Logistic Regression Model in a Machine Learning Application to Predict Elderly Kidney Transplant Recipients with Worse Renal Function One Year after Kidney Transplant: Elderly KTbot
Table 1
Pretransplant comorbidity history among younger and elderly patients.
Pre-KT covariables
<60 years (n = 100)
≥60 years (n = 18)
RR
95% CI
n (%)
n (%)
Time on RRT pre-KT, months
50.5 ± 46.1 (0–258)
49.6 ± 33 (8–108)
—
—
0.675
Obesity pre-KT
1.046
0.316–8.722
No
93 (93%)
16 (88.9%)
0.625
Yes
07 (7%)
02 (11.1%)
Systemic arterial hypertension pre-KT
3.594
0.778–16.596
No
31 (31%)
02 (11.1%)
0.084
Yes
69 (69%)
16 (88.9%)
Diabetes mellitus pre-KT
8.089
2.549–25.667
No
91 (91%)
10 (55.6%)
0.001
Yes
09 (09%)
08 (44.4%)
Type of donor
—
—
Expanded spectrum of deceased donor
03 (03%)
03 (16.7%)
0.021
Standard criteria deceased donor
85 (85%)
15 (83.3%)
Living donor
12 (12%)
00 (-)
RR: relative risk; CI: confidence interval; RRT: renal replacement therapy; pre-KT: before kidney transplant. Chi-square test; Fisher’s exact test; and Mann–Whitney U test.
