Postoperative atrial fibrillation (POAF) after cardiac surgery: clinical practice review
Orlando R. Suero,
Ahmed K. Ali,
Lauren R. Barron
et al.
Abstract:Postoperative atrial fibrillation (POAF) after cardiac surgery is associated with elevated morbidity and mortality. Although current prediction models have limited efficacy, several perioperative interventions can reduce patients’ risk of POAF. These begin with preoperative medications, including beta-blockers and amiodarone. Moreover, patients should be screened for preexisting atrial fibrillation (AF) so that concomitant surgical ablation and left atrial appendage occlusion can be performed in appropriate ca… Show more
Incidence of postoperative atrial fibrillation (POAF) after cardiac surgery remains high and is associated with adverse patient outcomes. Risk scoring tools have been developed to predict POAF, yet discrimination performance remains moderate. Machine learning (ML) models can achieve better performance but may exhibit performance heterogeneity across race and sex subpopulations. We evaluate 8 risk scoring tools and 6 ML models on a heterogeneous cohort derived from electronic health records. Our results suggest that ML models achieve higher discrimination yet are less fair, especially with respect to race. Our findings highlight the need for building accurate and fair ML models to facilitate consistent and equitable assessment of POAF risk.
Incidence of postoperative atrial fibrillation (POAF) after cardiac surgery remains high and is associated with adverse patient outcomes. Risk scoring tools have been developed to predict POAF, yet discrimination performance remains moderate. Machine learning (ML) models can achieve better performance but may exhibit performance heterogeneity across race and sex subpopulations. We evaluate 8 risk scoring tools and 6 ML models on a heterogeneous cohort derived from electronic health records. Our results suggest that ML models achieve higher discrimination yet are less fair, especially with respect to race. Our findings highlight the need for building accurate and fair ML models to facilitate consistent and equitable assessment of POAF risk.
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