Development of a predictive model for major adverse cardiac events in a coronary artery bypass and valve population

Quality improvement initiatives in cardiac surgery largely rely on risk prediction models. Most often, these models include isolated populations and describe isolated end-points. However, with the changing clinical profile of the cardiac surgical patients, mixed populations models are required to accurately represent the majority of the surgical population. Also, composite model end-points of morbidity and mortality, better reflect outcomes experienced by patients.

The model development cohort included 4,270 patients who underwent aortic or mitral valve replacement, or mitral valve repair with/without coronary artery bypass grafting, or isolated coronary artery bypass grafting. A composite end-point of infection, stroke, acute renal failure, or death was evaluated. Age, sex, surgical priority, and procedure were forced, a priori, into the model and then stepwise selection of candidate variables was utilized. Model performance was evaluated by concordance statistic, Hosmer-Lemeshow Goodness of Fit, and calibration plots. Bootstrap technique was employed to validate the model.

The model included 16 variables. Several variables were significant such as, emergent surgical priority (OR 4.3; 95% CI 2.9-7.4), CABG + Valve procedure (OR 2.3; 95% CI 1.8-3.0), and frailty (OR 1.7; 95% CI 1.2-2.5), among others. The concordance statistic for the major adverse cardiac events model in a mixed population was 0.764 (95% CL; 0.75-0.79) and had excellent calibration.

Development of predictive models with composite end-points and mixed procedure population can yield robust statistical and clinical validity. As they more accurately reflect current cardiac surgical profile, models such as this, are an essential tool in quality improvement efforts.