Thiru Krish, Donnan Peter T, Weller Peter, Sullivan Frank
Great Ormond Street Hospital, Cardiac Critical Care Services, Great Ormond Street Hospital, London, UK.
Inform Prim Care. 2009;17(4):215-24. doi: 10.14236/jhi.v17i4.740.
General practitioners are increasingly required to practice in a paperless environment and to collect clinical data electronically on electronic patient record (EPR) systems. A principal step in meeting general practice information needs continues to be the establishment of disease registers and consequently the identification of patient populations within primary care databases is a prerequisite. This study aims to identify and validate the optimal search strategy for coronary heart disease (CHD).
A multiple logistic regression model for the identification of CHD patients was developed in one site using electronic data, the receiver operating characteristic (ROC) curve and Bayesian statistics. The model was tested on two trial sites.
Young male CHD patients are more easily identified by generic searches than older females. The optimal search strategy for CHD was found to be the diagnostic code for CHD, nitrate and digoxin but this was dependent on the disease description, age and sex of the study population and the coding system used within the database. Diagnostic code for CHD identified 80.6% (95% confidence interval (CI) 77-83%), 90.0% (CI 88-92%) and 95.9% (CI 94-97%) of local, national and international definitions respectively, with 100% positive predictive values (PPVs) for all definitions.
Generic queries may inadvertently perpetuate inequalities in health care. Queries should be bespoke and mindful of the conceptualization of disease by the clinicians recording these data.
越来越要求全科医生在无纸环境中执业，并在电子病历（EPR）系统上以电子方式收集临床数据。满足全科医疗信息需求的一个主要步骤仍然是建立疾病登记册，因此在初级保健数据库中识别患者群体是一个先决条件。本研究旨在确定并验证冠心病（CHD）的最佳搜索策略。
在一个地点使用电子数据、受试者操作特征（ROC）曲线和贝叶斯统计方法建立了一个用于识别冠心病患者的多元逻辑回归模型。该模型在两个试验地点进行了测试。
与老年女性相比，年轻男性冠心病患者通过通用搜索更容易被识别。发现冠心病的最佳搜索策略是冠心病、硝酸盐和地高辛的诊断代码，但这取决于研究人群的疾病描述、年龄和性别以及数据库中使用的编码系统。冠心病诊断代码分别识别出本地、国家和国际定义的80.6%（95%置信区间（CI）77 - 83%）、90.0%（CI 88 - 92%）和95.9%（CI 94 - 97%），所有定义的阳性预测值（PPV）均为100%。
通用查询可能无意中使医疗保健中的不平等现象长期存在。查询应该是定制的，并考虑到记录这些数据的临床医生对疾病的概念化。
