Tuning and external validation of an adult congenital heart disease risk prediction model

Adequate risk prediction can optimize the clinical management in adult congenital heart disease (ACHD). We aimed to update and subsequently validate a previously developed ACHD risk prediction model.

A prediction model was developed in a prospective cohort study including 602 moderately or severely complex ACHD patients, enrolled as outpatients at a tertiary centre in the Netherlands (2011–2013). Multivariable Cox regression was used to develop a model for predicting the 1-year risks of death, heart failure (HF), or arrhythmia (primary endpoint). The Boston ACHD Biobank study, a prospectively enrolled cohort (n = 749) of outpatients who visited a referral centre in Boston (2012–2017), was used for external validation. The primary endpoint occurred in 153 (26%) and 191 (28%) patients in the derivation and validation cohorts over median follow-up of 5.6 and 2.3 years, respectively. The final model included 5 out of 14 pre-specified predictors with the following hazard ratios; New York Heart Association class ≥II: 1.92 [95% confidence interval (CI) 1.28–2.90], cardiac medication 2.52 (95% CI 1.72–3.69), ≥1 reintervention after initial repair: 1.56 (95% CI 1.09–2.22), body mass index: 1.04 (95% CI 1.01–1.07), log₂ N-terminal pro B-type natriuretic peptide (pmol/L): 1.48 (95% CI 1.32–1.65). At external validation, the model showed good discrimination (C-statistic 0.79, 95% CI 0.74–0.83) and excellent calibration (calibration-in-the-large = −0.002; calibration slope = 0.99).

These data support the validity and applicability of a parsimonious ACHD risk model based on five readily available clinical variables to accurately predict the 1-year risk of death, HF, or arrhythmia. This risk tool may help guide appropriate care for moderately or severely complex ACHD.