Predicting sudden cardiac death in adults with congenital heart disease

Abstract

Objectives: To develop, calibrate, test and validate a logistic regression model for accurate risk prediction of sudden cardiac death (SCD) and non-fatal sudden cardiac arrest (SCA) in adults with congenital heart disease (ACHD), based on baseline lesion-specific risk stratification and individual's characteristics, to guide primary prevention strategies.

Methods: We combined data from a single-centre cohort of 3311 consecutive ACHD patients (50% male) at 25-year follow-up with 71 events (53 SCD and 18 non-fatal SCA) and a multicentre case-control group with 207 cases (110 SCD and 97 non-fatal SCA) and 2287 consecutive controls (50% males). Cumulative incidences of events up to 20 years for specific lesions were determined in the prospective cohort. Risk model and its 5-year risk predictions were derived by logistic regression modelling, using separate development (18 centres: 144 cases and 1501 controls) and validation (two centres: 63 cases and 786 controls) datasets.

Results: According to the combined SCD/SCA cumulative 20 years incidence, a lesion-specific stratification into four clusters-very-low (<1%), low (1%-4%), moderate (4%-12%) and high (>12%)-was built. Multivariable predictors were lesion-specific cluster, young age, male sex, unexplained syncope, ischaemic heart disease, non-life threatening ventricular arrhythmias, QRS duration and ventricular systolic dysfunction or hypertrophy. The model very accurately discriminated (C-index 0.91; 95% CI 0.88 to 0.94) and calibrated (p=0.3 for observed vs expected proportions) in the validation dataset. Compared with current guidelines approach, sensitivity increases 29% with less than 1% change in specificity.

Conclusions: Predicting the risk of SCD/SCA in ACHD can be significantly improved using a baseline lesion-specific stratification and simple clinical variables.

Keywords: cardiac arrest; congenital heart disease; study design; sudden cardiac death; ventricular tachycardia.