Identification of patients at risk of sudden cardiac death in congenital heart disease. The prospective study on implantable cardIoverter defibrillator therapy and sudden cardiac death in adults with congenital heart disease: Prevention-ACHD.
Vehmeijer JT, Koyak Z, Leerink JM, Zwinderman AH, Harris L, Peinado R, Oechslin EN, Robbers-Visser D, Groenink M, Boekholdt SM, de Winter RJ, Oliver JM, Bouma BJ, Budts W, Van Gelder IC, Mulder BJM, de Groot JR.
Heart Rhythm. 2021 Jan 16:S1547-5271(21)00029-1. doi: 10.1016/j.hrthm.2021.01.009. Online ahead of print.
Take Home Points:
- The PREVENTION-ACHD risk model appears useful for identification of VT/VF or SCD risk among a heterogenous group of adults with congenital heart disease.
- Prospective validation suggests a sensitivity of 0.5 and specificity of 0.93 for prediction of VT/VF or SCD
- The proposed risk model outperforms existing classification schemes and may be useful for clinical practice and future guideline-based ICD indications.
Commentary by Dr. Jeremy Moore (Los Angeles) Congenital and Pediatric Cardiac EP section editor: This recent manuscript out of Amsterdam serves as prospective validation work to a previously proposed risk-stratification model from 2019 that is applicable to a broad group of adults with congenital heart disease (ACHD). The investigators had originally conducted a case-control study across several institutions involving the CONCOR (11,535 patients) registry, the Toronto Congenital Cardiac Centre for Adults, and the University Hospital Leuven (refer to Koyak et al. Circulation 2012) in order to identify predictors of sudden cardiac death (SCD) across multiple forms of ACHD. The proposed risk factors were subsequently externally validated in a retrospective group of 3,197 patients followed at the La Paz University Hospital in Spain that was published later that year (Gallego et al. Am J Cardiol 2012).
Using the baseline hazards derived from the large CONCOR registry, the authors were able to calculate annual rates of SCD using the results of the original multicenter case-control study. The predictors were modified slightly, as QRS duration and QT dispersion were dichotomized (QRSd>120 ms and QTd>70 ms) thus allowing for the inclusion of two additional risk factors in the model, specifically a history of coronary artery disease and clinical heart failure (refer to Vehmeijer et al. Neth Heart J 2019). The proposed risk model that was first described in 2019 is shown below.
The present manuscript examines the performance of the previously proposed model in a prospective validation cohort derived from 783 consecutive ACHD followed for 2 years at the Amsterdam University beginning in 2019. Patients were grouped as high (>3%) or low (<3%) annual risk for SCD based on the risk model. The primary outcome of this study was the combined endpoint of SCD or VT/VF, including appropriate ICD therapy. The secondary outcome was SCD alone.
The authors found that actual KM estimates for a primary outcome after 2 years was 7% for high risk patients vs 0.6% for low risk patients (HR 12.5, 95%CI 3.1-50.9; p<0.001). Similarly for the secondary outcome of SCD alone, KM estimates were 3.5% vs 0.3% (HR 12.4, 95%CI 1.8-88.1; p=0.01).
More importantly, the PREVENTION-ACHD risk score showed an overall sensitivity of 0.5 and specificity of 0.93 for SCD or VT/VF at 2 years and a sensitivity of 0.5 and specificity of 0.92 for SCD alone. This compared favorably with the PACES/HRS Expert Consensus Statement using the patient population, which showed a sensitivity of 0.25 and specificity of 0.98 for SCD or VT/VF at 2 years and a sensitivity of 0.0 and specificity of 0.98 for SCD alone.
This present work attempts to improve risk stratification for ACHD and as such, improves upon the existing classification schemes. Although patients considered to be high risk (i.e. annual risk of SCD or VT/VF >3%) were only identified with a sensitivity of 50% in the present cohort, this represents a significant improvement over the PACES/HRS Expert Consensus Statement. Meanwhile, this still equates to “missing” 50% of high-risk cases. Further work is still needed in order to accurately identify patients in whom primary prevention ICD placement is needed.