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Derivation and validation of a clinical model to predict death or cardiac hospitalizations while on the cardiac surgery waitlist

Sun LY, Eddeen AB, Wijeysundera HC, Mamas MA, Tam DY, Mesana TG. CMAJ. 2021; 193(34):E1333-40. Epub 2021 Aug 30. DOI: https://doi.org/10.1503/cmaj.210170


Background — Waitlist management is a global challenge. For patients with severe cardiovascular diseases awaiting cardiac surgery, prolonged wait times are associated with unplanned hospitalizations. To facilitate evidence-based resource allocation, we derived and validated a clinical risk model to predict the composite outcome of death and cardiac hospitalization of patients on the waitlist for cardiac surgery.

Methods — We used the CorHealth Ontario Registry and linked ICES health care administrative databases, which have information on all Ontario residents. We included patients 18 years or older who waited at home for coronary artery bypass grafting, valvular or thoracic aorta surgeries between 2008 and 2019. The primary outcome was death or an unplanned cardiac hospitalizaton, defined as nonelective admission for heart failure, myocardial infarction, unstable angina or endocarditis. We randomly divided two-thirds of these patients into derivation and one-third into validation data sets. We derived the model using a multivariable Cox proportional hazard model with backward stepwise variable selection.

Results — Among 62 375 patients, 41 729 patients were part of the derivation data set and 20 583 were part of the validation data set. Of the total, 3033 (4.9%) died or had an unplanned cardiac hospitalization while waiting for surgery. The area under the curve of our model at 15, 30, 60 and 89 days was 0.85, 0.82, 0.81 and 0.80, respectively, in the derivation cohort and 0.83, 0.80, 0.78 and 0.78, respctively, in the validation cohort. The model calibrated well at all time points.

Interpretation — We derived and validated a clinical risk model that provides accurate prediction of the risk of death and unplanned cardiac hospitalization for patients on the cardiac surgery waitlist. Our model could be used for quality benchmarking and data-driven decision support for managing access to cardiac surgery.

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