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% CI |
time_to_cre_year |
143,796 |
-0.90 |
-1.0, -0.76 |
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% CI |
time_to_cre_year |
66,956 |
-6.9 |
-7.4, -6.3 |
Table 1 survival_3year
|
Overall (N=2624) |
Race |
|
Asian |
60 (2.3%) |
Black |
60 (2.3%) |
Other/Unknown |
101 (3.8%) |
White |
2403 (91.6%) |
Ethnic_Group |
|
Hispanic |
15 (0.6%) |
Non_hispanic |
2432 (92.7%) |
Other |
177 (6.7%) |
male |
|
0 |
1222 (46.6%) |
1 |
1402 (53.4%) |
diu |
|
0 |
1254 (47.8%) |
1 |
1370 (52.2%) |
ace_arb |
|
0 |
1378 (52.5%) |
1 |
1246 (47.5%) |
esrd_kt |
|
0 |
2480 (94.5%) |
1 |
144 (5.5%) |
dm |
|
0 |
2271 (86.5%) |
1 |
353 (13.5%) |
htn |
|
0 |
1011 (38.5%) |
1 |
1613 (61.5%) |
ppi |
|
0 |
715 (27.2%) |
1 |
1909 (72.8%) |
steroids |
|
0 |
615 (23.4%) |
1 |
2009 (76.6%) |
smoking |
|
0 |
1222 (46.6%) |
1 |
1402 (53.4%) |
cad |
|
0 |
2050 (78.1%) |
1 |
574 (21.9%) |
ICI_Name_clean |
|
Atezolizumab |
152 (5.8%) |
Avelumab |
70 (2.7%) |
Cemiplimab |
42 (1.6%) |
Dostarlimab |
0 (0%) |
Durvalumab |
146 (5.6%) |
Ipilimumab |
62 (2.4%) |
Ipilimumab + Nivolumab |
337 (12.8%) |
Ipilimumab + Pembrolizumab |
1 (0.0%) |
Nivolumab |
543 (20.7%) |
Nivolumab + Relatlimab |
1 (0.0%) |
Pembrolizumab |
1270 (48.4%) |
ckd_stage_baseline |
|
Stage 1 |
1126 (42.9%) |
Stage 2 |
1146 (43.7%) |
Stage 3a |
263 (10.0%) |
Stage 3b |
77 (2.9%) |
Stage 4 |
12 (0.5%) |
ckd_stage_median |
|
Stage 1 |
1196 (45.6%) |
Stage 2 |
1123 (42.8%) |
Stage 3a |
224 (8.5%) |
Stage 3b |
70 (2.7%) |
Stage 4 |
10 (0.4%) |
Stage 5 (Kidney Failure) |
1 (0.0%) |
age_baseline |
|
Mean (SD) |
63.1 (12.7) |
Median [Min, Max] |
64.7 [8.55, 94.1] |
pre_MALBCRE_365days |
|
Mean (SD) |
123 (333) |
Median [Min, Max] |
23.0 [2.60, 2350] |
Missing |
2543 (96.9%) |
pre_CRE_180days |
|
Mean (SD) |
0.926 (0.281) |
Median [Min, Max] |
0.880 [0.300, 2.75] |
pre_HGB_180days |
|
Mean (SD) |
12.8 (1.77) |
Median [Min, Max] |
13.0 [6.40, 17.8] |
pre_ALB_180days |
|
Mean (SD) |
4.11 (0.393) |
Median [Min, Max] |
4.20 [1.60, 5.40] |
Missing |
4 (0.2%) |
pre_PLT_180days |
|
Mean (SD) |
251 (95.5) |
Median [Min, Max] |
235 [9.00, 1380] |
creatinine_median_365 |
|
Mean (SD) |
0.915 (0.285) |
Median [Min, Max] |
0.868 [0.360, 4.59] |
eGFR_cre_baseline |
|
Mean (SD) |
83.9 (20.0) |
Median [Min, Max] |
86.3 [19.5, 154] |
eGFR_cre_median |
|
Mean (SD) |
85.0 (19.4) |
Median [Min, Max] |
87.7 [12.4, 158] |
cre_doubling_3year |
|
0 |
2406 (91.7%) |
1 |
218 (8.3%) |
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% CI |
time_to_cre_year |
112,814 |
-0.78 |
-0.90, -0.66 |
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% CI |
time_to_cre_year |
45,098 |
-7.2 |
-7.8, -6.5 |
Table 1 survival_4year
|
Overall (N=1745) |
Race |
|
Asian |
38 (2.2%) |
Black |
28 (1.6%) |
Other/Unknown |
58 (3.3%) |
White |
1621 (92.9%) |
Ethnic_Group |
|
Hispanic |
9 (0.5%) |
Non_hispanic |
1616 (92.6%) |
Other |
120 (6.9%) |
male |
|
0 |
798 (45.7%) |
1 |
947 (54.3%) |
diu |
|
0 |
847 (48.5%) |
1 |
898 (51.5%) |
ace_arb |
|
0 |
917 (52.6%) |
1 |
828 (47.4%) |
esrd_kt |
|
0 |
1653 (94.7%) |
1 |
92 (5.3%) |
dm |
|
0 |
1518 (87.0%) |
1 |
227 (13.0%) |
htn |
|
0 |
671 (38.5%) |
1 |
1074 (61.5%) |
ppi |
|
0 |
444 (25.4%) |
1 |
1301 (74.6%) |
steroids |
|
0 |
392 (22.5%) |
1 |
1353 (77.5%) |
smoking |
|
0 |
831 (47.6%) |
1 |
914 (52.4%) |
cad |
|
0 |
1375 (78.8%) |
1 |
370 (21.2%) |
ICI_Name_clean |
|
Atezolizumab |
111 (6.4%) |
Avelumab |
46 (2.6%) |
Cemiplimab |
16 (0.9%) |
Dostarlimab |
0 (0%) |
Durvalumab |
83 (4.8%) |
Ipilimumab |
40 (2.3%) |
Ipilimumab + Nivolumab |
214 (12.3%) |
Ipilimumab + Pembrolizumab |
1 (0.1%) |
Nivolumab |
395 (22.6%) |
Nivolumab + Relatlimab |
1 (0.1%) |
Pembrolizumab |
838 (48.0%) |
ckd_stage_baseline |
|
Stage 1 |
762 (43.7%) |
Stage 2 |
756 (43.3%) |
Stage 3a |
168 (9.6%) |
Stage 3b |
52 (3.0%) |
Stage 4 |
7 (0.4%) |
ckd_stage_median |
|
Stage 1 |
802 (46.0%) |
Stage 2 |
747 (42.8%) |
Stage 3a |
139 (8.0%) |
Stage 3b |
49 (2.8%) |
Stage 4 |
7 (0.4%) |
Stage 5 (Kidney Failure) |
1 (0.1%) |
age_baseline |
|
Mean (SD) |
62.4 (12.6) |
Median [Min, Max] |
63.9 [18.4, 94.1] |
pre_MALBCRE_365days |
|
Mean (SD) |
151 (398) |
Median [Min, Max] |
21.1 [2.60, 2350] |
Missing |
1690 (96.8%) |
pre_CRE_180days |
|
Mean (SD) |
0.931 (0.285) |
Median [Min, Max] |
0.880 [0.300, 2.75] |
pre_HGB_180days |
|
Mean (SD) |
12.9 (1.72) |
Median [Min, Max] |
13.1 [7.20, 17.6] |
pre_ALB_180days |
|
Mean (SD) |
4.12 (0.384) |
Median [Min, Max] |
4.20 [1.60, 5.40] |
Missing |
2 (0.1%) |
pre_PLT_180days |
|
Mean (SD) |
249 (89.3) |
Median [Min, Max] |
235 [50.0, 917] |
creatinine_median_365 |
|
Mean (SD) |
0.918 (0.289) |
Median [Min, Max] |
0.870 [0.360, 4.59] |
eGFR_cre_baseline |
|
Mean (SD) |
84.1 (20.0) |
Median [Min, Max] |
86.8 [19.5, 143] |
eGFR_cre_median |
|
Mean (SD) |
85.4 (19.3) |
Median [Min, Max] |
88.1 [12.4, 158] |
cre_doubling_4year |
|
0 |
1584 (90.8%) |
1 |
161 (9.2%) |
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% CI |
time_to_cre_year |
79,456 |
-0.56 |
-0.68, -0.45 |
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% CI |
time_to_cre_year |
29,759 |
-7.8 |
-8.7, -7.0 |
Table 1 survival_5year
|
Overall (N=1088) |
Race |
|
Asian |
22 (2.0%) |
Black |
19 (1.7%) |
Other/Unknown |
38 (3.5%) |
White |
1009 (92.7%) |
Ethnic_Group |
|
Hispanic |
6 (0.6%) |
Non_hispanic |
1005 (92.4%) |
Other |
77 (7.1%) |
male |
|
0 |
497 (45.7%) |
1 |
591 (54.3%) |
diu |
|
0 |
535 (49.2%) |
1 |
553 (50.8%) |
ace_arb |
|
0 |
569 (52.3%) |
1 |
519 (47.7%) |
esrd_kt |
|
0 |
1039 (95.5%) |
1 |
49 (4.5%) |
dm |
|
0 |
959 (88.1%) |
1 |
129 (11.9%) |
htn |
|
0 |
410 (37.7%) |
1 |
678 (62.3%) |
ppi |
|
0 |
261 (24.0%) |
1 |
827 (76.0%) |
steroids |
|
0 |
241 (22.2%) |
1 |
847 (77.8%) |
smoking |
|
0 |
529 (48.6%) |
1 |
559 (51.4%) |
cad |
|
0 |
868 (79.8%) |
1 |
220 (20.2%) |
ICI_Name_clean |
|
Atezolizumab |
74 (6.8%) |
Avelumab |
32 (2.9%) |
Cemiplimab |
2 (0.2%) |
Dostarlimab |
0 (0%) |
Durvalumab |
34 (3.1%) |
Ipilimumab |
30 (2.8%) |
Ipilimumab + Nivolumab |
101 (9.3%) |
Ipilimumab + Pembrolizumab |
1 (0.1%) |
Nivolumab |
304 (27.9%) |
Nivolumab + Relatlimab |
1 (0.1%) |
Pembrolizumab |
509 (46.8%) |
ckd_stage_baseline |
|
Stage 1 |
521 (47.9%) |
Stage 2 |
442 (40.6%) |
Stage 3a |
95 (8.7%) |
Stage 3b |
26 (2.4%) |
Stage 4 |
4 (0.4%) |
ckd_stage_median |
|
Stage 1 |
540 (49.6%) |
Stage 2 |
437 (40.2%) |
Stage 3a |
82 (7.5%) |
Stage 3b |
23 (2.1%) |
Stage 4 |
5 (0.5%) |
Stage 5 (Kidney Failure) |
1 (0.1%) |
age_baseline |
|
Mean (SD) |
61.4 (12.5) |
Median [Min, Max] |
62.6 [18.4, 90.7] |
pre_MALBCRE_365days |
|
Mean (SD) |
85.7 (225) |
Median [Min, Max] |
18.6 [2.60, 1130] |
Missing |
1059 (97.3%) |
pre_CRE_180days |
|
Mean (SD) |
0.914 (0.278) |
Median [Min, Max] |
0.860 [0.300, 2.75] |
pre_HGB_180days |
|
Mean (SD) |
13.0 (1.70) |
Median [Min, Max] |
13.2 [7.20, 17.1] |
pre_ALB_180days |
|
Mean (SD) |
4.11 (0.386) |
Median [Min, Max] |
4.10 [1.60, 5.20] |
Missing |
2 (0.2%) |
pre_PLT_180days |
|
Mean (SD) |
248 (92.7) |
Median [Min, Max] |
234 [50.0, 917] |
creatinine_median_365 |
|
Mean (SD) |
0.908 (0.297) |
Median [Min, Max] |
0.860 [0.360, 4.59] |
eGFR_cre_baseline |
|
Mean (SD) |
85.9 (19.8) |
Median [Min, Max] |
88.8 [19.5, 143] |
eGFR_cre_median |
|
Mean (SD) |
86.8 (19.3) |
Median [Min, Max] |
89.8 [12.4, 158] |
cre_doubling_5year |
|
0 |
985 (90.5%) |
1 |
103 (9.5%) |
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% CI |
time_to_cre_year |
54,554 |
-0.45 |
-0.56, -0.34 |
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% CI |
time_to_cre_year |
18,644 |
-6.2 |
-7.2, -5.1 |
Table 1 survival_6year
|
Overall (N=664) |
Race |
|
Asian |
11 (1.7%) |
Black |
16 (2.4%) |
Other/Unknown |
20 (3.0%) |
White |
617 (92.9%) |
Ethnic_Group |
|
Hispanic |
5 (0.8%) |
Non_hispanic |
606 (91.3%) |
Other |
53 (8.0%) |
male |
|
0 |
314 (47.3%) |
1 |
350 (52.7%) |
diu |
|
0 |
338 (50.9%) |
1 |
326 (49.1%) |
ace_arb |
|
0 |
347 (52.3%) |
1 |
317 (47.7%) |
esrd_kt |
|
0 |
635 (95.6%) |
1 |
29 (4.4%) |
dm |
|
0 |
582 (87.7%) |
1 |
82 (12.3%) |
htn |
|
0 |
242 (36.4%) |
1 |
422 (63.6%) |
ppi |
|
0 |
141 (21.2%) |
1 |
523 (78.8%) |
steroids |
|
0 |
142 (21.4%) |
1 |
522 (78.6%) |
smoking |
|
0 |
329 (49.5%) |
1 |
335 (50.5%) |
cad |
|
0 |
536 (80.7%) |
1 |
128 (19.3%) |
ICI_Name_clean |
|
Atezolizumab |
46 (6.9%) |
Avelumab |
17 (2.6%) |
Cemiplimab |
0 (0%) |
Dostarlimab |
0 (0%) |
Durvalumab |
14 (2.1%) |
Ipilimumab |
28 (4.2%) |
Ipilimumab + Nivolumab |
63 (9.5%) |
Ipilimumab + Pembrolizumab |
0 (0%) |
Nivolumab |
178 (26.8%) |
Nivolumab + Relatlimab |
1 (0.2%) |
Pembrolizumab |
317 (47.7%) |
ckd_stage_baseline |
|
Stage 1 |
330 (49.7%) |
Stage 2 |
259 (39.0%) |
Stage 3a |
56 (8.4%) |
Stage 3b |
16 (2.4%) |
Stage 4 |
3 (0.5%) |
ckd_stage_median |
|
Stage 1 |
341 (51.4%) |
Stage 2 |
264 (39.8%) |
Stage 3a |
39 (5.9%) |
Stage 3b |
16 (2.4%) |
Stage 4 |
3 (0.5%) |
Stage 5 (Kidney Failure) |
1 (0.2%) |
age_baseline |
|
Mean (SD) |
61.1 (12.3) |
Median [Min, Max] |
62.3 [18.4, 87.7] |
pre_MALBCRE_365days |
|
Mean (SD) |
86.4 (262) |
Median [Min, Max] |
18.3 [2.60, 1130] |
Missing |
646 (97.3%) |
pre_CRE_180days |
|
Mean (SD) |
0.912 (0.283) |
Median [Min, Max] |
0.865 [0.300, 2.75] |
pre_HGB_180days |
|
Mean (SD) |
13.0 (1.66) |
Median [Min, Max] |
13.2 [7.30, 16.8] |
pre_ALB_180days |
|
Mean (SD) |
4.10 (0.400) |
Median [Min, Max] |
4.10 [1.60, 5.00] |
Missing |
2 (0.3%) |
pre_PLT_180days |
|
Mean (SD) |
249 (91.0) |
Median [Min, Max] |
234 [62.0, 917] |
creatinine_median_365 |
|
Mean (SD) |
0.903 (0.304) |
Median [Min, Max] |
0.860 [0.405, 4.59] |
eGFR_cre_baseline |
|
Mean (SD) |
86.0 (19.9) |
Median [Min, Max] |
89.8 [19.5, 143] |
eGFR_cre_median |
|
Mean (SD) |
87.1 (19.2) |
Median [Min, Max] |
90.8 [12.4, 158] |
cre_doubling_6year |
|
0 |
602 (90.7%) |
1 |
62 (9.3%) |
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% CI |
time_to_cre_year |
32,639 |
-0.40 |
-0.52, -0.28 |
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% CI |
time_to_cre_year |
10,617 |
-4.5 |
-5.9, -3.2 |
Table 1 survival_7year
|
Overall (N=368) |
Race |
|
Asian |
5 (1.4%) |
Black |
10 (2.7%) |
Other/Unknown |
9 (2.4%) |
White |
344 (93.5%) |
Ethnic_Group |
|
Hispanic |
3 (0.8%) |
Non_hispanic |
330 (89.7%) |
Other |
35 (9.5%) |
male |
|
0 |
160 (43.5%) |
1 |
208 (56.5%) |
diu |
|
0 |
186 (50.5%) |
1 |
182 (49.5%) |
ace_arb |
|
0 |
188 (51.1%) |
1 |
180 (48.9%) |
esrd_kt |
|
0 |
356 (96.7%) |
1 |
12 (3.3%) |
dm |
|
0 |
323 (87.8%) |
1 |
45 (12.2%) |
htn |
|
0 |
127 (34.5%) |
1 |
241 (65.5%) |
ppi |
|
0 |
77 (20.9%) |
1 |
291 (79.1%) |
steroids |
|
0 |
84 (22.8%) |
1 |
284 (77.2%) |
smoking |
|
0 |
184 (50.0%) |
1 |
184 (50.0%) |
cad |
|
0 |
302 (82.1%) |
1 |
66 (17.9%) |
ICI_Name_clean |
|
Atezolizumab |
29 (7.9%) |
Avelumab |
12 (3.3%) |
Cemiplimab |
0 (0%) |
Dostarlimab |
0 (0%) |
Durvalumab |
4 (1.1%) |
Ipilimumab |
18 (4.9%) |
Ipilimumab + Nivolumab |
43 (11.7%) |
Ipilimumab + Pembrolizumab |
0 (0%) |
Nivolumab |
88 (23.9%) |
Nivolumab + Relatlimab |
0 (0%) |
Pembrolizumab |
174 (47.3%) |
ckd_stage_baseline |
|
Stage 1 |
187 (50.8%) |
Stage 2 |
135 (36.7%) |
Stage 3a |
37 (10.1%) |
Stage 3b |
8 (2.2%) |
Stage 4 |
1 (0.3%) |
ckd_stage_median |
|
Stage 1 |
197 (53.5%) |
Stage 2 |
132 (35.9%) |
Stage 3a |
27 (7.3%) |
Stage 3b |
11 (3.0%) |
Stage 4 |
1 (0.3%) |
Stage 5 (Kidney Failure) |
0 (0%) |
age_baseline |
|
Mean (SD) |
60.9 (12.0) |
Median [Min, Max] |
62.3 [18.4, 85.8] |
pre_MALBCRE_365days |
|
Mean (SD) |
108 (309) |
Median [Min, Max] |
17.4 [2.60, 1130] |
Missing |
355 (96.5%) |
pre_CRE_180days |
|
Mean (SD) |
0.919 (0.267) |
Median [Min, Max] |
0.880 [0.310, 1.95] |
pre_HGB_180days |
|
Mean (SD) |
12.9 (1.73) |
Median [Min, Max] |
13.2 [7.30, 16.7] |
pre_ALB_180days |
|
Mean (SD) |
4.11 (0.411) |
Median [Min, Max] |
4.10 [1.60, 5.00] |
Missing |
1 (0.3%) |
pre_PLT_180days |
|
Mean (SD) |
248 (92.8) |
Median [Min, Max] |
236 [75.0, 917] |
creatinine_median_365 |
|
Mean (SD) |
0.907 (0.252) |
Median [Min, Max] |
0.870 [0.420, 2.02] |
eGFR_cre_baseline |
|
Mean (SD) |
86.0 (19.9) |
Median [Min, Max] |
90.3 [28.4, 130] |
eGFR_cre_median |
|
Mean (SD) |
87.0 (19.4) |
Median [Min, Max] |
91.7 [29.7, 131] |
cre_doubling_7year |
|
0 |
326 (88.6%) |
1 |
42 (11.4%) |