Interpretable machine learning reveals phosphorus-to-albumin ratio as a novel predictor of mortality and acute kidney injury in critically ill pancreatitis patients: a multi-center retrospective analysis.

Acute kidney injury (AKI), a common and severe complication of acute pancreatitis (AP), is amenable to early intervention. Phosphorus-to-albumin ratio (PAR) is a novel composite biomarker unexplored in AP largely. Data of ICU patients with AP were extracted from MIMIC-IV database and eICU-CRD, respectively. PAR's link to prognosis and AKI in AP were analyzed via Kaplan-Meier curves, Cox regression, restricted cubic splines (RCS), and logistic regression. Subgroup analysis tested interactions. Key variables were identified using least absolute shrinkage and selection operator regression. AKI prediction models were built and evaluated via seven machine learning (ML) algorithms. Shapley additive explanations (SHAP) interpreted variable contributions. Survival analysis, Cox models, and RCS collectively demonstrated PAR, as a potential risk factor, is associated with 28-day and 1-year all-cause mortality in patients with AP. Logistic regression identified PAR as a risk factor for AKI development in AP. AKI-related clinical features including PAR were indentified. Seven ML models were constructed, among which the Light Gradient Boosting Machine (LightGBM) model achieved an area under receiver operating characteristic curve (AUROC) of 0.880 (95% CI 0.825-0.935) and area under precision-recall curve (AUPRC) of 0.944 in the test set, and an AUROC of 0.837 (95% CI 0.785-0.889) and AUPRC of 0.784 in the external validation set. SHAP analysis of the LightGBM model confirmed that higher PAR levels corresponded to a higher predicted probability of AKI. PAR is associated with prognosis and AKI of patients with AP. Integrating PAR with other key clinical features, our LightGBM model provides physicians with a streamlined and efficient tool for early AKI identification in high-risk AP patients.