Objective: Patients with cardiac surgery-associated acute kidney injury are at risk of renal replacement therapy and in-hospital death. We aimed to develop and validate a novel predictive model for poor in-hospital outcomes among patients with cardiac surgery-associated acute kidney injury.
Methods: A total of 196 patients diagnosed with cardiac surgery-associated acute kidney injury were enrolled in this study as the training cohort, and 32 blood cytokines were measured. Least absolute shrinkage and selection operator regression and random forest quantile-classifier were performed to identify the key blood predictors for in-hospital composite outcomes (requiring renal replacement therapy or in-hospital death). The logistic regression model incorporating the selected predictors was validated internally using bootstrapping and externally in an independent cohort (n = 52).
Results: A change in serum creatinine (delta serum creatinine) and interleukin 16 and interleukin 8 were selected as key predictors for composite outcomes. The logistic regression model incorporating interleukin 16, interleukin 8, and delta serum creatinine yielded the optimal performance, with decent discrimination (area under the receiver operating characteristic curve: 0.947; area under the precision-recall curve: 0.809) and excellent calibration (Brier score: 0.056, Hosmer-Lemeshow test P = .651). Application of the model in the validation cohort yielded good discrimination. A nomogram was generated for clinical use, and decision curve analysis demonstrated that the new model adds more net benefit than delta serum creatinine.
Conclusions: We developed and validated a promising predictive model for in-hospital composite outcomes among patients with cardiac surgery-associated acute kidney injury and demonstrated interleukin-16 and interleukin-8 as useful predictors to improve risk stratification for poor in-hospital outcomes among those with cardiac surgery-associated acute kidney injury.
Keywords: CSA-AKI; poor in-hospital outcome; predictive model; predictors.
Copyright © 2021 The American Association for Thoracic Surgery. Published by Elsevier Inc. All rights reserved.