Oake Justin, Aref-Eshghi Erfan, Godwin Marshall, Collins Kayla, Aubrey-Bassler Kris, Duke Pauline, Mahdavian Masoud, Asghari Shabnam
Centre for Rural Health Studies, Faculty of Medicine, Memorial University of Newfoundland St. John's, NL, Canada.
Primary Healthcare Research Unit, Department of Family Medicine, Faculty of Medicine, Memorial University of Newfoundland, St. John's, NL, Canada.
Biomed Inform Insights. 2017 Feb 10;9:1178222616685880. doi: 10.1177/1178222616685880. eCollection 2017.
To assess the validity of the International Classification of Disease (ICD) codes for identifying patients with dyslipidemia in electronic medical record (EMR) data.
The EMRs of patients receiving primary care in St. John's, Newfoundland and Labrador (NL), Canada, were retrieved from the Canadian Primary Care Sentinel Surveillance Network database. International Classification of Disease codes were first compared with laboratory lipid data as an independent criterion standard, and next with a "comprehensive criterion standard," defined as any existence of abnormal lipid test, lipid-lowering medication record, or dyslipidemia ICD codes. The ability of ICD coding alone or combined with other components was evaluated against the two criterion standards using receiver operating characteristic (ROC) analysis, sensitivity, specificity, negative predictive value (NPV) and Kappa agreement. (No specificity was reported for the comparison of ICD codes against the comprehensive criterion standard as this naturally leads to 100% specificity.).
The ICD codes led to a poor outcome when compared with the serum lipid levels (sensitivity, 27%; specificity, 76%; PPV, 71%; NPV, 33%; Kappa, 0.02; area under the receiver operating characteristic curve (AUC), 0.51) or with the comprehensive criterion standard (sensitivity, 32%; NPV, 25%; Kappa, 0.15; AUC, 66%). International Classification of Disease codes combined with lipid-lowering medication data also resulted in low sensitivity (51.2%), NPV (32%), Kappa (0.28), and AUC (75%). The addition of laboratory lipid levels to ICD coding marginally improved the algorithm (sensitivity, 94%; NPV, 79%; Kappa, 0.85; AUC, 97%).
The use of ICD coding, either alone or in combination with laboratory data or lipid-lowering medication records, was not an accurate indicator in identifying dyslipidemia.
评估国际疾病分类（ICD）编码在电子病历（EMR）数据中识别血脂异常患者的有效性。
从加拿大初级保健哨点监测网络数据库中检索加拿大纽芬兰和拉布拉多省圣约翰市接受初级保健患者的电子病历。首先将国际疾病分类编码与实验室血脂数据作为独立的标准进行比较，其次与“综合标准”进行比较，“综合标准”定义为存在任何血脂检测异常、降脂药物记录或血脂异常ICD编码。使用受试者工作特征（ROC）分析、敏感性、特异性、阴性预测值（NPV）和Kappa一致性，针对这两个标准评估单独使用ICD编码或与其他组件组合的能力。（未报告ICD编码与综合标准比较的特异性，因为这自然会导致100%的特异性。）
与血脂水平（敏感性27%；特异性76%；阳性预测值71%；阴性预测值33%；Kappa值0.02；受试者工作特征曲线下面积（AUC）0.51）或综合标准（敏感性32%；阴性预测值25%；Kappa值0.15；AUC 66%）相比，ICD编码的结果较差。国际疾病分类编码与降脂药物数据相结合也导致敏感性（51.2%）、阴性预测值（32%）、Kappa值（0.28）和AUC（75%）较低。在ICD编码中加入实验室血脂水平对算法有轻微改善（敏感性94%；阴性预测值79%；Kappa值0.85；AUC 97%）。
单独使用ICD编码或与实验室数据或降脂药物记录结合使用，都不是识别血脂异常的准确指标。
