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

AIMS: 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. METHODS AND RESULTS: 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), log(2) 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). CONCLUSION: 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.