Understanding Transient Left Ventricular Ejection Fraction Reduction During Atrial Fibrillation With Artificial Intelligence.

Background: Atrial fibrillation (AF) can cause a reduction in left ventricular ejection fraction (LVEF) that resolves rapidly upon restoration of sinus rhythm. We used artificial intelligence to understand (1) how often transient LVEF reduction during AF is from mismeasurement due to AF's beat-to-beat variability and (2) whether true transient AF-LVEF reduction has prognostic significance.

Methods: In this observational study, we analyzed all patients at a large academic center with a transthoracic echocardiogram in AF and subsequent transthoracic echocardiogram in sinus rhythm within 90 days. We classified patients by their clinically reported LVEFs: no AF-LVEF reduction, transient AF-LVEF reduction that recovered after conversion to sinus rhythm, or persistent AF-LVEF reduction that did not recover. We evaluated how automated multicycle AF-LVEF measurement using a validated artificial intelligence algorithm affected AF-LVEF and reclassified patients. We used Fine-Gray hazard modeling to analyze 1-year heart failure hospitalization risk.

Results: In 810 patients (mean age 74.1 years, 34.3% female), 459 (56.7%) had no reduced AF-LVEF, 71 (8.8%) had transient AF-LVEF reduction, and 280 (34.6%) had persistent AF-LVEF reduction. In the group with transient AF-LVEF reduction, LVEF increased by 19.5% (95% CI, 12.0%-22.1%) upon conversion to sinus rhythm. AI reassessment increased AF-LVEF by 8.2% (95% CI, 6.0%-10.4%), reclassifying 20 (28.2%) patients as no longer having reduced AF-LVEF. The group with transient AF-LVEF reduction, as determined by AI, had significantly higher 1-year heart failure hospitalization risk (hazard ratio, 2.28 [95% CI, 1.23-4.21], P=0.003).

Conclusions: Artificial intelligence may decrease misdiagnosis of reduced LVEF during AF and more accurately identify true transient AF-LVEF reduction, a potentially high-risk phenotype.