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Sudden cardiac death risk in hypertrophic cardiomyopathy: comparison between echocardiography and magnetic resonance imaging.

ABSTRACT: In hypertrophic cardiomyopathy (HCM) patients, left ventricular (LV) maximal wall thickness (MWT) is one of the most important factors determining sudden cardiac death (SCD) risk. In a large unselected sample of HCM patients, we aimed to simulate what changes would occur in the calculated SCD risk according to the European HCM Risk-SCD calculator when MWT measured using echocardiography was changed to MWT measured using MRI. All consecutive patients with HCM who underwent cardiac MRI were included. MWT measured with echocardiography and MRI were compared, and 5-year SCD risk according to the HCM Risk-SCD calculator was computed using four different models. The final population included 673 patients [389 (57.8%) males, median age 50 years, interquartile range (36-60)]. The median MWT was lower measured by echocardiography than by MRI [20 (17-24) mm vs 21 (18-24) mm; p < 0.0001]. There was agreement between echocardiography and MRI in the measurement of maximal LV wall thickness in 96 patients (14.3%). The largest differences between echo and MRI were - 13 mm and + 9 mm. The differences in MWT by echocardiography and MRI translated to a maximal difference of 8.33% in the absolute 5-year risk of SCD, i.e., the echocardiography-based risk was 8.33% lower than the MRI-based estimates. Interestingly, 13.7% of patients would have been reclassified into different SCD risk categories if MRI had been used to measure MWT instead of echocardiography. In conclusion, although there was high general intermodality agreement between echocardiography and MRI in the MWT measurements, the differences in MWT translated to significant differences in the 5-year risk of SCD.

SUBMITTER: Spiewak M

PROVIDER: S-EPMC8009882 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:ObjectivesHypertrophic cardiomyopathy (HCM) often requires repeated enhanced cardiac magnetic resonance (CMR) imaging to detect fibrosis. We aimed to develop a practical model based on cine imaging to help identify patients with high risk of fibrosis and screen out patients without fibrosis to avoid unnecessary injection of contrast.MethodsA total of 273 patients with HCM were divided into training and test sets at a ratio of 7:3. Logistic regression analysis was used to find predictive image features to construct CMR model. Radiomic features were derived from the maximal wall thickness (MWT) slice and entire left ventricular (LV) myocardium. Extreme gradient boosting was used to build radiomic models. Integrated models were established by fusing image features and radiomic models. The model performance was validated in the test set and assessed by ROC and calibration curve and decision curve analysis (DCA).ResultsWe established five prediction models, including CMR, R1 (based on the MWT slice), R2 (based on the entire LV myocardium), and two integrated models (ICMR+R1 and ICMR+R2). In the test set, ICMR+R2 model had an excellent AUC value (0.898), diagnostic accuracy (89.02%), sensitivity (92.54%), and F1 score (93.23%) in identifying patients with positive late gadolinium enhancement. The calibration plots and DCA indicated that ICMR+R2 model was well-calibrated and presented a better net benefit than other models.ConclusionsA predictive model that fused image and radiomic features from the entire LV myocardium had good diagnostic performance, robustness, and clinical utility.Key points• Hypertrophic cardiomyopathy is prone to fibrosis, requiring patients to undergo repeated enhanced cardiac magnetic resonance imaging to detect fibrosis over their lifetime follow-up. • A predictive model based on the entire left ventricular myocardium outperformed a model based on a slice of the maximal wall thickness. • A predictive model that fused image and radiomic features from the entire left ventricular myocardium had excellent diagnostic performance, robustness, and clinical utility.

Project description:BackgroundHypertrophic 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.MethodsIn 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).ResultsOverall, 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).ConclusionOur 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.

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Sudden cardiac death risk in hypertrophic cardiomyopathy: comparison between echocardiography and magnetic resonance imaging.

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