The health informatics cohort enhancement project (HICE): using routinely collected primary care data to identify people with a lifetime diagnosis of psychotic disorder.
The health informatics cohort enhancement project (HICE): using routinely collected primary care data to identify people with a lifetime diagnosis of psychotic disorder.
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DOI：
10.1186/1756-0500-5-95
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发表时间：
2012-02-14
影响因子：
1.8
通讯作者：
Lloyd K
中科院分区：
文献类型：
--
作者：
Economou A;Grey M;McGregor J;Craddock N;Lyons RA;Owen MJ;Price V;Thomson S;Walters JT;Lloyd K
我们之前已经证明，常规收集的初级保健数据可用于确定抑郁症bbb试验的潜在参与者。在这里，我们展示了如何从初级保健记录中识别精神障碍患者，以纳入队列研究。我们讨论了这种方法的优点和局限性；评估其潜在价值并报告遇到的挑战。我们设计了一种算法，用于在常规收集的健康数据的安全匿名信息链接（SAIL）数据库中搜索终身诊断为精神障碍的患者。该算法是根据“金标准”的建立良好的操作标准清单的精神病和情感性疾病（OPCRIT）进行验证。研究人员研究了来自斯旺西社区精神健康小组（CMHT）的100名患者的病例记录，其中80人与全科医生的记录相符。该算法具有良好的测试特性，对精神障碍患者的检测能力非常好（灵敏度> 0.7），对精神障碍患者的不误识别能力也很好（特异性> 0.9）。在一定的限制下，我们的算法可以用于搜索一般实践数据并可靠地识别精神障碍患者。这可能有助于确定潜在纳入队列研究的候选人。
We have previously demonstrated that routinely collected primary care data can be used to identify potential participants for trials in depression [1]. Here we demonstrate how patients with psychotic disorders can be identified from primary care records for potential inclusion in a cohort study. We discuss the strengths and limitations of this approach; assess its potential value and report challenges encountered. We designed an algorithm with which we searched for patients with a lifetime diagnosis of psychotic disorders within the Secure Anonymised Information Linkage (SAIL) database of routinely collected health data. The algorithm was validated against the "gold standard" of a well established operational criteria checklist for psychotic and affective illness (OPCRIT). Case notes of 100 patients from a community mental health team (CMHT) in Swansea were studied of whom 80 had matched GP records. The algorithm had favourable test characteristics, with a very good ability to detect patients with psychotic disorders (sensitivity > 0.7) and an excellent ability not to falsely identify patients with psychotic disorders (specificity > 0.9). With certain limitations our algorithm can be used to search the general practice data and reliably identify patients with psychotic disorders. This may be useful in identifying candidates for potential inclusion in cohort studies.