BACKGROUND Clinical guidelines and public health authorities lack recommendations on scalable approaches to defining and monitoring the occurrence and severity of bleeding in populations prescribed antithrombotic therapy. METHODS We examined linked primary care, hospital admission and death registry electronic health records (CALIBER 1998-2010, England) of patients with newly diagnosed atrial fibrillation, acute myocardial infarction, unstable angina or stable angina with the aim to develop algorithms for bleeding events. Using the developed bleeding phenotypes, Kaplan-Meier plots were used to estimate the incidence of bleeding events and we used Cox regression models to assess the prognosis for all-cause mortality, atherothrombotic events and further bleeding. RESULTS We present electronic health record phenotyping algorithms for bleeding based on bleeding diagnosis in primary or hospital care, symptoms, transfusion, surgical procedures and haemoglobin values. In validation of the phenotype, we estimated a positive predictive value of 0.88 (95% CI 0.64, 0.99) for hospitalised bleeding. Amongst 128,815 patients, 27,259 (21.2%) had at least 1 bleeding event, with 5-year risks of bleeding of 29.1%, 21.9%, 25.3% and 23.4% following diagnoses of atrial fibrillation, acute myocardial infarction, unstable angina and stable angina, respectively. Rates of hospitalised bleeding per 1000 patients more than doubled from 1.02 (95% CI 0.83, 1.22) in January 1998 to 2.68 (95% CI 2.49, 2.88) in December 2009 coinciding with the increased rates of antiplatelet and vitamin K antagonist prescribing. Patients with hospitalised bleeding and primary care bleeding, with or without markers of severity, were at increased risk of all-cause mortality and atherothrombotic events compared to those with no bleeding. For example, the hazard ratio for all-cause mortality was 1.98 (95% CI 1.86, 2.11) for primary care bleeding with markers of severity and 1.99 (95% CI 1.92, 2.05) for hospitalised bleeding without markers of severity, compared to patients with no bleeding. CONCLUSIONS Electronic health record bleeding phenotyping algorithms offer a scalable approach to monitoring bleeding in the population. Incidence of bleeding has doubled in incidence since 1998, affects one in four cardiovascular disease patients, and is associated with poor prognosis. Efforts are required to tackle this iatrogenic epidemic.

中文翻译：

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心脏病患者出血服用抗血栓药物：电子健康记录表型算法、发生率、趋势和预后。


背景技术临床指南和公共卫生当局缺乏关于可扩展方法的建议来定义和监测接受抗血栓治疗的人群中出血的发生和严重程度。方法 我们检查了新诊断的房颤、急性心肌梗死、不稳定型心绞痛或稳定型心绞痛患者的初级保健、入院和死亡登记电子健康记录（CALIBRE 1998-2010，英国），旨在开发出血事件的算法。使用开发的出血表型，Kaplan-Meier 图用于估计出血事件的发生率，我们使用 Cox 回归模型来评估全因死亡率、动脉粥样硬化血栓事件和进一步出血的预后。结果我们提出了基于初级或医院护理中的出血诊断、症状、输血、外科手术和血红蛋白值的出血电子健康记录表型算法。在表型验证中，我们估计住院出血的阳性预测值为 0.88（95% CI 0.64，0.99）。在 128,815 名患者中，27,259 名患者（21.2%）至少发生过 1 次出血事件，诊断房颤、急性心肌梗死、不稳定型心绞痛和稳定型心绞痛后 5 年出血风险分别为 29.1%、21.9%、25.3% 和 23.4% ， 分别。每 1000 名患者的住院出血率从 1998 年 1 月的 1.02（95% CI 0.83，1.22）增加了一倍多，到 2009 年 12 月的 2.68（95% CI 2.49，2.88），与抗血小板和维生素 K 拮抗剂处方率的增加相一致。 与无出血的患者相比，住院出血和初级保健出血的患者，无论有或没有严重程度标记，全因死亡率和动脉粥样硬化血栓事件的风险增加。例如，与患者相比，具有严重程度标记的初级保健出血的全因死亡率的风险比为 1.98 (95% CI 1.86, 2.11)，而没有严重程度标记的住院出血的全因死亡率的风险比为 1.99 (95% CI 1.92, 2.05)。没有出血。结论电子健康记录出血表型算法提供了一种可扩展的方法来监测人群出血。自 1998 年以来，出血的发生率增加了一倍，影响四分之一的心血管疾病患者，并且与不良预后相关。需要努力应对这一医源性流行病。