Automated Real-Time Percutaneous Coronary Intervention Risk Model Leveraging Electronic Health Records

Abstract

Background

Automated individualized risk prediction tools linked to electronic health records (EHRs) are not available for patients undergoing percutaneous coronary interventions (PCIs).

Objectives

Our goal was to automatically extract data elements used in the Mayo Clinic PCI models from EHR to enable point of care risk assessment.

Methods

Using the Mayo Clinic PCI registry, variables in the Mayo Clinic PCI risk score were trained and tested in an EHR to identify in-hospital death, stroke, bleeding, acute kidney injury (AKI) in patients who underwent PCI from 2016 to 2024. Least absolute shrinkage and selection operator regression was utilized to train (data building) and test (assessing performance) prediction models and to estimate effect sizes that were weighted and integrated into a scoring system.

Results

Death, stroke, bleeding, AKI occurred in 157 (1.8%), 43 (0.5%), 157 (1.8%), and 682 (7.6%), respectively. The C-statistics (95% CI) from the training and testing data sets were 0.83 (95% CI: 0.80-0.86) and 0.84 (95% CI: 0.78-0.89); 0.76 (95% CI: 0.65-0.84) and 0.77 (95% CI: 0.65-0.86); 0.80 (95% CI: 0.75-0.83) and 0.75 (95% CI: 0.68-0.81); and 0.82 (95% CI: 0.80-0.84) and 0.80 (95% CI: 0.77-0.84) for in-hospital death, stroke, bleeding, and AKI, respectively. Bootstrap analysis indicated that the models were not overfit to the available data set. The probabilities estimated from the models matched the observed data well, as indicated by the calibration curve slope and intercept and across subgroups, including women, acute coronary syndrome, cardiogenic shock, and diabetes mellitus.

Conclusions

Real-time, automated, point of care PCI risk assessment is feasible in an EHR environment.