Atrial Fibrillation Prediction Model Following Aortic Valve Replacement Surgery

Area of Science:

• Cardiology
• Cardiac Surgery
• Medical Informatics

Background:

• Postoperative atrial fibrillation (POAF) is a frequent complication after cardiac surgery.
• POAF increases patient morbidity and healthcare expenses.
• Identifying POAF predictors in isolated aortic valve replacement (AVR) is crucial.

Purpose of the Study:

• To determine the incidence of new-onset POAF in patients undergoing isolated AVR.
• To develop a multivariate predictive model for POAF in this patient cohort.

Main Methods:

• Retrospective analysis of 1108 patients undergoing isolated AVR (January 2010 - December 2022).
• Exclusion criteria: age <18, prior AF, previous cardiac surgery, concomitant procedures.
• Multivariate logistic regression with backward elimination for predictive modeling.

Main Results:

• The incidence of POAF was 27% (297/1108 patients).
• Predictors identified: advanced age, larger valve size, prolonged cardiopulmonary bypass time, delayed sternal closure, extended ventilation, and longer ICU stay.
• The predictive model showed fair discrimination (AUC = 0.678) and good fit (Hosmer-Lemeshow p = 0.655).

Conclusions:

• A validated POAF prediction model can provide personalized risk stratification for AVR patients.
• Tailored management strategies based on risk estimates may improve patient outcomes.
• Optimizing patient care through predictive modeling can potentially reduce healthcare costs.