Abstract
BackgroundQuality 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.MethodsThe 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.ResultsThe 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.ConclusionsDevelopment 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.
Highlights
Quality improvement initiatives in cardiac surgery largely rely on risk prediction models
Quality improvement initiatives (QI), a cornerstone of cardiac surgery, have largely relied on predictive models to advance the quality of care cardiac surgical patients receive
For the last two decades, coronary artery bypass grafting (CABG) has dominated clinical practice in cardiac surgery, and the majority of quality improvement initiatives have focused on surgical outcomes following isolated CABG surgery [1,2,3,4]
Summary
Quality improvement initiatives in cardiac surgery largely rely on risk prediction models. Most often, these models include isolated populations and describe isolated end-points. CABG cases in cardiac surgery [5] and existing predictive models for isolated CABG may not accurately reflect current practice profiles. In order to maintain continued success in quality improvement, it is important to delineate risk profiles for a group of mixed procedures, including CABG, valve, and valve + CABG, that characterize current clinical practice. Mixed procedural models may be confounded or biased by different pathophysiological and risk profiles, they improve sample size [5] and increase their relevance to current surgical practice
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