Identification of Dyslipidemic Patients Attending Primary Care Clinics Using Electronic Medical Record (EMR) Data from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN) Database

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• 作者：Erfan Aref-Eshghi ; Justin Oake ; Marshall Godwin…
• 关键词：Electronic medical records ; Algorithm ; Dyslipidemia ; Data mining
• 刊名：Journal of Medical Systems
• 出版年：2017
• 出版时间：March 2017
• 年：2017
• 卷：41
• 期：3
• 全文大小：
• 刊物类别：Mathematics and Statistics
• 刊物主题：Health Informatics; Health Informatics; Statistics for Life Sciences, Medicine, Health Sciences;
• 出版者：Springer US
• ISSN：1573-689X
• 卷排序：41

文摘

The objective of this study was to define the optimal algorithm to identify patients with dyslipidemia using electronic medical records (EMRs). EMRs of patients attending primary care clinics in St. John’s, Newfoundland and Labrador (NL), Canada during 2009–2010, were studied to determine the best algorithm for identification of dyslipidemia. Six algorithms containing three components, dyslipidemia ICD coding, lipid lowering medication use, and abnormal laboratory lipid levels, were tested against a gold standard, defined as the existence of any of the three criteria. Linear discriminate analysis, and bootstrapping were performed following sensitivity/specificity testing and receiver’s operating curve analysis. Two validating datasets, NL records of 2011–2014, and Canada-wide records of 2010–2012, were used to replicate the results. Relative to the gold standard, combining laboratory data together with lipid lowering medication consumption yielded the highest sensitivity (99.6%), NPV (98.1%), Kappa agreement (0.98), and area under the curve (AUC, 0.998). The linear discriminant analysis for this combination resulted in an error rate of 0.15 and an Eigenvalue of 1.99, and the bootstrapping led to AUC: 0.998, 95% confidence interval: 0.997–0.999, Kappa: 0.99. This algorithm in the first validating dataset yielded a sensitivity of 97%, Negative Predictive Value (NPV) = 83%, Kappa = 0.88, and AUC = 0.98. These figures for the second validating data set were 98%, 93%, 0.95, and 0.99, respectively. Combining laboratory data with lipid lowering medication consumption within the EMR is the best algorithm for detecting dyslipidemia. These results can generate standardized information systems for dyslipidemia and other chronic disease investigations using EMRs.