Tuning and External Validation of an Adult Congenital Heart Disease Risk Prediction Model.

Aim 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, 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 HR's; NYHA class ≥II: 1.92 (95% CI 1.28-2.90), cardiac medication 2.52 (95% CI 1.72-3.69), ≥1 re-intervention after initial repair: 1.56 (95% CI 1.09-2.22), body mass index: 1.04 (95% CI 1.01-1.07), log2 NT-proBNP (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 5 readily available clinical variables to accurately predict the 1-year risk of death, heart failure or arrhythmia. This risk tool may help guide appropriate care for moderately or severely complex ACHD.