Validation of Pediatric Diabetes Case Identification Approaches for Diagnosed Cases by Using Information in the Electronic Health Records of a Large Integrated Managed Health Care Organization
Validation of Pediatric Diabetes Case Identification Approaches for Diagnosed Cases by Using Information in the Electronic Health Records of a Large Integrated Managed Health Care Organization
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DOI：
10.1093/aje/kwt230
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发表时间：
2014-01-01
影响因子：
5
通讯作者：
Reynolds, Kristi
中科院分区：
文献类型：
--
作者：
Lawrence, Jean M.;Black, Mary Helen;Reynolds, Kristi
我们探讨了不同的算法的效用，糖尿病病例识别使用电子健康记录。住院和门诊诊断代码，以及实验室结果和抗糖尿病药物的分发数据提取自2009年小于20岁的Kaiser Permanente Southern加州成员的电子健康记录。糖尿病病例是通过使用青年糖尿病研究方案来确定的，并构成了金标准。在1,000个自助样本中比较了灵敏度、特异性、阳性和阴性预测值、准确性和受试者工作特征曲线下面积（AUC）。基于792，992名青年的数据，其中1，568人患有糖尿病（77.2，1型糖尿病; 22.2，2型糖尿病; 0.6，其他），对于有1个或多个门诊糖尿病诊断或1个或多个胰岛素处方的患者，病例识别准确率在75个自助样本中最高（灵敏度，95.9;阳性预测值，95.5; AUC，97.9）和25个样本中有2个或更多门诊糖尿病诊断和1个或更多抗糖尿病药物（灵敏度，92.4;阳性预测值，98.4; AUC，96.2）。有1个或多个门诊1型糖尿病诊断（国际疾病分类，第九次修订，临床修改，代码250.x1或250.x3）具有最高的1型糖尿病准确性（94.4）和AUC（94.1）;没有1型糖尿病诊断具有最高的准确性（93.8）和AUC（93.6）用于识别2型糖尿病。来自管理型医疗保健组织的电子健康记录中的信息为儿童糖尿病监测提供了高效且具有成本效益的数据来源。
We explored the utility of different algorithms for diabetes case identification by using electronic health records. Inpatient and outpatient diagnosis codes, as well as data on laboratory results and dispensing of antidiabetic medications were extracted from electronic health records of Kaiser Permanente Southern California members who were less than 20 years of age in 2009. Diabetes cases were ascertained by using the SEARCH for Diabetes in Youth Study protocol and comprised the gold standard. Sensitivity, specificity, positive and negative predictive values, accuracy, and the area under the receiver operating characteristic curve (AUC) were compared in 1,000 bootstrapped samples. Based on data from 792,992 youth, of whom 1,568 had diabetes (77.2, type 1 diabetes; 22.2, type 2 diabetes; 0.6, other), case identification accuracy was highest in 75 of bootstrapped samples for those who had 1 or more outpatient diabetes diagnoses or 1 or more insulin prescriptions (sensitivity, 95.9; positive predictive value, 95.5; AUC, 97.9) and in 25 of samples for those who had 2 or more outpatient diabetes diagnoses and 1 or more antidiabetic medications (sensitivity, 92.4; positive predictive value, 98.4; AUC, 96.2). Having 1 or more outpatient type 1 diabetes diagnoses (International Classification of Diseases, Ninth Revision, Clinical Modification, code 250.x1 or 250.x3) had the highest accuracy (94.4) and AUC (94.1) for type 1 diabetes; the absence of type 1 diabetes diagnosis had the highest accuracy (93.8) and AUC (93.6) for identifying type 2 diabetes. Information in the electronic health records from managed health care organizations provides an efficient and cost-effective source of data for childhood diabetes surveillance.