Predicting complete heart block after alcohol septal ablation for hypertrophic cardiomyopathy using a risk

Ahmadreza Karimianpour1, Justin Heizer2, Davis Leaphart3

  • 1Division of Cardiology, Medical University of South Carolina, Charleston, South Carolina, USA.

Insights

A new clinical tool can predict complete heart block (CHB) after alcohol septal ablation (ASA) for hypertrophic obstructive cardiomyopathy (HOCM). This tool uses easily obtainable factors to identify patients at high risk for CHB.

Area of Science:

  • Cardiology
  • Interventional Cardiology
  • Cardiac Electrophysiology

Background:

  • Alcohol septal ablation (ASA) is a key treatment for drug-refractory, symptomatic hypertrophic obstructive cardiomyopathy (HOCM).
  • ASA carries a significant risk of complete heart block (CHB), ranging from 6.5% to 11%.

Purpose of the Study:

  • To identify predictors of complete heart block (CHB) following alcohol septal ablation (ASA).
  • To develop and validate a clinical prediction tool for stratifying CHB risk in patients undergoing ASA.

Main Methods:

  • A cohort of 636 patients undergoing ASA was analyzed, with 527 used for prediction tool development and 109 for validation.
  • Multivariate analysis identified key predictors, and an 11-point clinical prediction tool was created.
  • The tool underwent internal and external validation using receiver operating characteristic curves and contemporary patient data.

Main Results:

  • Forty-six of 527 patients developed CHB. Key predictors included age ≥50 years, pre-ASA left bundle branch block (LBBB), transient procedural block, post-ASA PR prolongation ≥68 ms, and new bifascicular block.
  • The clinical prediction tool demonstrated strong internal validation (AUC=0.88).
  • External validation showed 98% negative predictive value, 75% sensitivity, and 81% specificity for high-risk patients.

Conclusions:

  • Complete heart block risk after ASA can be predicted using accessible clinical and electrocardiographic factors.
  • A validated clinical prediction tool enables the identification of high-risk patients requiring intensified monitoring and potential therapeutic interventions.
Abstract

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