Predicting decline in left ventricular function after new-onset left bundle branch block.

BACKGROUND

Left bundle branch block (LBBB) has been associated with an increased risk of incident left ventricular systolic dysfunction. Identifying high-risk patients early is challenging but important for timely management.

OBJECTIVE

This study aimed to identify predictors of significant left ventricular ejection fraction (LVEF) decline in patients with LBBB using clinical, echocardiographic, and electrocardiographic (ECG) data.

METHODS

A retrospective cohort of 769 patients with newly diagnosed LBBB and preserved LVEF (≥50%) was analyzed. Univariable and multivariable Cox proportional hazards regression analyses were used to identify predictors of ≥20% LVEF decline on follow-up echocardiography as the primary outcome. A risk prediction model was constructed using pooled estimates.

RESULTS

Over a median follow-up of 4.9 years (interquartile range 2.3-8.4), 242 patients (31.5%) experienced an LVEF decline of ≥20%. In multivariable Cox regression, 2 artificial intelligence-derived ECG scores previously developed to detect LV systolic and diastolic dysfunction were independently associated with LVEF decline (hazard ratio 1.01, per 1% probability increase for low LVEF; P = .005; hazard ratio 1.25, per predicted diastolic dysfunction grade, P = .005, respectively). The final model demonstrated modest discriminative ability with a C-index of 0.615, improving to 0.65 after adjusting for baseline LVEF and further to 0.70 in a landmark analysis of 1-year follow-up.

CONCLUSION

Artificial intelligence-derived ECG markers of LV systolic and diastolic dysfunction independently predicted future LVEF decline in patients with new-onset LBBB. Echocardiographic parameters may also enhance risk stratification. This predictive framework could be used to support monitoring and early intervention strategies in patients with LBBB.