Derivation and validation of predictive indices for 30-day mortality after coronary and valvular surgery in Ontario,Canada

Background:Coronary artery bypass grafting (CABG) and surgical aortic valve replacement (AVR) are the 2 most common cardiac surgery procedures in North America. We derived and externally validated clinical models to estimate the likelihood of death within 30 days of CABG, AVR or combined CABG + AVR.Methods:We obtained data from the CorHealth Ontario Cardiac Registry and several linked population health administrative databases from Ontario, Canada. We derived multiple logistic regression models from all adult patients who underwent CABG, AVR or combined CABG + AVR from April 2017 to March 2019, and validated them in 2 temporally distinct cohorts (April 2015 to March 2017 and April 2019 to March 2020).Results:The derivation cohorts included 13 435 patients who underwent CABG (30-d mortality 1.73%), 1970 patients who underwent AVR (30-d mortality 1.68%) and 1510 patients who underwent combined CABG + AVR (30-d mortality 3.05%). The final models for predicting 30-day mortality included 15 variables for patients undergoing CABG, 5 variables for patients undergoing AVR and 5 variables for patients undergoing combined CABG + AVR. Model discrimination was excellent for the CABG (c-statistic 0.888, optimism-corrected 0.866) AVR (c-statistic 0.850, optimism-corrected 0.762) and CABG + AVR (c-statistic 0.844, optimism-corrected 0.776) models, with similar results in the validation cohorts.Interpretation:Our models, leveraging readily available, multidimensional data sources, computed accurate risk-adjusted 30-day mortality rates for CABG, AVR and combined CABG + AVR, with discrimination comparable to more complex American and European models. The ability to accurately predict perioperative mortality rates for these procedures will be valuable for quality improvement initiatives across institutions.

Coronary artery bypass grafting (CABG) and surgical aortic valve replacement (AVR) are 2 of the most common cardiac surgical procedures in North America.¹ Accurate risk models of perioperative mortality for CABG and AVR are not only useful for operative decision-making,² but also valuable for quality improvement initiatives across surgeons and institutions.In North America, the most widely used 30-day mortality risk score is the Society of Thoracic Surgeons (STS)–Predicted Risk of Mortality tool, derived from more than 1000 hospitals in the United States and encompassing more than 50 variables.³ An ideal risk model should be built and validated on the patient population in which it will be applied. Although the STS–Predicted Risk of Mortality tool was derived from a large surgical population, regional differences in patient sociodemographics and health care delivery systems may preclude this model from performing optimally in the health system where cardiac surgery is publicly funded. Furthermore, collecting more than 50 variables is resource intensive and is not feasible for all institutions. Similar limitations apply to the EuroSCORE II, which was derived from a population-based cohort in Europe.⁴ Given these limitations, we developed a more parsimonious model using readily available, linked clinical and administrative data sets in Ontario, Canada, to efficiently and accurately calculate risk-adjusted 30-day mortality rates for the purpose of province-wide quality improvement after CABG, AVR and combined CABG + AVR.