percentage :discont_before_day3=ifelse(Discont_Day<3,1,0)

gmodels::CrossTable(final_master$discont_before_day3)

## Registered S3 method overwritten by 'gdata':
##   method         from     
##   reorder.factor DescTools

## 
##  
##    Cell Contents
## |-------------------------|
## |                       N |
## |         N / Table Total |
## |-------------------------|
## 
##  
## Total Observations in Table:  243 
## 
##  
##           |         0 |         1 | 
##           |-----------|-----------|
##           |       154 |        89 | 
##           |     0.634 |     0.366 | 
##           |-----------|-----------|
## 
## 
## 
##

percentage :pc_change_before_day3 =ifelse( (dosechange_terli_day1==1 | dosechange_terli_day2==1 | dosechange_terli_day3==1) & Discont_Day>3 ,1,0 )

gmodels::CrossTable(final_master$pc_change_before_day3)

## 
##  
##    Cell Contents
## |-------------------------|
## |                       N |
## |         N / Table Total |
## |-------------------------|
## 
##  
## Total Observations in Table:  243 
## 
##  
##           |         0 |         1 | 
##           |-----------|-----------|
##           |       206 |        37 | 
##           |     0.848 |     0.152 | 
##           |-----------|-----------|
## 
## 
## 
##

percentage : pc_nochange_after_day3 =ifelse(dosechange_terli_day1==0 & dosechange_terli_day2==0 & dosechange_terli_day3==0 & Discont_Day>3 ,1,0 )

gmodels::CrossTable(final_master$pc_nochange_after_day3)

## 
##  
##    Cell Contents
## |-------------------------|
## |                       N |
## |         N / Table Total |
## |-------------------------|
## 
##  
## Total Observations in Table:  243 
## 
##  
##           |         0 |         1 | 
##           |-----------|-----------|
##           |       171 |        72 | 
##           |     0.704 |     0.296 | 
##           |-----------|-----------|
## 
## 
## 
##

hrs_responders_cat_2: %>% filter(!is.na(new_var))

new <- final_master %>% mutate(new_var=case_when(pc_nochange_after_day3==1 ~ "group B:pc_nochange_after_day3",
                                                 pc_change_before_day3 ==1 ~ "group A:pc_change_before_day3")
                               ) %>% filter(!is.na(new_var))
table1(~new_var|hrs_responders_cat_2,new)

	0 (N=31)	1 (N=78)	Overall (N=109)
new_var
group A:pc_change_before_day3	15 (48.4%)	22 (28.2%)	37 (33.9%)
group B:pc_nochange_after_day3	16 (51.6%)	56 (71.8%)	72 (66.1%)

chisq.test(new$new_var,new$hrs_responders_cat_2)

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  new$new_var and new$hrs_responders_cat_2
## X-squared = 3.1799, df = 1, p-value = 0.07455

ckd_baseline

final_master$pc_change_before_day3 <- as.factor(final_master$pc_change_before_day3)
table1(~pc_change_before_day3|ckd_baseline,final_master %>% filter(!is.na(ckd_baseline)))

## Warning in table1.formula(~pc_change_before_day3 | ckd_baseline, final_master
## %>% : Terms to the right of '|' in formula 'x' define table columns and are
## expected to be factors with meaningful labels.

	0 (N=127)	1 (N=89)	Overall (N=216)
pc_change_before_day3
0	105 (82.7%)	79 (88.8%)	184 (85.2%)
1	22 (17.3%)	10 (11.2%)	32 (14.8%)

chisq.test(final_master$pc_change_before_day3,final_master$ckd_baseline)

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  final_master$pc_change_before_day3 and final_master$ckd_baseline
## X-squared = 1.0918, df = 1, p-value = 0.2961

percentage : pc_day3=ifelse( Discont_Day==3 ,1,0 )

gmodels::CrossTable(final_master$pc_day3)

## 
##  
##    Cell Contents
## |-------------------------|
## |                       N |
## |         N / Table Total |
## |-------------------------|
## 
##  
## Total Observations in Table:  243 
## 
##  
##           |         0 |         1 | 
##           |-----------|-----------|
##           |       203 |        40 | 
##           |     0.835 |     0.165 | 
##           |-----------|-----------|
## 
## 
## 
##

pc_change_before_day3

table1(~ Discont_Day | pc_change_before_day3, data = final_master,render.continuous = c(.="Mean (SD)", .="Median [Q1, Q3]"))

	0 (N=206)	1 (N=37)	Overall (N=243)
Discont_Day
Mean (SD)	3.53 (2.76)	6.76 (2.68)	4.02 (2.98)
Median [Q1, Q3]	3.00 [1.25, 4.00]	6.00 [4.00, 8.00]	3.00 [2.00, 5.00]

pc_nochange_after_day3

table1(~ Discont_Day |pc_nochange_after_day3 , data = final_master,render.continuous = c(.="Mean (SD)", .="Median [Q1, Q3]") )

## Warning in table1.formula(~Discont_Day | pc_nochange_after_day3, data =
## final_master, : Terms to the right of '|' in formula 'x' define table columns
## and are expected to be factors with meaningful labels.

	0 (N=171)	1 (N=72)	Overall (N=243)
Discont_Day
Mean (SD)	3.04 (2.48)	6.38 (2.75)	4.02 (2.98)
Median [Q1, Q3]	2.00 [1.00, 3.00]	5.50 [4.00, 8.00]	3.00 [2.00, 5.00]

percentage final_master_hrs_responders_group_1 :discont_before_day3=ifelse(Discont_Day<3,1,0)

gmodels::CrossTable(final_master_hrs_responders_group_1$discont_before_day3)

## 
##  
##    Cell Contents
## |-------------------------|
## |                       N |
## |         N / Table Total |
## |-------------------------|
## 
##  
## Total Observations in Table:  134 
## 
##  
##           |         0 |         1 | 
##           |-----------|-----------|
##           |       104 |        30 | 
##           |     0.776 |     0.224 | 
##           |-----------|-----------|
## 
## 
## 
##

percentage final_master_hrs_responders_group_1 :pc_change_before_day3 =ifelse( (dosechange_terli_day1==1 | dosechange_terli_day2==1 | dosechange_terli_day3==1) & Discont_Day>3 ,1,0 )

gmodels::CrossTable(final_master_hrs_responders_group_1$pc_change_before_day3)

## 
##  
##    Cell Contents
## |-------------------------|
## |                       N |
## |         N / Table Total |
## |-------------------------|
## 
##  
## Total Observations in Table:  134 
## 
##  
##           |         0 |         1 | 
##           |-----------|-----------|
##           |       112 |        22 | 
##           |     0.836 |     0.164 | 
##           |-----------|-----------|
## 
## 
## 
##

percentage final_master_hrs_responders_group_1 : pc_nochange_after_day3 =ifelse(dosechange_terli_day1==0 & dosechange_terli_day2==0 & dosechange_terli_day3==0 & Discont_Day>3 ,1,0 )

gmodels::CrossTable(final_master_hrs_responders_group_1$pc_nochange_after_day3)

## 
##  
##    Cell Contents
## |-------------------------|
## |                       N |
## |         N / Table Total |
## |-------------------------|
## 
##  
## Total Observations in Table:  134 
## 
##  
##           |         0 |         1 | 
##           |-----------|-----------|
##           |        78 |        56 | 
##           |     0.582 |     0.418 | 
##           |-----------|-----------|
## 
## 
## 
##

percentage final_master_hrs_responders_group_1 : pc_day3=ifelse( Discont_Day==3 ,1,0 )

gmodels::CrossTable(final_master_hrs_responders_group_1$pc_day3)

## 
##  
##    Cell Contents
## |-------------------------|
## |                       N |
## |         N / Table Total |
## |-------------------------|
## 
##  
## Total Observations in Table:  134 
## 
##  
##           |         0 |         1 | 
##           |-----------|-----------|
##           |       110 |        24 | 
##           |     0.821 |     0.179 | 
##           |-----------|-----------|
## 
## 
## 
##

pc_change_before_day3

table1(~ Discont_Day | pc_change_before_day3, data = final_master_hrs_responders_group_1,render.continuous = c(.="Mean (SD)", .="Median [Q1, Q3]"))

## Warning in table1.formula(~Discont_Day | pc_change_before_day3, data =
## final_master_hrs_responders_group_1, : Terms to the right of '|' in formula 'x'
## define table columns and are expected to be factors with meaningful labels.

	0 (N=112)	1 (N=22)	Overall (N=134)
Discont_Day
Mean (SD)	4.48 (3.16)	6.59 (2.77)	4.83 (3.18)
Median [Q1, Q3]	4.00 [2.00, 6.00]	6.00 [4.00, 7.75]	4.00 [3.00, 6.00]

pc_nochange_after_day3

table1(~ Discont_Day |pc_nochange_after_day3 , data = final_master_hrs_responders_group_1,render.continuous = c(.="Mean (SD)", .="Median [Q1, Q3]") )

## Warning in table1.formula(~Discont_Day | pc_nochange_after_day3, data =
## final_master_hrs_responders_group_1, : Terms to the right of '|' in formula 'x'
## define table columns and are expected to be factors with meaningful labels.

	0 (N=78)	1 (N=56)	Overall (N=134)
Discont_Day
Mean (SD)	3.45 (2.61)	6.75 (2.92)	4.83 (3.18)
Median [Q1, Q3]	3.00 [2.00, 4.00]	6.00 [4.00, 8.25]	4.00 [3.00, 6.00]

#1 Univariate logistic regression outcome hrs_responders_cat_2: aclf_grade_new 3 lvls

table(final_master$hrs_responders_cat_2,final_master$aclf_grade)

##    
##      0  1  2  3
##   0  0 19 31 59
##   1  2 28 54 50

  final_master %>% 
    select(sbp_previous_comp,refascites_previous_comp,scr_terli_day0,tbili_admit,aclf_grade_3,hrs_responders_cat_2) %>%
    tbl_uvregression(
      method = glm,
      method.args = list(family = binomial),
      y = hrs_responders_cat_2,
      pvalue_fun = ~style_pvalue(.x, digits = 3),
      exponentiate = TRUE
    ) %>%
    bold_p(0.05) %>%        # bold p-values under a given threshold (default 0.05)
    bold_labels()

Characteristic	N	OR¹	95% CI¹	p-value
sbp_previous_comp	241
0		—	—
1		2.32	1.17, 4.82	0.019
refascites_previous_comp	204
0		—	—
1		1.90	1.09, 3.34	0.025
scr_terli_day0	243	0.73	0.56, 0.95	0.021
tbili_admit	235	0.96	0.94, 0.99	0.002
aclf_grade_3	243
0		—	—
1		0.50	0.30, 0.84	0.009
¹ OR = Odds Ratio, CI = Confidence Interval

#1 Multivariate logistic regression outcome hrs_responders_cat_2

glm(
  hrs_responders_cat_2 ~ sbp_previous_comp + refascites_previous_comp +
    scr_terli_day0 + tbili_admit +aclf_grade_3,
  data = final_master,
  family = binomial
) %>% tbl_regression(pvalue_fun = ~style_pvalue(.x, digits = 3),
      exponentiate = TRUE) %>%
    bold_p(0.05) %>%        # bold p-values under a given threshold (default 0.05)
    bold_labels()

Characteristic	OR¹	95% CI¹	p-value
sbp_previous_comp
0	—	—
1	2.03	0.94, 4.64	0.078
refascites_previous_comp
0	—	—
1	1.53	0.82, 2.84	0.179
scr_terli_day0	0.82	0.59, 1.13	0.222
tbili_admit	0.98	0.95, 1.01	0.147
aclf_grade_3
0	—	—
1	0.86	0.45, 1.67	0.651
¹ OR = Odds Ratio, CI = Confidence Interval

#2 Univariate logistic regression outcome HRS_reversal_new

  final_master %>% 
    select(scr_terli_day0,sodium_admit,sbp_previous_comp,refascites_previous_comp,hcc_previous_comp,clif_c_score,HRS_reversal_new) %>%
    tbl_uvregression(
      method = glm,
      method.args = list(family = binomial),
      y = HRS_reversal_new,
      pvalue_fun = ~style_pvalue(.x, digits = 3),
      exponentiate = TRUE
    ) %>%
    bold_p(0.05) %>%        # bold p-values under a given threshold (default 0.05)
    bold_labels()

Characteristic	N	OR¹	95% CI¹	p-value
scr_terli_day0	243	0.35	0.23, 0.51	<0.001
sodium_admit	234	0.94	0.90, 0.98	0.003
sbp_previous_comp	241
0		—	—
1		2.58	1.33, 5.03	0.005
refascites_previous_comp	204
0		—	—
1		2.11	1.15, 3.96	0.018
hcc_previous_comp	242
0		—	—
1		3.02	1.19, 7.82	0.020
clif_c_score	234	0.94	0.90, 0.97	<0.001
¹ OR = Odds Ratio, CI = Confidence Interval

#2 Multivariate logistic regression outcome HRS_reversal_new

glm(
  HRS_reversal_new ~ scr_terli_day0+sodium_admit+sbp_previous_comp+refascites_previous_comp+hcc_previous_comp+clif_c_score,
  data = final_master,
  family = binomial
) %>% tbl_regression(pvalue_fun = ~style_pvalue(.x, digits = 3),
      exponentiate = TRUE) %>%
    bold_p(0.05) %>%        # bold p-values under a given threshold (default 0.05)
    bold_labels()

Characteristic	OR¹	95% CI¹	p-value
scr_terli_day0	0.43	0.26, 0.67	<0.001
sodium_admit	0.95	0.90, 1.00	0.044
sbp_previous_comp
0	—	—
1	3.59	1.54, 8.61	0.003
refascites_previous_comp
0	—	—
1	1.24	0.60, 2.56	0.564
hcc_previous_comp
0	—	—
1	2.16	0.64, 7.22	0.207
clif_c_score	0.95	0.91, 1.00	0.044
¹ OR = Odds Ratio, CI = Confidence Interval

#3.1 Unviariate cmp: time_to_death_status_90days_cmp failcode=1 :

## 10 cases omitted due to missing values
## 8 cases omitted due to missing values

Characteristic	N	HR¹	95% CI¹	p-value
hrs_responders_cat_2	243
0		—	—
1		0.56	0.38, 0.83	0.004
scr_terli_day0	243	1.04	0.85, 1.26	0.710
akin	233
1		—	—
2		1.40	0.80, 2.46	0.240
3		1.50	0.89, 2.53	0.130
tbili_admit	235	1.01	1.00, 1.03	0.067
aclf_grade_3	243
0		—	—
1		1.93	1.31, 2.86	<0.001
¹ HR = Hazard Ratio, CI = Confidence Interval

#3.1 Multiviariate cmp: time_to_death_status_90days_cmp failcode=1

## 18 cases omitted due to missing values

Characteristic	N	HR¹	95% CI¹	p-value
hrs_responders_cat_2	225
0		—	—
1		0.59	0.39, 0.90	0.015
scr_terli_day0	225	0.87	0.67, 1.12	0.3
akin	225
1		—	—
2		1.40	0.79, 2.48	0.2
3		1.67	0.92, 3.03	0.090
tbili_admit	225	1.0	0.98, 1.01	0.6
aclf_grade_3	225
0		—	—
1		2.01	1.24, 3.24	0.004
¹ HR = Hazard Ratio, CI = Confidence Interval

#3.2 Unviariate cmp: time_to_death_status_90days_cmp failcode=1

## 10 cases omitted due to missing values
## 8 cases omitted due to missing values

Characteristic	N	HR¹	95% CI¹	p-value
HRS_reversal_new	243
0		—	—
1		0.66	0.42, 1.02	0.059
scr_terli_day0	243	1.04	0.85, 1.26	0.710
akin	233
1		—	—
2		1.40	0.80, 2.46	0.240
3		1.50	0.89, 2.53	0.130
tbili_admit	235	1.01	1.00, 1.03	0.067
aclf_grade_3	243
0		—	—
1		1.93	1.31, 2.86	<0.001
¹ HR = Hazard Ratio, CI = Confidence Interval

#3.2 Multiviariate cmp: time_to_death_status_90days_cmp failcode=1

## 18 cases omitted due to missing values

Characteristic	N	HR¹	95% CI¹	p-value
HRS_reversal_new	225
0		—	—
1		0.62	0.37, 1.02	0.061
scr_terli_day0	225	0.82	0.62, 1.08	0.2
akin	225
1		—	—
2		1.42	0.80, 2.54	0.2
3		1.70	0.93, 3.10	0.086
tbili_admit	225	1.00	0.98, 1.02	0.8
aclf_grade_3	225
0		—	—
1		1.98	1.23, 3.18	0.005
¹ HR = Hazard Ratio, CI = Confidence Interval

#4.1 Unviariate cmp: time_to_death_status_90days_cmp failcode=1:

Characteristic	N	HR¹	95% CI¹	p-value
hrs_responders_cat_2	243
0		—	—
1		0.56	0.38, 0.83	0.004
age	243	1.01	0.99, 1.03	0.410
race_2	243	1.06	0.62, 1.80	0.830
hispanic	243
0		—	—
1		0.76	0.43, 1.36	0.360
admit_meld_3	243	1.02	1.00, 1.05	0.070
center	243	1.04	0.96, 1.13	0.350
¹ HR = Hazard Ratio, CI = Confidence Interval

#4.1 Multiviariate cmp: time_to_death_status_90days_cmp failcode=1

Characteristic	N	HR¹	95% CI¹	p-value
hrs_responders_cat_2	243
0		—	—
1		0.57	0.38, 0.85	0.005
age	243	1.02	1.00, 1.04	0.10
race_2	243	1.12	0.64, 1.96	0.7
hispanic	243
0		—	—
1		0.85	0.49, 1.50	0.6
admit_meld_3	243	1.03	1.00, 1.06	0.049
center	243	1.04	0.96, 1.13	0.4
¹ HR = Hazard Ratio, CI = Confidence Interval

#4.2 Unviariate cmp: time_to_death_status_90days_cmp failcode=1

## 2 cases omitted due to missing values

Characteristic	N	HR¹	95% CI¹	p-value
HRS_reversal_new	243
0		—	—
1		0.66	0.42, 1.02	0.059
race	241
1		—	—
2		0.67	0.24, 1.83	0.430
3		0.00	0.00, 0.00	<0.001
4		1.38	0.76, 2.49	0.280
hispanic	243
0		—	—
1		0.76	0.43, 1.36	0.360
admit_meld_3	243	1.02	1.00, 1.05	0.070
center	243	1.04	0.96, 1.13	0.350
¹ HR = Hazard Ratio, CI = Confidence Interval

#4.2 Multiviariate cmp: time_to_death_status_90days_cmp failcode=1

Characteristic	N	HR¹	95% CI¹	p-value
HRS_reversal_new	243
0		—	—
1		0.73	0.46, 1.15	0.2
age	243	1.01	0.99, 1.03	0.2
race_2	243	1.14	0.66, 1.97	0.6
hispanic	243
0		—	—
1		0.79	0.44, 1.40	0.4
admit_meld_3	243	1.03	1.00, 1.06	0.063
center	243	1.03	0.94, 1.12	0.5
¹ HR = Hazard Ratio, CI = Confidence Interval

#5 survived_day3 Multiviariate cmp: time_to_death_status_90days_cmp failcode=1

Characteristic	N	HR¹	95% CI¹	p-value
hrs_responders_cat_2	236
0		—	—
1		0.61	0.40, 0.92	0.019
age	236	1.02	1.00, 1.04	0.056
race_2	236	1.14	0.64, 2.01	0.7
hispanic	236
0		—	—
1		0.84	0.47, 1.51	0.6
admit_meld_3	236	1.04	1.01, 1.07	0.018
¹ HR = Hazard Ratio, CI = Confidence Interval

Plot 1 scr hrs_responders_cat_2

risk table HRS Responders

hrs_responders_cat_2	n_baseline_scr	n_last_scr	n_scr_terli_day0	n_scr_terli_day1	n_scr_terli_day2	n_scr_terli_day3	n_scr_day1_post_terli	n_scr_day2_post_terli	n_scr_day3_post_terli	n_dc_scr
0	109	107	109	108	74	49	95	90	87	87
1	133	132	134	133	116	103	115	103	98	118

Plot 1 scr HRS_reversal_new

# risk table HRS Reversal

HRS_reversal_new	n_baseline_scr	n_last_scr	n_scr_terli_day0	n_scr_terli_day1	n_scr_terli_day2	n_scr_terli_day3	n_scr_day1_post_terli	n_scr_day2_post_terli	n_scr_day3_post_terli	n_dc_scr
0	167	165	168	166	119	87	143	133	129	137
1	75	74	75	75	71	65	67	60	56	68

any_ae

	0 (N=201)	1 (N=42)	Overall (N=243)
totalalbumin_before_terli
Mean (SD)	237 (171)	302 (238)	247 (184)
Median [Q1, Q3]	200 [113, 350]	225 [175, 359]	200 [125, 350]
Missing	17 (8.5%)	8 (19.0%)	25 (10.3%)

## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  totalalbumin_before_terli by any_ae
## W = 2616.5, p-value = 0.1301
## alternative hypothesis: true location shift is not equal to 0

any_ae no missing

	0 (N=201)	1 (N=42)	Overall (N=243)
totalalbumin_before_terli
Mean (SD)	217 (177)	244 (245)	222 (190)
Median [Q1, Q3]	188 [75.0, 325]	200 [87.5, 331]	188 [75.0, 325]

## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  totalalbumin_before_terli by any_ae
## W = 4156.5, p-value = 0.8771
## alternative hypothesis: true location shift is not equal to 0

terli_ae_respfail

	0 (N=220)	1 (N=23)	Overall (N=243)
totalalbumin_before_terli
Mean (SD)	239 (172)	329 (270)	247 (184)
Median [Q1, Q3]	200 [125, 350]	250 [179, 394]	200 [125, 350]
Missing	22 (10.0%)	3 (13.0%)	25 (10.3%)

## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  totalalbumin_before_terli by terli_ae_respfail
## W = 1567.5, p-value = 0.1251
## alternative hypothesis: true location shift is not equal to 0

terli_ae_respfail no mssing

	0 (N=220)	1 (N=23)	Overall (N=243)
totalalbumin_before_terli
Mean (SD)	215 (178)	286 (276)	222 (190)
Median [Q1, Q3]	188 [75.0, 321]	225 [93.8, 369]	188 [75.0, 325]

## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  totalalbumin_before_terli by terli_ae_respfail
## W = 2194.5, p-value = 0.2958
## alternative hypothesis: true location shift is not equal to 0

Plots revision 1210

2025-12-10

percentage :discont_before_day3=ifelse(Discont_Day<3,1,0)

percentage :pc_change_before_day3 =ifelse( (dosechange_terli_day1==1 | dosechange_terli_day2==1 | dosechange_terli_day3==1) & Discont_Day>3 ,1,0 )

percentage : pc_nochange_after_day3 =ifelse(dosechange_terli_day1==0 & dosechange_terli_day2==0 & dosechange_terli_day3==0 & Discont_Day>3 ,1,0 )

hrs_responders_cat_2: %>% filter(!is.na(new_var))

ckd_baseline

percentage : pc_day3=ifelse( Discont_Day==3 ,1,0 )

pc_change_before_day3

pc_nochange_after_day3

percentage final_master_hrs_responders_group_1 :discont_before_day3=ifelse(Discont_Day<3,1,0)

percentage final_master_hrs_responders_group_1 :pc_change_before_day3 =ifelse( (dosechange_terli_day1==1 | dosechange_terli_day2==1 | dosechange_terli_day3==1) & Discont_Day>3 ,1,0 )

percentage final_master_hrs_responders_group_1 : pc_nochange_after_day3 =ifelse(dosechange_terli_day1==0 & dosechange_terli_day2==0 & dosechange_terli_day3==0 & Discont_Day>3 ,1,0 )

percentage final_master_hrs_responders_group_1 : pc_day3=ifelse( Discont_Day==3 ,1,0 )

pc_change_before_day3

pc_nochange_after_day3

Plot 1 scr hrs_responders_cat_2

risk table HRS Responders

Plot 1 scr HRS_reversal_new

any_ae

any_ae no missing

terli_ae_respfail

terli_ae_respfail no mssing