Significance of Intraoperative Medication Data and Predictive Model Selection for Predicting Postoperative First-Time Atrial Fibrillation.

Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia in clinical practice and has a well-established association with coronary artery bypass graft (CABG) surgery. Being able to predict post-operative AF (POAF) may improve surgical outcomes. This study retrospectively assembled a large cohort of 3,807 first-time CABG patients with no prior AF to study factors that contribute to occurrence of POAF, in addition to testing models that may predict its incidence. Several clinical features with established relevance to POAF were extracted from the EHR, along with a record of medications administered intra-operatively. Tests of performance with logistic regression, decision tree, and neural network predictive models showed slight improvements when incorporating medication information. Analysis of the clinical and medications data indicate that there may be effects contributing to POAF incidence captured in the medication administration records. Our results show that improved predictive performance is achievable by incorporating a record of medications administered intra-operatively.