Background: Hypertrophic cardiomyopathy (HCM) 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 HCM to guide SCD prevention strategies. Methods: In an international multi-center observational cohort study, phenotype-positive patients with isolated HCM <18 years 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 (SCA), and aborted SCD, i.e. appropriate shock following primary prevention ICD. 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 ten repeated four-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 using c-statistic for competing risk models. The final model was validated in an independent external cohort (SHaRe, 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 SCA, 14 aborted SCD). Risk predictors included age at diagnosis, documented non-sustained ventricular tachycardia, unexplained syncope, septal diameter z-score, LV posterior wall diameter z-score, LA diameter z-score, peak LV outflow tract (LVOT) gradient, and presence of a pathogenic variant. Unlike adults, LVOT 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 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). Conclusions:Our study provides a validated SCD risk prediction model with over 70% prediction accuracy and incorporates risk factors that are unique to pediatric HCM. An individualized risk prediction model has the potential to improve the application of clinical practice guidelines and shared decision-making for ICD insertion. Clinical Trial Registration:URL: https://clinicaltrials.gov Unique Identifier: NCT04036799.

A Validated Model for Sudden Cardiac Death Risk Prediction in Pediatric Hypertrophic Cardiomyopathy / Miron, Anastasia; Lafreniere-Roula, Myriam; Fan, Chun-Po Steve; Armstrong, Katey R; Dragulescu, Andreea; Papaz, Tanya; Manlhiot, Cedric; Kaufman, Beth; Butts, Ryan J; Gardin, Letizia; Stephenson, Elizabeth A; Howard, Taylor S; Aziz, Peter F; Balaji, Seshadri; Beauséjour Ladouceur, Virginie; Benson, Lee N; Colan, Steven D; Godown, Justin; Henderson, Heather T; Ingles, Jodie; Jeewa, Aamir; Jefferies, John L; Lal, Ashwin K; Mathew, Jacob; Jean-St-Michel, Emilie; Michels, Michelle; Nakano, Stephanie J; Olivotto, Iacopo; Parent, John J; Pereira, Alexandre C; Semsarian, Christopher; Whitehill, Robert D; Wittekind, Samuel G; Russell, Mark W; Conway, Jennifer; Richmond, Marc E; Villa, Chet; Weintraub, Robert G; Rossano, Joseph W; Kantor, Paul F; Ho, Carolyn Y; Mital, Seema. - In: CIRCULATION. - ISSN 0009-7322. - ELETTRONICO. - (2020), pp. 0-0. [10.1161/CIRCULATIONAHA.120.047235]

A Validated Model for Sudden Cardiac Death Risk Prediction in Pediatric Hypertrophic Cardiomyopathy

Olivotto, Iacopo;
2020

Abstract

Background: Hypertrophic cardiomyopathy (HCM) 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 HCM to guide SCD prevention strategies. Methods: In an international multi-center observational cohort study, phenotype-positive patients with isolated HCM <18 years 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 (SCA), and aborted SCD, i.e. appropriate shock following primary prevention ICD. 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 ten repeated four-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 using c-statistic for competing risk models. The final model was validated in an independent external cohort (SHaRe, 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 SCA, 14 aborted SCD). Risk predictors included age at diagnosis, documented non-sustained ventricular tachycardia, unexplained syncope, septal diameter z-score, LV posterior wall diameter z-score, LA diameter z-score, peak LV outflow tract (LVOT) gradient, and presence of a pathogenic variant. Unlike adults, LVOT 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 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). Conclusions:Our study provides a validated SCD risk prediction model with over 70% prediction accuracy and incorporates risk factors that are unique to pediatric HCM. An individualized risk prediction model has the potential to improve the application of clinical practice guidelines and shared decision-making for ICD insertion. Clinical Trial Registration:URL: https://clinicaltrials.gov Unique Identifier: NCT04036799.
2020
0
0
Miron, Anastasia; Lafreniere-Roula, Myriam; Fan, Chun-Po Steve; Armstrong, Katey R; Dragulescu, Andreea; Papaz, Tanya; Manlhiot, Cedric; Kaufman, Beth; Butts, Ryan J; Gardin, Letizia; Stephenson, Elizabeth A; Howard, Taylor S; Aziz, Peter F; Balaji, Seshadri; Beauséjour Ladouceur, Virginie; Benson, Lee N; Colan, Steven D; Godown, Justin; Henderson, Heather T; Ingles, Jodie; Jeewa, Aamir; Jefferies, John L; Lal, Ashwin K; Mathew, Jacob; Jean-St-Michel, Emilie; Michels, Michelle; Nakano, Stephanie J; Olivotto, Iacopo; Parent, John J; Pereira, Alexandre C; Semsarian, Christopher; Whitehill, Robert D; Wittekind, Samuel G; Russell, Mark W; Conway, Jennifer; Richmond, Marc E; Villa, Chet; Weintraub, Robert G; Rossano, Joseph W; Kantor, Paul F; Ho, Carolyn Y; Mital, Seema
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1194230
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