.005
liver_transplant_listed 611 0.37 0.16, 0.87 0.023
Stage 4
age_admission 611 0.97 0.95, 0.99 0.002
sex 611
    Female — —
    Male 0.80 0.46, 1.38 0.4
White 611 0.99 0.51, 1.91 >0.9
hispanic_race 611 1.79 0.49, 6.56 0.4
MELD_Na_baseline 611 1.05 1.02, 1.09 0.001
hrs_vasoconstrictor 611 3.46 1.94, 6.14 <0.001
icu_admission 611 9.70 5.01, 18.8 <0.001
liver_transplant_listed 611 1.20 0.56, 2.60 0.6
1 OR = Odds Ratio, CI = Confidence Interval 
## Warning in ModuleReturnVarsExist(vars, data): The data frame does not have:
## albumin_amount_prior db_admit Dropped

Model rrt

Characteristic N Logistic model RRT
OR1 95% CI1 p-value
age_admission 613 0.98 0.97, 0.99 0.004
sex 613
    Female — —
    Male 1.06 0.74, 1.52 0.8
White 613 0.76 0.49, 1.18 0.2
hispanic_race 613 0.85 0.40, 1.78 0.7
MELD_Na_baseline 613 1.04 1.01, 1.06 <0.001
hrs_vasoconstrictor 613 2.98 2.08, 4.30 <0.001
liver_transplant_listed 613 1.75 1.06, 2.92 0.030
1 OR = Odds Ratio, CI = Confidence Interval 
## Warning in ModuleReturnVarsExist(vars, data): The data frame does not have:
## albumin_amount_prior db_admit Dropped
## Warning in ModuleReturnVarsExist(vars, data): The data frame does not have:
## albumin_amount_prior db_admit Dropped