Machine Learning and Shock Indices-Derived Score for Predicting Contrast-Induced Nephropathy in ACS Patients.

Abstract

Background: Contrast-induced nephropathy (CIN) is a serious complication following acute coronary syndrome (ACS), leading to increased morbidity and mortality. Machine learning (ML), combined with parameters such as shock indices, can potentially improve CIN risk prediction by analyzing complex variable interactions and creating accessible, clinically applicable models.

Methods: This retrospective case-control study included 719 ACS patients who underwent percutaneous coronary intervention (PCI). Patients were divided into two groups (CIN and non-CIN), and clinical, procedural, and hemodynamic parameters, including shock indices, were analyzed using machine learning algorithms. A new predictive model, CIN-Predict 5, was developed using the Gradient Boosting Machine (GBM) algorithm, incorporating clinically relevant and statistically significant variables. Correlations between model predictions and secondary outcomes, including in-hospital mortality and hospitalization duration, were evaluated.

Results: Among the variables used in the GBM algorithm, the Modified Shock Index emerged as the most significant predictor, with an importance score of 0.25. The CIN-Predict 5 model achieved an AUC of 0.87, outperforming the Mehran Risk Score (AUC = 0.75) for predicting CIN. The secondary outcomes showed that CIN-Predict 5 correlated significantly with in hospital mortality (r = 0.16, p < 0.001) and hospitalization duration (r = 0.20, p < 0.001).

Conclusions: The GBM-based model we developed, utilizing shock indices and derived through ML, provides a practical tool for early identification of high-risk CIN patients post-ACS, enabling timely preventive strategies and improving clinical decision-making.