Development and validation of an explainable machine learning model for predicting acute kidney injury in critically ill patients with primary peritonitis: a multicenter cohort study.

Primary peritonitis-associated acute kidney injury (PPA-AKI) represents a complex complication that substantially elevates mortality risk. Currently, no effective machine learning (ML) models exist for precise detection. This investigation aims to construct an interpretable ML model for forecasting PPA-AKI risk while determining modifiable risk factors. This investigation used two cohorts: a derivation cohort (n = 329) from Hengyang Central Hospital (HYCH) and a validation cohort (n = 367) from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. Using the Boruta algorithm selection, 15 characteristics were determined and incorporated into 12 ML approaches, yielding 113 combinations, from which we identified the Stepglm[both] + gradient boosting machine as a superior algorithm for predicting PPA-AKI. The PPA-AKI index, comprising 9 variables, exhibited excellent diagnostic capabilities, attaining an area under the curve (AUC) of 0.976 (95%CI: 0.963-0.989) within the HYCH and 0.901 (95%CI: 0.870-0.931) within the MIMIC-IV, along with notable fitting performance and robustness. The Shapley Additive Explanations ranks the importance of features (urine output, prognostic nutritional index, blood urea nitrogen (BUN) and phosphate), and visualizes individual and global PPA-AKI risk prediction. Restricted cubic spline regression alongside threshold effect analysis revealed a nonlinear association between magnesium, BUN, creatinine and PPA-AKI, whilst generating inflection points for these features. To offer a more flexible predictive instrument, the PPA-AKI model was developed utilizing a free, publicly accessible web-based calculator (https://lglcz.shinyapps.io/DynMod/). This study developed and validated a low-cost, accurate, and readily available diagnostic tool for PPA-AKI, offering potential utility in PPA-AKI preventive management.