Predicting common cardiovascular and non-cardiovascular outcomes afteng common cardiovascular and non-cardiovascularr New-Onset Atrial Fibrillation: An Analysis from a UK Nationwide Primary-Secondary Care Linkage Dataset

Background Atrial fibrillation (AF) increases the risk of major cardiovascular and neurological complications. Utilization of risk schemes for identifying patients at risk for stroke has improved outcomes for patients with AF. Objectives Using linked UK primary and secondary care data, we identified risk predictors and develop risk models for heart failure hospitalisation, myocardial infarction hospitalisation, vascular dementia, sepsis, sudden cardiac death, and all-cause death in the AF population. Methods We included 198,995 patients diagnosed with AF between January 1998 and May 2016. We developed multivariable Cox regression models to predict outcome incidence after AF diagnosis, using 70% of the data for derivation and 30% for validation. Model performance was assessed using Harrell’s C-index. Predictor variables were selected based on their availability in routine clinical practice. Results Among the assessed predictors, excessive alcohol intake, heart failure, myocardial infarction, sepsis, and stroke showed the strongest associations with adverse outcomes. The prediction models demonstrated fair to moderate discrimination, with c-index values of 0.70 (95%CI 0.70–0.71) for heart failure hospitalisation, 0.64 (95%CI 0.63–0.65) for myocardial infarction hospitalisation, 0.76 (95%CI 0.75–0.77) for vascular dementia, 0.66 (95%CI 0.65–0.67) for sepsis, 0.65 (95%CI 0.62–0.68) for sudden cardiac death, and 0.71 (95%CI 0.71–0.71) for all-cause death. Conclusions In patients with AF, we developed prediction models that demonstrate fair to good performance. Our models, based on variables easily obtainable in routine clinical care, may facilitate patient risk stratification and guide further diagnostic or preventive interventions.