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开发用于识别严重肢体缺血患者的行政数据算法。

Development of administrative data algorithms to identify patients with critical limb ischemia.

作者信息

Bekwelem Wobo, Bengtson Lindsay G S, Oldenburg Niki C, Winden Tamara J, Keo Hong H, Hirsch Alan T, Duval Sue

机构信息

Lillehei Heart Institute and Cardiovascular Division, University of Minnesota Medical School, Minneapolis, MN, USA.

Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, MN, USA.

出版信息

Vasc Med. 2014 Dec;19(6):483-90. doi: 10.1177/1358863X14559589.

Abstract

Administrative data have been used to identify patients with various diseases, yet no prior study has determined the utility of International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM)-based codes to identify CLI patients. CLI cases (n=126), adjudicated by a vascular specialist, were carefully defined and enrolled in a hospital registry. Controls were frequency matched to cases on age, sex and admission date in a 2:1 ratio. ICD-9-CM codes for all patients were extracted. Algorithms were developed using frequency distributions of these codes, risk factors and procedures prevalent in CLI. The sensitivity for each algorithm was calculated and applied within the hospital system to identify CLI patients not included in the registry. Sensitivity ranged from 0.29 to 0.92. An algorithm based on diagnosis and procedure codes exhibited the best overall performance (sensitivity of 0.92). Each algorithm had differing CLI identification characteristics based on patient location. Administrative data can be used to identify CLI patients within a health system. The algorithms, developed from these data, can serve as a tool to facilitate clinical care, research, quality improvement, and population surveillance.

摘要

行政数据已被用于识别患有各种疾病的患者,但此前尚无研究确定基于国际疾病分类第九版临床修订本(ICD-9-CM)编码来识别下肢慢性缺血(CLI)患者的效用。由血管专科医生判定的CLI病例(n = 126)经过仔细定义后被纳入医院登记系统。对照组在年龄、性别和入院日期方面与病例按2:1的比例进行频率匹配。提取了所有患者的ICD-9-CM编码。利用这些编码的频率分布、CLI中常见的危险因素和手术制定了算法。计算了每种算法的敏感性,并将其应用于医院系统内以识别未纳入登记系统的CLI患者。敏感性范围为0.29至0.92。一种基于诊断和手术编码的算法表现出最佳的总体性能(敏感性为0.92)。每种算法根据患者所在位置具有不同的CLI识别特征。行政数据可用于在医疗系统内识别CLI患者。从这些数据中开发的算法可作为一种工具,以促进临床护理、研究、质量改进和人群监测。

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