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Published on: August 8, 2022
Improving sudden cardiac death risk stratification in hypertrophic cardiomyopathy using established clinical
Ali Amr1,2, Jan Koelemen1,2, Christoph Reich1,2
1Institute for Cardiomyopathies & Center for Cardiogenetics, Department of Medicine III, University of Heidelberg, Im Neuenheimer Feld 410, 69120, Heidelberg, Germany.
Insights
This study validates sudden cardiac death (SCD) risk models in hypertrophic cardiomyopathy (HCM) patients. Integrating genetic information improves risk prediction and aids clinical decisions, especially for borderline cases.
Area of Science:
- Cardiology
- Genetics
- Preventive Medicine
Background:
- European and US cardiac societies have distinct sudden cardiac death (SCD) risk models for hypertrophic cardiomyopathy (HCM).
- Current risk stratification methods require validation in diverse patient cohorts.
- Genotype information may enhance SCD risk prediction in HCM.
Purpose of the Study:
- To validate existing SCD risk prediction models in a German HCM cohort.
- To assess the impact of integrating genotype information into SCD risk stratification.
- To improve clinical decision-making for preventing SCD in HCM patients.
Main Methods:
- Enrolled 283 adult HCM patients without prior SCD or arrhythmic events.
- Analyzed 5-year SCD risk estimates using ESC and AHA/ACC guidelines.
- Integrated genetic findings into multivariate Cox proportional hazards models.
Main Results:
- A disease-causing variant was identified in 49% of patients.
- The genotype-integrated model showed improved AUC (0.76) and sensitivity (0.86) compared to ESC (AUC 0.74) and AHA/ACC (AUC 0.70) models.
- The modified genotype model reduced the number-needed-to-treat (NNT) for ICD implantation from 13 (ESC) to 9.
Conclusions:
- Current SCD risk models demonstrate acceptable performance but may miss high-risk individuals.
- Integrating genetic findings into risk stratification is feasible and enhances decision-making, particularly for borderline risk groups.
- Further refinement of risk models is needed to identify all high-risk HCM patients.
Background And Aims:
The cardiac societies of Europe and the United States have established different risk models for preventing sudden cardiac death (SCD) in hypertrophic cardiomyopathy (HCM). The aim of this study is to validate current SCD risk prediction methods in a German HCM cohort and to improve them by the addition of genotype information.
Methods:
HCM patients without prior SCD or equivalent arrhythmic events ≥ 18 years of age were enrolled in an expert cardiomyopathy center in Germany. The primary endpoint was defined as SCD/-equivalent within 5 years of baseline evaluation. 5-year SCD-risk estimates and recommendations for ICD implantations, as defined by the ESC and AHA/ACC guidelines, were analyzed. Multivariate cox proportional hazards analyses were integrated with genetic findings as additive SCD risk.
Results:
283 patients were included and followed for in median 5.77 years (2.92; 8.85). A disease-causing variant was found in 138 (49%) patients. 14 (5%) patients reached the SCD endpoint (5-year incidence 4.9%). Kaplan-Meier survival analysis shows significantly lower overall SCD event-free survival for patients with an identified disease-causing variant (p < 0.05). The ESC HCM Risk-SCD model showed an area-under-the-curve (AUC) of 0.74 (95% CI 0.68-0.79; p < 0.0001) with a sensitivity of 0.29 (95% CI 0.08-0.58) and specificity of 0.83 (95% CI 0.78-0.88) for a risk estimate ≥ 6%/5-years. By comparison, the AHA/ACC HCM SCD risk stratification model showed an AUC of 0.70 (95% CI 0.65-0.76; p = 0.003) with a sensitivity of 0.93 (95% CI, 0.66-0.998) and specificity of 0.28 (95% CI 0.23-0.34) at the respective cut-off. The modified SCD Risk Score with genetic information yielded an AUC of 0.76 (95% CI 0.71-0.81; p < 0.0001) with a sensitivity of 0.86 (95% CI 0.57-0.98) and specificity of 0.69 (95% CI 0.63-0.74). The number-needed-to-treat (NNT) to prevent 1 SCD event by prophylactic ICD-implantation is 13 for the ESC model, 28 for AHA/ACC and 9 for the modified Genotype-model.
Conclusion:
This study confirms the performance of current risk models in clinical decision making. The integration of genetic findings into current SCD risk stratification methods seem feasible and can add in decision making, especially in borderline risk-groups. A subgroup of patients with high SCD risk remains unidentified by current risk scores.
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