a <- master %>% mutate(time_cate=case_when(time_to_cre<=365 ~ "<1 year",
                                           time_to_cre>365 & time_to_cre<=365*2 ~ "1-2 year",
                                           time_to_cre>365*2 & time_to_cre<=365*3 ~ "2-3 year"))
summary_df <- a %>%
  group_by(time_cate) %>%
  summarise(mean_post_egfr = mean(post_egfr, na.rm = TRUE)) %>%
  arrange(factor(time_cate, levels = c("1 year", "1-2 year", "2-3 year")))

# Step 3: Plot
ggplot(summary_df, aes(x = time_cate, y = mean_post_egfr)) +
  geom_col(fill = "steelblue") +
  labs(
    title = "Mean post_egfr by Time Category",
    x = "Time Category",
    y = "Mean post_egfr"
  ) +
  theme_minimal()

survival_3year cohort Model post_egfr ~ time_to_cre_year : time_to_cre<=365*3

master_3year <- master %>% filter(time_to_cre<=365*3,survival_3year==1) 

model <- lmer(post_egfr ~ time_to_cre_year + (1 | EMPI), data = master_3year)
model %>% tbl_regression( exponentiate = FALSE) %>% add_n()

Characteristic	N	Beta	95% CI¹
time_to_cre_year	21,183	-2.1	-2.3, -1.9
¹ CI = Confidence Interval

survival_3year cohort Model post_egfr ~ time_to_cre_year : time_to_cre<=365

master_3year <- master %>% filter(time_to_cre<=365,survival_3year==1)

model <- lmer(post_egfr ~ time_to_cre_year + (1 | EMPI), data = master_3year)
model %>% tbl_regression( exponentiate = FALSE) %>% add_n()

Characteristic	N	Beta	95% CI¹
time_to_cre_year	9,219	-4.9	-5.6, -4.3
¹ CI = Confidence Interval

Table 1 survival_3year

	Overall (N=353)
Race
Asian	15 (4.2%)
Black	18 (5.1%)
Other/Unknown	17 (4.8%)
White	303 (85.8%)
Ethnic_Group
Hispanic	1 (0.3%)
Non_hispanic	330 (93.5%)
Other	22 (6.2%)
male
0	155 (43.9%)
1	198 (56.1%)
diu
0	102 (28.9%)
1	251 (71.1%)
ace_arb
0	81 (22.9%)
1	272 (77.1%)
esrd_kt
0	307 (87.0%)
1	46 (13.0%)
dm
1	353 (100%)
htn
0	37 (10.5%)
1	316 (89.5%)
ppi
0	79 (22.4%)
1	274 (77.6%)
steroids
0	49 (13.9%)
1	304 (86.1%)
smoking
0	137 (38.8%)
1	216 (61.2%)
cad
0	210 (59.5%)
1	143 (40.5%)
ICI_Name_clean
Atezolizumab	28 (7.9%)
Avelumab	10 (2.8%)
Cemiplimab	7 (2.0%)
Dostarlimab	0 (0%)
Durvalumab	16 (4.5%)
Ipilimumab	4 (1.1%)
Ipilimumab + Nivolumab	49 (13.9%)
Nivolumab	61 (17.3%)
Nivolumab + Relatlimab	0 (0%)
Pembrolizumab	178 (50.4%)
ckd_stage_baseline
Stage 1	123 (34.8%)
Stage 2	153 (43.3%)
Stage 3a	56 (15.9%)
Stage 3b	18 (5.1%)
Stage 4	3 (0.8%)
ckd_stage_median
Stage 1	130 (36.8%)
Stage 2	150 (42.5%)
Stage 3a	52 (14.7%)
Stage 3b	17 (4.8%)
Stage 4	4 (1.1%)
Stage 5 (Kidney Failure)	0 (0%)
age_baseline
Mean (SD)	67.1 (9.72)
Median [Min, Max]	67.4 [38.0, 91.9]
pre_MALBCRE_365days
Mean (SD)	152 (420)
Median [Min, Max]	27.0 [2.60, 2350]
Missing	312 (88.4%)
pre_CRE_180days
Mean (SD)	0.992 (0.339)
Median [Min, Max]	0.910 [0.450, 2.75]
pre_HGB_180days
Mean (SD)	12.5 (1.79)
Median [Min, Max]	12.5 [7.20, 17.6]
pre_ALB_180days
Mean (SD)	4.04 (0.419)
Median [Min, Max]	4.10 [1.80, 5.00]
pre_PLT_180days
Mean (SD)	247 (94.9)
Median [Min, Max]	234 [73.0, 917]
creatinine_median_365
Mean (SD)	0.987 (0.333)
Median [Min, Max]	0.905 [0.470, 2.83]
eGFR_cre_baseline
Mean (SD)	77.7 (20.7)
Median [Min, Max]	78.8 [23.3, 128]
eGFR_cre_median
Mean (SD)	78.2 (20.3)
Median [Min, Max]	80.0 [22.5, 118]
cre_doubling_3year
0	315 (89.2%)
1	38 (10.8%)

survival_4year cohort Model post_egfr ~ time_to_cre_year : time_to_cre<=365*4

master_4year <- master %>% filter(time_to_cre<=365*4,survival_4year==1)

model <- lmer(post_egfr ~ time_to_cre_year + (1 | EMPI), data = master_4year)
model %>% tbl_regression( exponentiate = FALSE) %>% add_n()

Characteristic	N	Beta	95% CI¹
time_to_cre_year	16,325	-1.4	-1.5, -1.2
¹ CI = Confidence Interval

survival_4year cohort Model post_egfr ~ time_to_cre_year : time_to_cre<=365

master_4year <- master %>% filter(time_to_cre<=365,survival_4year==1)

model <- lmer(post_egfr ~ time_to_cre_year + (1 | EMPI), data = master_4year)
model %>% tbl_regression( exponentiate = FALSE) %>% add_n()

Characteristic	N	Beta	95% CI¹
time_to_cre_year	5,902	-5.6	-6.5, -4.8
¹ CI = Confidence Interval

Table 1 survival_4year

	Overall (N=227)
Race
Asian	6 (2.6%)
Black	10 (4.4%)
Other/Unknown	11 (4.8%)
White	200 (88.1%)
Ethnic_Group
Hispanic	0 (0%)
Non_hispanic	209 (92.1%)
Other	18 (7.9%)
male
0	97 (42.7%)
1	130 (57.3%)
diu
0	58 (25.6%)
1	169 (74.4%)
ace_arb
0	53 (23.3%)
1	174 (76.7%)
esrd_kt
0	196 (86.3%)
1	31 (13.7%)
dm
1	227 (100%)
htn
0	26 (11.5%)
1	201 (88.5%)
ppi
0	50 (22.0%)
1	177 (78.0%)
steroids
0	30 (13.2%)
1	197 (86.8%)
smoking
0	91 (40.1%)
1	136 (59.9%)
cad
0	138 (60.8%)
1	89 (39.2%)
ICI_Name_clean
Atezolizumab	20 (8.8%)
Avelumab	7 (3.1%)
Cemiplimab	4 (1.8%)
Dostarlimab	0 (0%)
Durvalumab	8 (3.5%)
Ipilimumab	4 (1.8%)
Ipilimumab + Nivolumab	27 (11.9%)
Nivolumab	44 (19.4%)
Nivolumab + Relatlimab	0 (0%)
Pembrolizumab	113 (49.8%)
ckd_stage_baseline
Stage 1	78 (34.4%)
Stage 2	101 (44.5%)
Stage 3a	36 (15.9%)
Stage 3b	10 (4.4%)
Stage 4	2 (0.9%)
ckd_stage_median
Stage 1	78 (34.4%)
Stage 2	102 (44.9%)
Stage 3a	34 (15.0%)
Stage 3b	10 (4.4%)
Stage 4	3 (1.3%)
Stage 5 (Kidney Failure)	0 (0%)
age_baseline
Mean (SD)	66.6 (9.78)
Median [Min, Max]	67.3 [38.0, 91.9]
pre_MALBCRE_365days
Mean (SD)	184 (494)
Median [Min, Max]	22.6 [2.60, 2350]
Missing	198 (87.2%)
pre_CRE_180days
Mean (SD)	1.00 (0.349)
Median [Min, Max]	0.920 [0.480, 2.75]
pre_HGB_180days
Mean (SD)	12.6 (1.79)
Median [Min, Max]	12.6 [7.20, 17.6]
pre_ALB_180days
Mean (SD)	4.05 (0.393)
Median [Min, Max]	4.10 [2.40, 5.00]
pre_PLT_180days
Mean (SD)	242 (96.7)
Median [Min, Max]	229 [73.0, 917]
creatinine_median_365
Mean (SD)	0.996 (0.342)
Median [Min, Max]	0.930 [0.470, 2.83]
eGFR_cre_baseline
Mean (SD)	77.3 (20.6)
Median [Min, Max]	77.0 [23.3, 115]
eGFR_cre_median
Mean (SD)	78.1 (19.9)
Median [Min, Max]	79.2 [22.5, 112]
cre_doubling_4year
0	198 (87.2%)
1	29 (12.8%)

survival_5year cohort Model post_egfr ~ time_to_cre_year : time_to_cre<=365*5

master_5year <- master %>% filter(time_to_cre<=365*5,survival_5year==1)

model <- lmer(post_egfr ~ time_to_cre_year + (1 | EMPI), data = master_5year)
model %>% tbl_regression( exponentiate = FALSE) %>% add_n()

Characteristic	N	Beta	95% CI¹
time_to_cre_year	9,971	-1.2	-1.3, -1.1
¹ CI = Confidence Interval

survival_5year cohort Model post_egfr ~ time_to_cre_year : time_to_cre<=365

master_5year <- master %>% filter(time_to_cre<=365,survival_5year==1)

model <- lmer(post_egfr ~ time_to_cre_year + (1 | EMPI), data = master_5year)
model %>% tbl_regression( exponentiate = FALSE) %>% add_n()

Characteristic	N	Beta	95% CI¹
time_to_cre_year	3,396	-7.0	-8.1, -5.9
¹ CI = Confidence Interval

Table 1 survival_5year

	Overall (N=129)
Race
Asian	2 (1.6%)
Black	8 (6.2%)
Other/Unknown	8 (6.2%)
White	111 (86.0%)
Ethnic_Group
Hispanic	0 (0%)
Non_hispanic	119 (92.2%)
Other	10 (7.8%)
male
0	57 (44.2%)
1	72 (55.8%)
diu
0	33 (25.6%)
1	96 (74.4%)
ace_arb
0	25 (19.4%)
1	104 (80.6%)
esrd_kt
0	116 (89.9%)
1	13 (10.1%)
dm
1	129 (100%)
htn
0	11 (8.5%)
1	118 (91.5%)
ppi
0	27 (20.9%)
1	102 (79.1%)
steroids
0	19 (14.7%)
1	110 (85.3%)
smoking
0	52 (40.3%)
1	77 (59.7%)
cad
0	82 (63.6%)
1	47 (36.4%)
ICI_Name_clean
Atezolizumab	15 (11.6%)
Avelumab	5 (3.9%)
Cemiplimab	1 (0.8%)
Dostarlimab	0 (0%)
Durvalumab	3 (2.3%)
Ipilimumab	1 (0.8%)
Ipilimumab + Nivolumab	11 (8.5%)
Nivolumab	29 (22.5%)
Nivolumab + Relatlimab	0 (0%)
Pembrolizumab	64 (49.6%)
ckd_stage_baseline
Stage 1	47 (36.4%)
Stage 2	57 (44.2%)
Stage 3a	21 (16.3%)
Stage 3b	3 (2.3%)
Stage 4	1 (0.8%)
ckd_stage_median
Stage 1	47 (36.4%)
Stage 2	60 (46.5%)
Stage 3a	18 (14.0%)
Stage 3b	2 (1.6%)
Stage 4	2 (1.6%)
Stage 5 (Kidney Failure)	0 (0%)
age_baseline
Mean (SD)	66.2 (9.73)
Median [Min, Max]	65.8 [38.0, 87.7]
pre_MALBCRE_365days
Mean (SD)	77.3 (140)
Median [Min, Max]	22.8 [2.60, 528]
Missing	115 (89.1%)
pre_CRE_180days
Mean (SD)	0.986 (0.347)
Median [Min, Max]	0.910 [0.510, 2.75]
pre_HGB_180days
Mean (SD)	12.4 (1.77)
Median [Min, Max]	12.6 [7.20, 16.8]
pre_ALB_180days
Mean (SD)	4.03 (0.384)
Median [Min, Max]	4.00 [2.40, 5.00]
pre_PLT_180days
Mean (SD)	241 (103)
Median [Min, Max]	229 [73.0, 917]
creatinine_median_365
Mean (SD)	0.974 (0.350)
Median [Min, Max]	0.900 [0.470, 2.83]
eGFR_cre_baseline
Mean (SD)	78.6 (20.0)
Median [Min, Max]	78.2 [23.3, 115]
eGFR_cre_median
Mean (SD)	79.9 (19.1)
Median [Min, Max]	79.5 [22.5, 112]
cre_doubling_5year
0	115 (89.1%)
1	14 (10.9%)

survival_6year cohort Model post_egfr ~ time_to_cre_year : time_to_cre<=365*6

master_6year <- master %>% filter(time_to_cre<=365*6,survival_6year==1)

model <- lmer(post_egfr ~ time_to_cre_year + (1 | EMPI), data = master_6year)
model %>% tbl_regression( exponentiate = FALSE) %>% add_n()

Characteristic	N	Beta	95% CI¹
time_to_cre_year	6,899	-0.85	-1.0, -0.71
¹ CI = Confidence Interval

survival_6year cohort Model post_egfr ~ time_to_cre_year : time_to_cre<=365

master_6year <- master %>% filter(time_to_cre<=365,survival_6year==1)

model <- lmer(post_egfr ~ time_to_cre_year + (1 | EMPI), data = master_6year)
model %>% tbl_regression( exponentiate = FALSE) %>% add_n()

Characteristic	N	Beta	95% CI¹
time_to_cre_year	2,127	-5.0	-6.2, -3.7
¹ CI = Confidence Interval

Table 1 survival_6year

	Overall (N=82)
Race
Asian	2 (2.4%)
Black	8 (9.8%)
Other/Unknown	4 (4.9%)
White	68 (82.9%)
Ethnic_Group
Hispanic	0 (0%)
Non_hispanic	73 (89.0%)
Other	9 (11.0%)
male
0	32 (39.0%)
1	50 (61.0%)
diu
0	24 (29.3%)
1	58 (70.7%)
ace_arb
0	17 (20.7%)
1	65 (79.3%)
esrd_kt
0	75 (91.5%)
1	7 (8.5%)
dm
1	82 (100%)
htn
0	8 (9.8%)
1	74 (90.2%)
ppi
0	18 (22.0%)
1	64 (78.0%)
steroids
0	15 (18.3%)
1	67 (81.7%)
smoking
0	36 (43.9%)
1	46 (56.1%)
cad
0	53 (64.6%)
1	29 (35.4%)
ICI_Name_clean
Atezolizumab	11 (13.4%)
Avelumab	3 (3.7%)
Cemiplimab	0 (0%)
Dostarlimab	0 (0%)
Durvalumab	1 (1.2%)
Ipilimumab	1 (1.2%)
Ipilimumab + Nivolumab	6 (7.3%)
Nivolumab	16 (19.5%)
Nivolumab + Relatlimab	0 (0%)
Pembrolizumab	44 (53.7%)
ckd_stage_baseline
Stage 1	30 (36.6%)
Stage 2	36 (43.9%)
Stage 3a	14 (17.1%)
Stage 3b	1 (1.2%)
Stage 4	1 (1.2%)
ckd_stage_median
Stage 1	31 (37.8%)
Stage 2	39 (47.6%)
Stage 3a	10 (12.2%)
Stage 3b	1 (1.2%)
Stage 4	1 (1.2%)
Stage 5 (Kidney Failure)	0 (0%)
age_baseline
Mean (SD)	66.2 (9.11)
Median [Min, Max]	65.3 [47.4, 87.7]
pre_MALBCRE_365days
Mean (SD)	28.9 (30.0)
Median [Min, Max]	16.7 [2.60, 92.0]
Missing	72 (87.8%)
pre_CRE_180days
Mean (SD)	1.00 (0.340)
Median [Min, Max]	0.945 [0.530, 2.75]
pre_HGB_180days
Mean (SD)	12.4 (1.79)
Median [Min, Max]	12.6 [8.80, 16.8]
pre_ALB_180days
Mean (SD)	4.00 (0.368)
Median [Min, Max]	4.00 [3.10, 4.80]
pre_PLT_180days
Mean (SD)	244 (106)
Median [Min, Max]	228 [75.0, 917]
creatinine_median_365
Mean (SD)	0.981 (0.342)
Median [Min, Max]	0.900 [0.470, 2.83]
eGFR_cre_baseline
Mean (SD)	78.4 (19.5)
Median [Min, Max]	77.1 [23.3, 112]
eGFR_cre_median
Mean (SD)	80.3 (18.5)
Median [Min, Max]	82.0 [22.5, 109]
cre_doubling_6year
0	73 (89.0%)
1	9 (11.0%)

survival_7year cohort Model post_egfr ~ time_to_cre_year : time_to_cre<=365*7

master_7year <- master %>% filter(time_to_cre<=365*7,survival_7year==1)

model <- lmer(post_egfr ~ time_to_cre_year + (1 | EMPI), data = master_7year)
model %>% tbl_regression( exponentiate = FALSE) %>% add_n()

Characteristic	N	Beta	95% CI¹
time_to_cre_year	3,695	-1.8	-2.0, -1.7
¹ CI = Confidence Interval

survival_7year cohort Model post_egfr ~ time_to_cre_year : time_to_cre<=365

master_7year <- master %>% filter(time_to_cre<=365,survival_7year==1)

model <- lmer(post_egfr ~ time_to_cre_year + (1 | EMPI), data = master_7year)
model %>% tbl_regression( exponentiate = FALSE) %>% add_n()

Characteristic	N	Beta	95% CI¹
time_to_cre_year	1,175	-3.3	-5.0, -1.5
¹ CI = Confidence Interval

Table 1 survival_7year

	Overall (N=45)
Race
Asian	0 (0%)
Black	5 (11.1%)
Other/Unknown	2 (4.4%)
White	38 (84.4%)
Ethnic_Group
Hispanic	0 (0%)
Non_hispanic	39 (86.7%)
Other	6 (13.3%)
male
0	16 (35.6%)
1	29 (64.4%)
diu
0	13 (28.9%)
1	32 (71.1%)
ace_arb
0	8 (17.8%)
1	37 (82.2%)
esrd_kt
0	43 (95.6%)
1	2 (4.4%)
dm
1	45 (100%)
htn
0	2 (4.4%)
1	43 (95.6%)
ppi
0	9 (20.0%)
1	36 (80.0%)
steroids
0	9 (20.0%)
1	36 (80.0%)
smoking
0	18 (40.0%)
1	27 (60.0%)
cad
0	30 (66.7%)
1	15 (33.3%)
ICI_Name_clean
Atezolizumab	6 (13.3%)
Avelumab	3 (6.7%)
Cemiplimab	0 (0%)
Dostarlimab	0 (0%)
Durvalumab	0 (0%)
Ipilimumab	1 (2.2%)
Ipilimumab + Nivolumab	4 (8.9%)
Nivolumab	10 (22.2%)
Nivolumab + Relatlimab	0 (0%)
Pembrolizumab	21 (46.7%)
ckd_stage_baseline
Stage 1	16 (35.6%)
Stage 2	20 (44.4%)
Stage 3a	8 (17.8%)
Stage 3b	1 (2.2%)
Stage 4	0 (0%)
ckd_stage_median
Stage 1	17 (37.8%)
Stage 2	20 (44.4%)
Stage 3a	7 (15.6%)
Stage 3b	1 (2.2%)
Stage 4	0 (0%)
Stage 5 (Kidney Failure)	0 (0%)
age_baseline
Mean (SD)	65.2 (8.76)
Median [Min, Max]	65.2 [47.4, 79.4]
pre_MALBCRE_365days
Mean (SD)	24.6 (28.8)
Median [Min, Max]	13.3 [2.60, 92.0]
Missing	37 (82.2%)
pre_CRE_180days
Mean (SD)	0.998 (0.286)
Median [Min, Max]	0.960 [0.570, 1.93]
pre_HGB_180days
Mean (SD)	12.1 (1.84)
Median [Min, Max]	11.9 [9.20, 16.1]
pre_ALB_180days
Mean (SD)	4.00 (0.382)
Median [Min, Max]	4.10 [3.10, 4.80]
pre_PLT_180days
Mean (SD)	253 (126)
Median [Min, Max]	234 [75.0, 917]
creatinine_median_365
Mean (SD)	0.983 (0.289)
Median [Min, Max]	0.925 [0.590, 2.02]
eGFR_cre_baseline
Mean (SD)	78.8 (19.7)
Median [Min, Max]	77.3 [36.9, 112]
eGFR_cre_median
Mean (SD)	80.6 (19.1)
Median [Min, Max]	83.3 [34.9, 109]
cre_doubling_7year
0	39 (86.7%)
1	6 (13.3%)

DM cohort

2025-04-07

survival_3year cohort Model post_egfr ~ time_to_cre_year : time_to_cre<=365*3

survival_3year cohort Model post_egfr ~ time_to_cre_year : time_to_cre<=365

Table 1 survival_3year

survival_4year cohort Model post_egfr ~ time_to_cre_year : time_to_cre<=365*4

survival_4year cohort Model post_egfr ~ time_to_cre_year : time_to_cre<=365

Table 1 survival_4year

survival_5year cohort Model post_egfr ~ time_to_cre_year : time_to_cre<=365*5

survival_5year cohort Model post_egfr ~ time_to_cre_year : time_to_cre<=365

Table 1 survival_5year

survival_6year cohort Model post_egfr ~ time_to_cre_year : time_to_cre<=365*6

survival_6year cohort Model post_egfr ~ time_to_cre_year : time_to_cre<=365

Table 1 survival_6year

survival_7year cohort Model post_egfr ~ time_to_cre_year : time_to_cre<=365*7

survival_7year cohort Model post_egfr ~ time_to_cre_year : time_to_cre<=365

Table 1 survival_7year