Accuracy of administrative data for surveillance of healthcare-associated infections: a systematic review.
Accuracy of administrative data for surveillance of healthcare-associated infections: a systematic review.
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DOI：
10.1136/bmjopen-2015-008424
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发表时间：
2015-08-27
期刊：
影响因子：
2.9
通讯作者：
Lee GM
中科院分区：
文献类型：
--
作者：
van Mourik MS;van Duijn PJ;Moons KG;Bonten MJ;Lee GM
测量医疗相关感染（HAI）的发生率在当前的医疗保健提供系统中越来越重要。管理数据算法，包括诊断代码（组合），通常用于确定HAI的发生，以支持医院内监测计划或作为独立的质量指标。我们进行了一项系统评价，评价HAI检测的管理数据的诊断准确性。系统检索Medline、Embase、CINAHL和科克伦的相关研究（1995-2013）。使用QUADAS-2标准进行方法学质量评估;诊断准确性估计值按HAI类型和关键研究特征分层。纳入了57项研究，大多数旨在检测手术部位或血流感染。研究设计在管理数据算法（代码选择、随访）的规范和HAI存在的定义方面非常多样化。三分之一的研究存在重要的方法学局限性，包括差异或不完整的HAI确定或缺乏对评估者的盲法。对于医院内算法和正式质量指标，HAI检测的管理数据算法的观察到的灵敏度和阳性预测值非常不均匀，通常最多也就是中等;识别器械相关HAI（如中心线相关血流感染）的准确性特别差。在大多数情况下，所纳入研究的研究设计存在较大异质性，无法正式计算总结诊断准确性估计值。行政数据检测HAI的准确性有限且高度可变，建议明智地将其用于内部监测工作和外部质量评估。如果医院和政策制定者选择依赖行政数据进行HAI监测，则必须继续改进现有算法及其稳健的验证。
Measuring the incidence of healthcare-associated infections (HAI) is of increasing importance in current healthcare delivery systems. Administrative data algorithms, including (combinations of) diagnosis codes, are commonly used to determine the occurrence of HAI, either to support within-hospital surveillance programmes or as free-standing quality indicators. We conducted a systematic review evaluating the diagnostic accuracy of administrative data for the detection of HAI. Systematic search of Medline, Embase, CINAHL and Cochrane for relevant studies (1995–2013). Methodological quality assessment was performed using QUADAS-2 criteria; diagnostic accuracy estimates were stratified by HAI type and key study characteristics. 57 studies were included, the majority aiming to detect surgical site or bloodstream infections. Study designs were very diverse regarding the specification of their administrative data algorithm (code selections, follow-up) and definitions of HAI presence. One-third of studies had important methodological limitations including differential or incomplete HAI ascertainment or lack of blinding of assessors. Observed sensitivity and positive predictive values of administrative data algorithms for HAI detection were very heterogeneous and generally modest at best, both for within-hospital algorithms and for formal quality indicators; accuracy was particularly poor for the identification of device-associated HAI such as central line associated bloodstream infections. The large heterogeneity in study designs across the included studies precluded formal calculation of summary diagnostic accuracy estimates in most instances. Administrative data had limited and highly variable accuracy for the detection of HAI, and their judicious use for internal surveillance efforts and external quality assessment is recommended. If hospitals and policymakers choose to rely on administrative data for HAI surveillance, continued improvements to existing algorithms and their robust validation are imperative.