Abstract
Background
The prognostic value of the Prognostic Nutritional Index (PNI) in critically ill patients with coronary heart disease (CHD) remains unclear. This multicenter retrospective cohort study aimed to investigate the association between PNI and in-hospital mortality in severe CHD patients and develop a machine learning-based predictive model.
Methods
This study is a multicenter retrospective cohort study, extracting data of adult critically ill patients with CHD who met the inclusion criteria from the MIMIC-IV database and the eICU-CRD database. Multivariate logistic regression and restricted cubic spline (RCS) analyses were used to evaluate the relationship between PNI and in-hospital mortality and 365-day mortality. The Boruta algorithm and LASSO regression were used to screen predictive features, six machine learning models were constructed, and SHapley Additive explanation (SHAP) was used to interpret the importance of model features.
Results
Low PNI was significantly associated with higher in-hospital mortality (OR=0.95, 95% CI 0.94–0.97). RCS revealed a linear negative relationship between PNI and mortality. The Random Forest model demonstrated superior predictive performance (AUC=0.846), significantly outperforming traditional scoring systems. SHAP analysis identified CRRT, Sepsis, Vasopressor, PNI, and BUN as key predictors. An online clinical calculator was developed based on these findings.
Conclusions
This study developed a machine learning-based calculator to predict in-hospital mortality in patients with CHD in the ICU (
https://leongxj.shinyapps.io/chd-mortality-prediction/
). This calculator may guide clinical decision-making and improve the treatment of patients with CHD by identifying those at higher risk of in-hospital death.