A validated model for sudden cardiac death risk prediction in pediatric hypertrophic cardiomyopathy

Anastasia Miron, Myriam Lafreniere-Roula, Chun Po Steve Fan, Katey R. Armstrong, Andreea Dragulescu, Tanya Papaz, Cedric Manlhiot, Beth Kaufman, Ryan J. Butts, Letizia Gardin, Elizabeth A. Stephenson, Taylor S. Howard, Pete F. Aziz, Seshadri Balaji, Virginie Beauséjour Ladouceur, Lee N. Benson, Steven D. Colan, Justin Godown, Heather T. Henderson, Jodie InglesAamir Jeewa, John L. Jefferies, Ashwin K. Lal, Jacob Mathew, Emilie Jean-St-Michel, Michelle Michels, Stephanie J. Nakano, Iacopo Olivotto, John J. Parent, Alexandre C. Pereira, Christopher Semsarian, Robert D. Whitehill, Samuel G. Wittekind, Mark W. Russell, Jennifer Conway, Marc E. Richmond, Chet Villa, Robert G. Weintraub, Joseph W. Rossano, Paul F. Kantor, Carolyn Y. Ho, Seema Mital

Research output: Contribution to journalArticlepeer-review

100 Scopus citations


Background: Hypertrophic cardiomyopathy is the leading cause of sudden cardiac death (SCD) in children and young adults. Our objective was to develop and validate a SCD risk prediction model in pediatric hypertrophic cardiomyopathy to guide SCD prevention strategies. Methods: In an international multicenter observational cohort study, phenotype-positive patients with isolated hypertrophic cardiomyopathy <18 years of age at diagnosis were eligible. The primary outcome variable was the time from diagnosis to a composite of SCD events at 5-year follow-up: SCD, resuscitated sudden cardiac arrest, and aborted SCD, that is, appropriate shock following primary prevention implantable cardioverter defibrillators. Competing risk models with cause-specific hazard regression were used to identify and quantify clinical and genetic factors associated with SCD. The cause-specific regression model was implemented using boosting, and tuned with 10 repeated 4-fold cross-validations. The final model was fitted using all data with the tuned hyperparameter value that maximizes the c-statistic, and its performance was characterized by using the c-statistic for competing risk models. The final model was validated in an independent external cohort (SHaRe [Sarcomeric Human Cardiomyopathy Registry], n=285). Results: Overall, 572 patients met eligibility criteria with 2855 patient-years of follow-up. The 5-year cumulative proportion of SCD events was 9.1% (14 SCD, 25 resuscitated sudden cardiac arrests, and 14 aborted SCD). Risk predictors included age at diagnosis, documented nonsustained ventricular tachycardia, unexplained syncope, septal diameter z-score, left ventricular posterior wall diameter z score, left atrial diameter z score, peak left ventricular outflow tract gradient, and presence of a pathogenic variant. Unlike in adults, left ventricular outflow tract gradient had an inverse association, and family history of SCD had no association with SCD. Clinical and clinical/genetic models were developed to predict 5-year freedom from SCD. Both models adequately discriminated between patients with and without SCD events with a c-statistic of 0.75 and 0.76, respectively, and demonstrated good agreement between predicted and observed events in the primary and validation cohorts (validation c-statistic 0.71 and 0.72, respectively). Conclusion: Our study provides a validated SCD risk prediction model with >70% prediction accuracy and incorporates risk factors that are unique to pediatric hypertrophic cardiomyopathy. An individualized risk prediction model has the potential to improve the application of clinical practice guidelines and shared decision making for implantable cardioverter defibrillator insertion. Registration: URL: https://www.clinicaltrials.gov; Unique identifier: NCT0403679.

Original languageEnglish (US)
Pages (from-to)217-229
Number of pages13
Issue number3
StatePublished - Jul 21 2020


  • cardiomyopathies
  • cardiomyopathy, hypertrophic
  • death, sudden, heart
  • defibrillators, implantable
  • hypertrophy
  • pediatrics

ASJC Scopus subject areas

  • Cardiology and Cardiovascular Medicine
  • Physiology (medical)


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