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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.