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ABSTRACT

Understanding restrictive cardiomyopathy (RCM) in children is limited, and currently, no prognostic model is available for assessing risk stratification in pediatric RCM. The authors elaborated on the clinical and genetic characteristics of pediatric RCM and developed a prediction model to assess the 1-year risk of major adverse cardiovascular events (MACE), aiding in determining the optimal timing for heart transplantation (HTx). This multicenter retrospective cohort study collected data on children with RCM from 14 centers in China, with patient enrollment from January 1, 2013, to December 31, 2022, and follow-up concluding on August 1, 2023. It analyzed clinical and genetic characteristics and followed patients longitudinally for the development of MACE (including cardiac death, HTx, or equivalent events). A logistic regression model was developed to predict MACE one year post-diagnosis, with internal validation using bootstrapping. The model’s performance was evaluated in terms of its discrimination, calibration, and clinical utility. The study included 185 children with a median diagnosis age of 5.4 years (IQR, 3.1–9.4), and 110 (59%) were male. Significant heart failure was the primary clinical feature. TNNI3 mutations were present in 61% of cases, the most common in pediatric RCM. During the follow-up period, 114 patients (62%) experienced MACE, with the median MACE-free survival time for the entire cohort being 2.1 years post-diagnosis (IQR, 0.6–5.4). A prediction model was developed to estimate the one-year risk of MACE using four easily accessible clinical parameters: heart failure classification, brain natriuretic peptide levels, cardiac troponin levels, and a modified score for ST-segment deviation. Internal validation with bootstrapping confirmed accuracy, showing an optimism-adjusted C statistic of 0.78 (95% CI, 0.72–0.85) and a Brier score of 0.17 (95% CI, 0.14–0.21), with a calibration slope of 0.90 (95% CI, 0.63–1.27). Decision curve analysis indicated high net benefit across HTx treatment thresholds from 8.5% to 78.3%. This model utilizes accessible clinical parameters to assess individual risk for MACE in pediatric RCM, potentially improving precision in healthcare strategies and supporting more informed clinical decision-making.

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