Validation of an algorithm for identifying type 1 diabetes in adults based on electronic health record data.
Validation of an algorithm for identifying type 1 diabetes in adults based on electronic health record data.
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DOI：
10.1002/pds.4377
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发表时间：
2018-10
影响因子：
2.6
通讯作者：
Raebel MA
中科院分区：
文献类型：
--
作者：
Schroeder EB;Donahoo WT;Goodrich GK;Raebel MA
使用电子健康记录(EHR)中的信息来识别患有1型糖尿病的成年人的算法尚未得到很好的研究。这样的算法将在药物流行病学、药物安全性研究、临床试验、监测和质量改进中得到应用。我们的主要目标是使用已发表的算法(由Klompas等人开发)确定在成人中识别1型糖尿病的阳性预测值，并将其与大多数糖尿病诊断代码为1型糖尿病的简单要求进行比较。我们将Klompas算法和诊断代码标准应用于66,690名患有糖尿病的成年Kaiser Permanente Colorado成员。我们回顾了220例确诊为1型糖尿病患者的图表，并计算出阳性预测值。Klompas算法确定3,286名(66,690名)成年糖尿病患者中有4.9%患有1型糖尿病。根据图表回顾，总体阳性预测值为94.5%。大多数糖尿病诊断代码为1型的要求确定为3,000例(4.5%)患有1型糖尿病，阳性预测值为96.4%。然而，涉及尿丙酮试纸分配的算法标准表现不佳，阳性预测值为20.0%。来自EHR的数据可以用来准确地识别患有1型糖尿病的成年人。在诊断成人1型糖尿病时，我们建议采用不含尿丙酮试纸标准的Klompas算法的改进版本，或要求大多数糖尿病诊断代码为1型代码。
Algorithms using information from electronic health records (EHR) to identify adults with type 1 diabetes have not been well studied. Such algorithms would have applications in pharmacoepidemiology, drug safety research, clinical trials, surveillance, and quality improvement. Our main objectives were to determine the positive predictive value for identifying type 1 diabetes in adults using a published algorithm (developed by Klompas et al), and to compare it to a simple requirement that the majority of diabetes diagnosis codes be type 1. We applied the Klompas algorithm and the diagnosis code criterion to a cohort of 66,690 adult Kaiser Permanente Colorado members with diabetes. We reviewed 220 charts of those identified as having type 1 diabetes, and calculated positive predictive values. The Klompas algorithm identified 3,286 (4.9% of 66,690) adults with diabetes as having type 1 diabetes. Based on chart reviews, the overall positive predictive value was 94.5%. The requirement that the majority of diabetes diagnosis codes be type 1 identified 3,000 (4.5%) as having type 1 diabetes, and had a positive predictive value of 96.4%. However, the algorithm criterion involving dispensing of urine acetone test strips performed poorly, with a positive predictive value of 20.0%. Data from EHRs can be used to accurately identify adults with type 1 diabetes. When identifying adults with type 1 diabetes, we recommend either a modified version of the Klompas algorithm without the urine acetone test strips criterion, or the requirement that the majority of diabetes diagnosis codes be type 1 codes.
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影响因子：
5.1
作者：
Zgibor, Janice C.;Orchard, Trevor J.;Siminerio, Linda M.
通讯作者：
Siminerio, Linda M.
影响因子：
16.2
作者：
Rhodes, Erinn T.;Laffel, Lori M. B.;Ludwig, David S.
通讯作者：
Ludwig, David S.
影响因子：
5
作者：
Lawrence, Jean M.;Black, Mary Helen;Reynolds, Kristi
通讯作者：
Reynolds, Kristi
影响因子：
16.2
作者：
Klompas M;Eggleston E;McVetta J;Lazarus R;Li L;Platt R
通讯作者：
Platt R
影响因子：
5.5
作者：
Nichols GA;Desai J;Elston Lafata J;Lawrence JM;O'Connor PJ;Pathak RD;Raebel MA;Reid RJ;Selby JV;Silverman BG;Steiner JF;Stewart WF;Vupputuri S;Waitzfelder B;SUPREME-DM Study Group
通讯作者：
SUPREME-DM Study Group