Accuracy of routinely-collected healthcare data for identifying motor neurone disease cases: A systematic review.
Accuracy of routinely-collected healthcare data for identifying motor neurone disease cases: A systematic review.
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DOI：
10.1371/journal.pone.0172639
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发表时间：
2017
期刊：
影响因子：
3.7
通讯作者：
Sudlow CL
中科院分区：
文献类型：
--
作者：
Horrocks S;Wilkinson T;Schnier C;Ly A;Woodfield R;Rannikmäe K;Quinn TJ;Sudlow CL
运动神经元病（MND）是一种罕见的神经退行性疾病，病因尚不清楚。如果对疾病病例的识别足够准确，基于人群的大型前瞻性队列将能够对MND的决定因素进行强有力的研究。许多此类研究的后续工作依赖于与常规收集的健康数据集的联系。我们系统地评估了这些数据集在识别MND病例方面的准确性。我们对MEDLINE、EMBASE、Cochrane Library和Web of Science进行了电子检索，检索1990年1月1日至2015年11月16日期间发表的研究，将常规收集的编码数据集中发现的MND病例与参考标准进行比较。我们记录了研究特征和诊断准确性的两个关键指标-阳性预测值（PPV）和敏感性。我们对纳入的研究进行了描述性分析和质量评估。13项符合条件的研究提供了13项PPV估计和5项敏感性估计。12项研究评估了医院和/或死亡证明衍生的数据集；其中一项评估了初级保健数据集。所有研究均来自高收入国家（英国、欧洲、美国、香港）。研究方法千差万别，但质量总体上是好的。PPV估计范围为55-92%，敏感性范围为75-93%。对初级保健数据的单一（基于英国的）研究报告PPV为85%。常规收集的卫生数据集的诊断准确性可能足以在高收入国家背景下的大规模前瞻性流行病学研究中确定MND病例。初级保健数据集，特别是来自拥有广泛国家卫生保健系统的国家的数据集，是潜在的有价值的数据源，值得进一步调查。
Motor neurone disease (MND) is a rare neurodegenerative condition, with poorly understood aetiology. Large, population-based, prospective cohorts will enable powerful studies of the determinants of MND, provided identification of disease cases is sufficiently accurate. Follow-up in many such studies relies on linkage to routinely-collected health datasets. We systematically evaluated the accuracy of such datasets in identifying MND cases. We performed an electronic search of MEDLINE, EMBASE, Cochrane Library and Web of Science for studies published between 01/01/1990-16/11/2015 that compared MND cases identified in routinely-collected, coded datasets to a reference standard. We recorded study characteristics and two key measures of diagnostic accuracy—positive predictive value (PPV) and sensitivity. We conducted descriptive analyses and quality assessments of included studies. Thirteen eligible studies provided 13 estimates of PPV and five estimates of sensitivity. Twelve studies assessed hospital and/or death certificate-derived datasets; one evaluated a primary care dataset. All studies were from high income countries (UK, Europe, USA, Hong Kong). Study methods varied widely, but quality was generally good. PPV estimates ranged from 55–92% and sensitivities from 75–93%. The single (UK-based) study of primary care data reported a PPV of 85%. Diagnostic accuracy of routinely-collected health datasets is likely to be sufficient for identifying cases of MND in large-scale prospective epidemiological studies in high income country settings. Primary care datasets, particularly from countries with a widely-accessible national healthcare system, are potentially valuable data sources warranting further investigation.