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OBJECTIVE: To develop a validated clinical prognostic model to determine the risk of atrial fibrillation after cardiac surgery as part of the PARADISE project (NIHR131227). METHODS: Prospective cohort study with linked electronic health records from a cohort of 5.6 million people in the United Kingdom Clinical Practice Research Datalink from 1998 to 2016. For model development, we considered a priori candidate predictors including demographics, medical history, medications, and clinical biomarkers. We evaluated associations between covariates and the AF incidence at the end of follow-up using logistic regression with the least absolute shrinkage and selection operator. The model was validated internally with the bootstrap method; subsequent performance was examined by discrimination quantified with the c-statistic and calibration assessed by calibration plots. The study follows TRIPOD guidelines. RESULTS: Between 1998 and 2016, 33,464 patients received cardiac surgery among the 5,601,803 eligible individuals. The final model included 13-predictors at baseline: age, year of index surgery, elevated CHA2DS2-VASc score, congestive heart failure, hypertension, acute coronary syndromes, mitral valve disease, ventricular tachycardia, valve surgery, receiving two combined procedures (e.g., valve replacement + coronary artery bypass grafting), or three combined procedures in the index procedure, statin use, and ethnicity other than white or black (statins and ethnicity were protective). This model had an optimism-corrected C-statistic of 0.68 both for the derivation and validation cohort. Calibration was good. CONCLUSIONS: We developed a model to identify a group of individuals at high risk of AF and adverse outcomes who could benefit from long-term arrhythmia monitoring, risk factor management, rhythm control and/or thromboprophylaxis.

Original publication




Journal article


Clin Res Cardiol

Publication Date



Atrial fibrillation, Atrial fibrillation after cardiac surgery, Cardiac surgery, Electronic health records, Epidemiology, Prognostic model, Risk prediction, Risk score, United Kingdom