A Predictive Model to Detect High-risk Chronic Kidney Disease Following Postorthotopic Liver Transplantation in Indian Cohort: An Ambispective, Single-center Analysis

Introduction: In liver transplantation (LT), chronic kidney disease (CKD) prediction and management improve morbidity and mortality, both of which are challenging and no Indian study has explored this field of research. We aimed to develop a predictor model to detect kidney disease initiative and outcome CKD 3a or higher, with preoperative risk factors. Materials and Methods: In this study of liver donor LT, a derivation cohort (retrospective arm, n = 196) and a validation cohort (prospective arm, n = 46) were studied during 2017–2020. Outcome assessed was stage CKD 3a or higher. Results: The incidence of CKD 3a or higher in derivation and validation cohorts was 50/196 (25.5%) and 9/46 (19.5%), respectively, with a follow-up of 3 years. There was statistically significant difference between derivation and validation cohorts for following preoperative risk factors: pretransplant estimated glomerular filtration rate (eGFR), preoperative platelet count, and preoperative acute kidney injury (AKI). The predictor equation for the development of CKD 3a after living donor liver transplant (LDLT) was R = −0.2691 + 1.1193 × diabetes – 0.0213 × pretransplant eGFR + 2.0542 × pre-AKI, with optimal cutoff = 0.03176. A C-statistics of 0.7237 was obtained in the validation cohort, with sensitivity and negative predictive values of 77% and 92.31%, respectively. Conclusion: We report the first Indian study which predicts the development of high-risk CKD in the liver transplant population. As the risk factors for CKD development in the Indian context are different from western data, this report will help in improving LDLT outcomes in emerging nations like India.