Predicting In-Hospital Mortality in Patients Undergoing Percutaneous Coronary Intervention

Abstract

Background Standardization of risk is critical in benchmarking and quality improvement efforts for percutaneous coronary interventions (PCI). In 2018, the CathPCI Registry was updated to include additional variables to better classify higher-risk patients. Objectives We sought to develop a model for predicting in-hospital mortality risk following PCI incorporating these additional variables. Methods Data from 706,263 PCIs performed between 7/2018-6/2019 at 1,608 sites were used to develop and validate a new full and pre-catheterization model to predict in-hospital mortality, and a simplified bedside risk score. The sample was randomly split into a development (70%, n=495,005) and validation cohort (30%, n=211,258). We created 1,000 bootstrapped samples of the development cohort and used stepwise selection logistic regression on each sample. The final model included variables that were selected in at least 70% of the bootstrapped samples and those identified a priori due to clinical relevance. Results In-hospital mortality following PCI varied based on clinical presentation. Procedural urgency, cardiovascular instability, and level of consciousness after cardiac arrest were most predictive of in-hospital mortality. The full model performed well, with excellent discrimination (c-index: 0.943) in the validation cohort and good calibration across different clinical and procedural risk cohorts. The median hospital risk-standardized mortality rate was 1.9% and ranged from 1.1% to 3.3% (interquartile range: 1.7%-2.1%). Conclusions The risk of mortality following PCI can be predicted in contemporary practice by incorporating variables that reflect clinical acuity. This model, which includes data previously not captured, is a valid instrument for risk stratification and for quality improvement efforts.