Corrales-Medina Vicente F, van Walraven Carl
Clinical Epidemiology Program, The Ottawa Hospital Research Institute, Ottawa, Ontario, Canada.
Department of Medicine, University of Ottawa, Ottawa, Ontario, Canada.
J Gen Intern Med. 2021 Mar;36(3):683-690. doi: 10.1007/s11606-020-06211-4. Epub 2021 Jan 8.
Administrative data algorithms (ADAs) to identify pneumonia cases are commonly used in the analysis of pneumonia burden, trends, etiology, processes of care, outcomes, health care utilization, cost, and response to preventative and therapeutic interventions. However, without a good understanding of the validity of ADAs for pneumonia case identification, an adequate appreciation of this literature is difficult. We systematically reviewed the quality and accuracy of published ADAs to identify adult hospitalized pneumonia cases.
We reviewed the Medline, EMBase, and Cochrane Central databases through May 2020. All studies describing ADAs for adult hospitalized pneumonia and at least one accuracy statistic were included. Investigators independently extracted information about the sampling frame, reference standard, ADA composition, and ADA accuracy.
Thirteen studies involving 24 ADAs were analyzed. Compliance with a 38-item study-quality assessment tool ranged from 17 to 29 (median, 23; interquartile range [IQR], 20 to 25). Study setting, design, and ADA composition varied extensively. Inclusion criteria of most studies selected for high-risk populations and/or increased pneumonia likelihood. Reference standards with explicit criteria (clinical, laboratorial, and/or radiographic) were used in only 4 ADAs. Only 2 ADAs were validated (one internally and one externally). ADA positive predictive values ranged from 35.0 to 96.5% (median, 84.8%; IQR, 65.3 to 89.1%). However, these values are exaggerated for an unselected patient population because pneumonia prevalences in the study cohorts were very high (median, 66%; IQR, 46 to 86%). ADA sensitivities ranged from 31.3 to 97.8% (median, 65.1%; IQR 52.5-72.4).
ADAs for identification of adult pneumonia hospitalizations are highly heterogeneous, poorly validated, and at risk for misclassification bias. Greater standardization in reporting ADA accuracy is required in studies using pneumonia ADA for case identification so that results can be properly interpreted.
用于识别肺炎病例的行政数据算法(ADA)常用于分析肺炎负担、趋势、病因、护理过程、结局、医疗保健利用情况、成本以及对预防和治疗干预措施的反应。然而,如果对ADA识别肺炎病例的有效性缺乏充分了解,就很难充分理解这方面的文献。我们系统地回顾了已发表的用于识别成人住院肺炎病例的ADA的质量和准确性。
我们检索了截至2020年5月的Medline、EMBase和Cochrane Central数据库。纳入所有描述用于成人住院肺炎的ADA且至少有一项准确性统计数据的研究。研究人员独立提取有关抽样框架、参考标准、ADA组成和ADA准确性的信息。
分析了13项涉及24种ADA的研究。对一项包含38个条目的研究质量评估工具的依从性范围为17至29(中位数为23;四分位间距[IQR]为20至25)。研究背景、设计和ADA组成差异很大。大多数研究的纳入标准选择了高危人群和/或肺炎可能性增加的人群。仅4种ADA使用了具有明确标准(临床、实验室和/或影像学)的参考标准。仅2种ADA得到了验证(一种为内部验证,一种为外部验证)。ADA的阳性预测值范围为35.0%至96.5%(中位数为84.8%;IQR为65.3%至89.1%)。然而,对于未经过选择的患者群体,这些值被夸大了,因为研究队列中的肺炎患病率非常高(中位数为66%;IQR为46%至86%)。ADA的敏感性范围为31.3%至97.8%(中位数为65.1%;IQR为52.5%至72.4%)。
用于识别成人肺炎住院病例的ADA高度异质,验证不足,存在分类错误偏差的风险。在使用肺炎ADA进行病例识别的研究中,需要在报告ADA准确性方面实现更大程度的标准化,以便能够正确解释结果。