Unraveling the impact of time-dependent perioperative variables on 30 day readmission following coronary artery bypass surgery

Abstract: OBJECTIVES Readmission within 30 days of discharge following Coronary Artery Bypass Grafting (CABG) is a measure of quality and a driver of cost in healthcare. Traditional predictive models use time-independent variables. We developed a new model to predict time to readmission after CABG using both time-independent and time-dependent pre- and perioperative data. METHODS Adults surviving to discharge after isolated CABG at a multi-hospital academic health system from January 2017 to September 2018 were included in this study. Two distinct data sources were used: the institutional cardiac surgical database, and the clinical data warehouse which provided more granular data points for each patient. Patients were divided into training and validation sets in 80:20 ratio. We evaluated 82 potential risk factors using Cox survival regression and machine learning techniques. The area under the receiver operating characteristic (AUROC) curve was used to estimate model predictive accuracy. RESULTS We trained the model with 21 variables that scored a p-value of CONCLUSIONS Time-dependent perioperative variables in an isolated CABG cohort provided better predictive ability to a readmission model. This study was unique in the inclusion of time-dependent covariates in the predictive model for readmission after discharge following CABG. [Word count: 245]