Automated detection and classification of type 1 versus type 2 diabetes using electronic health record data.
Automated detection and classification of type 1 versus type 2 diabetes using electronic health record data.
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DOI：
10.2337/dc12-0964
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发表时间：
2013-04
期刊：
影响因子：
16.2
通讯作者：
Platt R
中科院分区：
文献类型：
--
作者：
Klompas M;Eggleston E;McVetta J;Lazarus R;Li L;Platt R
创建监测算法，使用结构化电子健康记录（EHR）数据检测糖尿病并对1型和2型糖尿病进行分类。我们从一个大型的、多站点的、多专业的门诊实践中提取了4年的数据，该实践为170万名患者提供服务。我们使用实验室检查结果、诊断代码和处方来标记可能的糖尿病病例。我们评估了这些数据的新组合的敏感性和阳性预测值，以在210名个体中对1型和2型糖尿病进行分类。我们将优化算法应用于实时、前瞻性、基于EHR的监测系统，并审查了100个额外的病例进行验证。糖尿病算法标记了43,177名患者。所有标准均贡献了独特的病例：78%有糖尿病诊断代码，66%符合实验室标准，46%有提示性处方。ICD-9编码对1型糖尿病的敏感性和阳性预测值分别为26%（95%CI 12-49）和94%（83-100）;对于两个或更多个1型编码加上任意数量的2型编码，分别为90%（81-95）和57%（33-86）。结合1型与2型代码比率、血浆C肽和自身抗体水平以及提示性处方的优化算法标记了66/66例（100% [96-100]）1型糖尿病患者。经验证，优化算法正确分类了36例1型糖尿病患者中的35例（原始灵敏度为97% [87-100]，人群加权灵敏度为65% [36-100]，阳性预测值为88% [78-98]）。应用于EHR数据的算法比索赔代码检测到更多的糖尿病病例，并合理区分1型和2型糖尿病。
To create surveillance algorithms to detect diabetes and classify type 1 versus type 2 diabetes using structured electronic health record (EHR) data. We extracted 4 years of data from the EHR of a large, multisite, multispecialty ambulatory practice serving ∼700,000 patients. We flagged possible cases of diabetes using laboratory test results, diagnosis codes, and prescriptions. We assessed the sensitivity and positive predictive value of novel combinations of these data to classify type 1 versus type 2 diabetes among 210 individuals. We applied an optimized algorithm to a live, prospective, EHR-based surveillance system and reviewed 100 additional cases for validation. The diabetes algorithm flagged 43,177 patients. All criteria contributed unique cases: 78% had diabetes diagnosis codes, 66% fulfilled laboratory criteria, and 46% had suggestive prescriptions. The sensitivity and positive predictive value of ICD-9 codes for type 1 diabetes were 26% (95% CI 12–49) and 94% (83–100) for type 1 codes alone; 90% (81–95) and 57% (33–86) for two or more type 1 codes plus any number of type 2 codes. An optimized algorithm incorporating the ratio of type 1 versus type 2 codes, plasma C-peptide and autoantibody levels, and suggestive prescriptions flagged 66 of 66 (100% [96–100]) patients with type 1 diabetes. On validation, the optimized algorithm correctly classified 35 of 36 patients with type 1 diabetes (raw sensitivity, 97% [87–100], population-weighted sensitivity, 65% [36–100], and positive predictive value, 88% [78–98]). Algorithms applied to EHR data detect more cases of diabetes than claims codes and reasonably discriminate between type 1 and type 2 diabetes.
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影响因子：
16.2
作者：
Vehik, Kendra;Hamman, Richard F.;Dabelea, Dana
通讯作者：
Dabelea, Dana
影响因子：
13.6
作者：
Borchers, Andrea T.;Uibo, Raivo;Gershwin, M. E.
通讯作者：
Gershwin, M. E.
影响因子：
158.5
作者：
Zeitler, Phil;Hirst, Kathryn;Kaufman, Francine
通讯作者：
Kaufman, Francine
影响因子：
12.7
作者：
Klompas, Michael;McVetta, Jason;Platt, Richard
通讯作者：
Platt, Richard
DOI：
10.1197/jamia.m2848
发表时间：
2009-01-01
影响因子：
6.4
作者：
Lazarus, Ross;Klompas, Michael;Platt, Richard
通讯作者：
Platt, Richard