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Predicting sudden cardiac death in adults with congenital heart disease

Jose M Oliver1,2, Pastora Gallego3, Ana Elvira Gonzalez2

  • 1Adult Congenital Heart Disease Unit, Department of Cardiology, Hospital General Universitario Gregorio Marañon, Instituto de Investigación Sanitaria Gregorio Marañón, Facultad de Medicina, Universidad Complutense de Madrid and CIBERCV, Madrid, Spain.

Heart (British Cardiac Society)
|June 18, 2020
PubMed

Insights

A new logistic regression model accurately predicts sudden cardiac death (SCD) and sudden cardiac arrest (SCA) in adults with congenital heart disease (ACHD). This model, using lesion-specific risk and clinical factors, improves risk stratification for primary prevention strategies.

Area of Science:

  • Cardiology
  • Medical Informatics

Background:

  • Adults with congenital heart disease (ACHD) have an increased risk of sudden cardiac death (SCD) and sudden cardiac arrest (SCA).
  • Accurate risk prediction is crucial for guiding primary prevention strategies in this population.

Purpose of the Study:

  • To develop, calibrate, test, and validate a logistic regression model for predicting SCD and SCA risk in ACHD patients.
  • To incorporate baseline lesion-specific risk stratification and individual characteristics into the predictive model.

Main Methods:

  • Combined data from a single-center cohort (3311 ACHD patients) and a multicenter case-control group (207 cases, 2287 controls).
  • Derived a risk model using logistic regression on development and validation datasets.
  • Stratified patients into four lesion-specific risk clusters based on cumulative incidence.

Main Results:

  • Identified key predictors: lesion-specific cluster, young age, male sex, syncope, ischemic heart disease, ventricular arrhythmias, QRS duration, and ventricular dysfunction.
  • The model demonstrated high accuracy in discrimination (C-index 0.91) and calibration in the validation dataset.
  • Achieved a 29% increase in sensitivity for SCD/SCA prediction compared to current guidelines, with minimal specificity change.

Conclusions:

  • A lesion-specific risk stratification combined with clinical variables significantly improves SCD/SCA risk prediction in ACHD.
  • The developed model offers a more accurate approach to identify high-risk individuals for targeted primary prevention.
Abstract

Keywords:
cardiac arrestcongenital heart diseasestudy designsudden cardiac deathventricular tachycardia

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