PICM is not uncommon in patients receiving PPM for CHB with preserved LVEF and is strongly associated with RV pacing burden >20%. CRT response rate is high in PICM, but is perhaps underutilized.

Background-Cardiac resynchronization therapy (CRT) has significant non-response rates. We assessed whether machine learning could predict CRT response beyond current guidelines. Methods-We analyzed CRT patients from Cleveland Clinic and Johns Hopkins. A training cohort was created from all Johns Hopkins patients and an equal number of randomly sampled Cleveland Clinic patients. All remaining patients comprised the testing cohort. Response was defined as ≥10% increase in left ventricular (LV) ejection fraction. Machine learning models were developed to predict CRT response using different combinations of classification algorithms and clinical variable sets on the training cohort. The model with the highest area under curve (AUC) was evaluated on the testing cohort. Probability of response was used to predict survival free from a composite endpoint of death, heart transplant, or placement of LV assist device. Predictions were compared to current guidelines.

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QRS morphology is a more important baseline electrocardiographic determinant of CRT response than QRSd.

After adjusting for potential confounders, QRS narrowing, indexed to baseline QRS duration, is associated with enhanced reverse ventricular remodeling following CRT.