Predicting Atrial Fibrillation and Its Complications.

Atrial fibrillation (AF) is a common cardiac arrhythmia associated with an increased risk of stroke and other complications. Identifying individuals at higher risk of developing AF in the community is now possible using validated predictive models that take into account clinical variables and circulating biomarkers. These models have shown adequate performance in racially and ethnically diverse populations. Similarly, risk stratification schemes predict incidence of ischemic stroke in persons with AF, assisting clinicians and patients in decisions regarding oral anticoagulation use. Complementary schemes have been developed to predict the risk of bleeding in AF patients taking vitamin K antagonists. However, major gaps in our ability to predict AF and its complications exist. Additional research should refine models for AF prediction and determine their value to improve population health and clinical outcomes, advance our ability to predict stroke and other complications in AF patients, and develop predictive models for bleeding events and other adverse effects in patients using non-vitamin K oral anticoagulants.

心房颤动（AF）是一种常见的心律失常，与中风及其他并发症风险增加相关。利用考虑临床变量和循环生物标志物的经过验证的预测模型，现在有可能在社区中识别出发生心房颤动风险较高的个体。这些模型在不同种族和民族的人群中已显示出足够的性能。同样，风险分层方案可预测心房颤动患者缺血性中风的发生率，协助临床医生和患者就口服抗凝剂的使用做出决策。已经制定了补充方案来预测服用维生素K拮抗剂的心房颤动患者的出血风险。然而，我们在预测心房颤动及其并发症的能力方面仍存在重大差距。进一步的研究应完善心房颤动预测模型，并确定其对改善人群健康和临床结果的价值，提高我们预测心房颤动患者中风及其他并发症的能力，并为使用非维生素K口服抗凝剂的患者开发出血事件和其他不良反应的预测模型。