Evaluation of structured data from electronic health records to identify clinical classification criteria attributes for systemic lupus erythematosus.
Evaluation of structured data from electronic health records to identify clinical classification criteria attributes for systemic lupus erythematosus.
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DOI：
10.1136/lupus-2021-000488
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发表时间：
2021-04
影响因子：
3.9
通讯作者：
Ramsey-Goldman R
中科院分区：
文献类型：
--
作者：
Walunas TL;Ghosh AS;Pacheco JA;Mitrovic V;Wu A;Jackson KL;Schusler R;Chung A;Erickson D;Mancera-Cuevas K;Luo Y;Kho AN;Ramsey-Goldman R
我们的目标是开发算法，使用电子健康记录（EHR）中的结构化数据识别狼疮临床分类标准属性，并确定它们是否可用于描述狼疮患者队列，并将其与定义的健康对照队列区分开来。我们创建了金标准狼疮和健康患者队列，这些队列完全符合美国风湿病学会（ACR）、系统性狼疮国际合作诊所（SLICC）和欧洲抗风湿病联盟/ACR（EULAR/ACR）分类标准，并具有匹配的EHR数据。我们实现了基于规则的算法，使用结构化数据的EHR系统中的三个分类标准的每个属性。在我们的组合队列中评估了个体标准属性和分类标准算法作为一个整体，并通过灵敏度和特异性测量了算法的总体性能。个体分类标准属性的敏感性范围很广，为7%（口腔溃疡）至97%（血液系统疾病），特异性为56%（血液系统疾病）至98%（光敏性），但所有这些都可以在EHR数据中识别。一般来说，基于实验室结果的算法比主要基于诊断代码的算法表现更好。所有三种分类标准系统都有效地区分了我们的病例和对照组，但基于SLICC标准的算法具有最高的整体性能（76%的灵敏度，99%的特异性）。使用基于分类标准的算法来评估结构化的EHR数据，可以对狼疮患者的疾病表现进行评估。这些算法可以减少图表审查负担，并且是基于疾病表现识别狼疮患者亚群以支持精准医学应用的基础。
Our objective was to develop algorithms to identify lupus clinical classification criteria attributes using structured data found in the electronic health record (EHR) and determine whether they could be used to describe a cohort of people with lupus and discriminate them from a defined healthy control cohort. We created gold standard lupus and healthy patient cohorts that were fully adjudicated for the American College of Rheumatology (ACR), Systemic Lupus International Collaborating Clinics (SLICC) and European League Against Rheumatism/ACR (EULAR/ACR) classification criteria and had matched EHR data. We implemented rule-based algorithms using structured data within the EHR system for each attribute of the three classification criteria. Individual criteria attribute and classification criteria algorithms as a whole were assessed over our combined cohorts and the overall performance of the algorithms was measured through sensitivity and specificity. Individual classification criteria attributes had a wide range of sensitivities, 7% (oral ulcers) to 97% (haematological disorders) and specificities, 56% (haematological disorders) to 98% (photosensitivity), but all could be identified in EHR data. In general, algorithms based on laboratory results performed better than those primarily based on diagnosis codes. All three classification criteria systems effectively distinguished members of our case and control cohorts, but the SLICC criteria-based algorithm had the highest overall performance (76% sensitivity, 99% specificity). It is possible to characterise disease manifestations in people with lupus using classification criteria-based algorithms that assess structured EHR data. These algorithms may reduce chart review burden and are a foundation for identifying subpopulations of patients with lupus based on disease presentation to support precision medicine applications.
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影响因子：
--
作者：
Petri, Michelle;Orbai, Ana-Maria;Alarcon, Graciela S.;Gordon, Caroline;Merrill, Joan T.;Fortin, Paul R.;Bruce, Ian N.;Isenberg, David;Wallace, Daniel J.;Nived, Ola;Sturfelt, Gunnar;Ramsey-Goldman, Rosalind;Bae, Sang-Cheol;Hanly, John G.;Sanchez-Guerrero, Jorge;Clarke, Ann;Aranow, Cynthia;Manzi, Susan;Urowitz, Murray;Gladman, Dafna;Kalunian, Kenneth;Costner, Melissa;Werth, Victoria P.;Zoma, Asad;Bernatsky, Sasha;Ruiz-Irastorza, Guillermo;Khamashta, Munther A.;Jacobsen, Soren;Buyon, Jill P.;Maddison, Peter;Dooley, Mary Anne;van vollenhoven, Ronald F.;Ginzler, Ellen;Stoll, Thomas;Peschken, Christine;Jorizzo, Joseph L.;Callen, Jeffrey P.;Lim, S. Sam;Fessler, Barri J.;Inanc, Murat;Kamen, Diane L.;Rahman, Anisur;Steinsson, Kristjan;Franks, Andrew G., Jr.;Sigler, Lisa;Hameed, Suhail;Fang, Hong;Ngoc Pham;Brey, Robin;Weisman, Michael H.;McGwin, Gerald, Jr.;Magder, Laurence S.
通讯作者：
Magder, Laurence S.
影响因子：
13.3
作者：
Mosca, Marta;Costenbader, Karen H.;Touma, Zahi
通讯作者：
Touma, Zahi
影响因子：
27.4
作者：
Bruce IN;O'Keeffe AG;Farewell V;Hanly JG;Manzi S;Su L;Gladman DD;Bae SC;Sanchez-Guerrero J;Romero-Diaz J;Gordon C;Wallace DJ;Clarke AE;Bernatsky S;Ginzler EM;Isenberg DA;Rahman A;Merrill JT;Alarcón GS;Fessler BJ;Fortin PR;Petri M;Steinsson K;Dooley MA;Khamashta MA;Ramsey-Goldman R;Zoma AA;Sturfelt GK;Nived O;Aranow C;Mackay M;Ramos-Casals M;van Vollenhoven RF;Kalunian KC;Ruiz-Irastorza G;Lim S;Kamen DL;Peschken CA;Inanc M;Urowitz MB
通讯作者：
Urowitz MB
影响因子：
168.9
作者：
Lisnevskaia, Larissa;Murphy, Grainne;Isenberg, David
通讯作者：
Isenberg, David
影响因子：
13.3
作者：
Aringer, Martin;Costenbader, Karen;Johnson, Sindhu R.
通讯作者：
Johnson, Sindhu R.