Prognostication of Acute Kidney Injury Following Percutaneous Coronary Intervention: A Machine Learning Approach

Acute kidney injury (AKI) is a serious complication of percutaneous coronary intervention (PCI) associated with increased mortality and health care costs. Traditional risk scores often rely on intraprocedural variables, limiting their utility for preprocedural prophylaxis.

We aimed to develop and validate a machine learning model to predict post-PCI AKI using strictly preprocedural electronic health record data.

This retrospective cohort study analyzed routine electronic health record data from a tertiary medical center (2004-2022). The primary outcome was AKI, defined according to Kidney Disease: Improving Global Outcomes criteria (absolute serum creatinine increase ≥0.3 mg/dL or relative increase ≥50% within 48 hours). A gradient-boosted decision tree ensemble (CatBoost) was trained on preprocedural demographic, clinical, and laboratory variables. Performance was evaluated on a held-out test set (20%) using the area under the receiver operating characteristic curve and precision-recall curve.

The final cohort included 23,728 PCI procedures from 17,943 patients, with an AKI prevalence of 7.0%. On the held-out test set, the model achieved an area under the receiver operating characteristic curve of 0.85 (95% CI: 0.82-0.87) and a precision-recall curve of 0.38 (95% CI: 0.32-0.43). Calibration was excellent (integrated calibration index = 0.017). At the screening threshold (probability 0.041), sensitivity was 0.83 (95% CI: 0.80-0.87). At the rule-in threshold (probability 0.199), specificity was 0.92 (95% CI: 0.91-0.93). Key predictors included baseline creatinine, hemoglobin, uric acid, and white blood cell count.

In this single-center study, we developed a machine learning model using preprocedural variables that predicts post-PCI AKI. Although external validation is required, this model could support individualized risk stratification and preventive strategies.