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首页> 外文期刊>Thrombosis Research: An International Journal on Vascular Obstruction, Hemorrhage and Hemostasis >Identifying major hemorrhage with automated data: Results of the Veterans Affairs Study to Improve Anticoagulation (VARIA)
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Identifying major hemorrhage with automated data: Results of the Veterans Affairs Study to Improve Anticoagulation (VARIA)

机译:使用自动数据识别重大出血:退伍军人事务研究以提高抗凝能力(VARIA)

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摘要

Introduction: Identifying major bleeding is fundamental to assessing the outcomes of anticoagulation therapy. This drives the need for a credible implementation in automated data for the International Society of Thrombosis and Haemostasis (ISTH) definition of major bleeding. Materials and Methods: We studied 102,395 patients who received 158,511 person-years of warfarin treatment from the Veterans Health Administration (VA) between 10/1/06-9/30/08. We constructed a list of ICD-9-CM codes of "candidate" bleeding events. Each candidate event was identified as a major hemorrhage if it fulfilled one of four criteria: 1) associated with death within 30 days; 2) bleeding in a critical anatomic site; 3) associated with a transfusion; or 4) was coded as the event that precipitated or was responsible for the majority of an inpatient hospitalization. Results: This definition classified 11,240 (15.8%) of 71, 338 candidate events as major hemorrhage. Typically, events more likely to be severe were retained at higher rates than those less likely to be severe. For example, Diverticula of Colon with Hemorrhage (562.12) and Hematuria (599.7) were retained 46% and 4% of the time, respectively. Major, intracranial, and fatal hemorrhage were identified at rates comparable to those found in randomized clinical trials however, higher than those reported in observational studies: 4.73, 1.29, and 0.41 per 100 patient years, respectively. Conclusions: We describe here a workable definition for identifying major hemorrhagic events from large automated datasets. This method of identifying major bleeding may have applications for quality measurement, quality improvement, and comparative effectiveness research.
机译:简介:识别大出血是评估抗凝治疗结果的基础。这推动了对国际血栓形成和止血协会(ISTH)对大出血的定义自动化数据中可靠实施的需求。材料和方法:我们研究了102,395例患者,这些患者在10/1 / 06-9 / 30/08期间接受了退伍军人卫生管理局(VA)接受的158,511人年华法林治疗。我们构建了“候选”出血事件的ICD-9-CM代码列表。如果每个候选事件满足以下四个标准之一,则被认为是重大出血:1)在30天内与死亡相关; 2)关键解剖部位出血; 3)伴有输血;或4)被编码为导致住院病人大部分住院或起因的事件。结果:该定义将71个,338个候选事件中的11240个(15.8%)归类为大出血。通常,与不太可能发生的严重事件相比,更可能发生的严重事件被保留的比率更高。例如,有出血的结肠憩室(562.12)和血尿(599.7)的保留时间分别为46%和4%。重大出血,颅内出血和致命性出血的发生率与随机临床试验中发现的相当,但高于观察性研究报告的比率:每100个患者年分别为4.73、1.29和0.41。结论:我们在这里描述了一个可行的定义,用于从大型自动化数据集中识别重大出血事件。这种识别重大出血的方法可能在质量测量,质量改进和比较有效性研究中具有应用。

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