{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T22:59:24Z","timestamp":1767913164408,"version":"3.49.0"},"reference-count":21,"publisher":"Wiley","issue":"11","license":[{"start":{"date-parts":[[2023,6,23]],"date-time":"2023-06-23T00:00:00Z","timestamp":1687478400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Pharmacoepidemiology and Drug"],"published-print":{"date-parts":[[2023,11]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Purpose<\/jats:title><jats:p>Improved hypereosinophilic syndrome (HES) ascertainment in electronic health record (EHR) databases may improve disease understanding and management. An algorithm to ascertain and characterize this rare condition was therefore developed and validated.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Using the UK clinical practice research datalink (CPRD)\u2010Aurum database linked to the hospital episode statistics database (<jats:italic>Admitted Patient Care data<\/jats:italic>) from Jan 2012 to June 2019, this cross\u2010sectional study ascertained patients with a specific HES code (index). Patients with HES were matched (age, sex and index date) 1:29 with a non\u2010HES cohort. An algorithm was developed by identifying pre\u2010defined variables differing between cohorts; model\u2010fitting using Firth logistic regression and statistical determination of the top\u2010five performing models; and internal validation using Leave\u2010One\u2010Out Cross Validation. Final model sensitivity and specificity were determined at an 80% probability threshold.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The HES and non\u2010HES cohorts included 88 and 2552 patients, respectively; 270 models with four variables each (treatment used for HES, asthma code, white blood cell condition code, and blood eosinophil count [BEC] code) plus age and sex variables were tested. Of the top five models, the sensitivity model performed best (sensitivity, 69% [95% CI: 59%, 79%]; specificity, &gt;99%). The strongest predictors of HES versus non\u2010HES cases (odds &gt;1000 times greater) were an ICD\u201010 code for white blood cell disorders and a BEC \u22651500\u2009cells\/\u03bcL in the 24\u2009months pre\u2010index.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Using a combination of medical codes, prescribed treatments data and laboratory results, the algorithm can help ascertain patients with HES from EHR databases; this approach may be useful for other rare diseases.<\/jats:p><\/jats:sec>","DOI":"10.1002\/pds.5655","type":"journal-article","created":{"date-parts":[[2023,6,13]],"date-time":"2023-06-13T04:05:57Z","timestamp":1686629157000},"page":"1261-1270","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["An algorithm\u2010based approach to ascertain patients with rare diseases in electronic health records using hypereosinophilic syndrome as an example"],"prefix":"10.1002","volume":"32","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1889-6656","authenticated-orcid":false,"given":"Gema","family":"Requena","sequence":"first","affiliation":[{"name":"Epidemiology, Value Evidence and Outcomes, Global Research &amp; Development GSK  Brentford Middlesex UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sandra","family":"Joksaite","sequence":"additional","affiliation":[{"name":"Real\u2010World Analytics, Value Evidence and Outcomes, Global Research &amp; Development GSK  Brentford Middlesex UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nicholas","family":"Galwey","sequence":"additional","affiliation":[{"name":"Real World Analytics, Epidemiology GSK  Stevenage UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rupert W.","family":"Jakes","sequence":"additional","affiliation":[{"name":"Epidemiology, Value Evidence and Outcomes, Global Research &amp; Development GSK  Brentford Middlesex UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2023,6,23]]},"reference":[{"key":"e_1_2_13_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.iac.2007.07.004"},{"key":"e_1_2_13_3_1","doi-asserted-by":"publisher","DOI":"10.1007\/s12016-015-8506-7"},{"key":"e_1_2_13_4_1","doi-asserted-by":"publisher","DOI":"10.1182\/blood-2014-11-551614"},{"key":"e_1_2_13_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jaci.2009.09.022"},{"key":"e_1_2_13_6_1","doi-asserted-by":"publisher","DOI":"10.1002\/ajh.25617"},{"key":"e_1_2_13_7_1","doi-asserted-by":"publisher","DOI":"10.3389\/fmed.2017.00216"},{"key":"e_1_2_13_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jaci.2010.03.035"},{"key":"e_1_2_13_9_1","unstructured":"2022 ICD\u201010\u2010CM diagnosis code D72.11 Hypereosinophilic syndrome [HES]. 2022 ICD10Data. 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