{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T00:57:28Z","timestamp":1785200248525,"version":"3.55.0"},"reference-count":29,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2022,3,11]],"date-time":"2022-03-11T00:00:00Z","timestamp":1646956800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Muscle and Nerve"],"published-print":{"date-parts":[[2022,6]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Introduction\/Aims<\/jats:title><jats:p>Administrative health data has been increasingly used to study the epidemiology of myasthenia gravis (MG) but a case ascertainment algorithm is lacking. We aimed to develop a valid algorithm for identifying MG patients in the older population with Medicare coverage.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Local older patients (age \u226565) who received healthcare at the Cleveland Clinic and possessed Medicare coverage in 2014 and 2015 were selected. Potential MG patients were identified by using a combination of ICD9 or ICD10 codes for MG and MG\u2010related text\u2010word search. Diagnosis was categorized as \u201cdefinite MG\u201d, \u201cpossible MG\u201d or \u201cnon\u2010MG\u201d after review of clinical summaries by 5 neuromuscular specialists. Performances of various algorithms were tested by use of the definite MG cohort as a reference standard, and calculation of sensitivity, specificity, and predictive values.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>A total of 118\u2009988 local older patients with Medicare coverage were identified. Usage of MG ICD codes and text\u2010word search resulted in 125 patients with definite and 67 with possible MG. A total of 45 algorithms involving ICD usage, medication prescription, and specialty visit were tested. The best performing algorithm was identified as 2 office visits using MG ICD codes separated by at least 4\u2009weeks or 1 hospital discharge and 1 office visit each using MG ICD codes separated by at least 4\u2009weeks within the two\u2010year period, resulting in a sensitivity and positive predictive value of 80% for identifying definite MG patients.<\/jats:p><\/jats:sec><jats:sec><jats:title>Discussion<\/jats:title><jats:p>Algorithms using ICD codes can reliably identify patients with MG with a high degree of accuracy.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mus.27526","type":"journal-article","created":{"date-parts":[[2022,2,26]],"date-time":"2022-02-26T07:16:45Z","timestamp":1645859805000},"page":"676-682","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Validation of myasthenia gravis diagnosis in the older Medicare population"],"prefix":"10.1002","volume":"65","author":[{"given":"Ikjae","family":"Lee","sequence":"first","affiliation":[{"name":"The Neurological Institute of New York Columbia University  New York New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jesse D.","family":"Schold","sequence":"additional","affiliation":[{"name":"Department of Quantitative Health Sciences Cleveland Clinic  Cleveland Ohio USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael K.","family":"Hehir","sequence":"additional","affiliation":[{"name":"Department of Neurological Sciences University of Vermont Larner College of Medicine  Burlington Vermont USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Benjamin","family":"Claytor","sequence":"additional","affiliation":[{"name":"Department of Neurology, Neurological Institute Clevelnd Clinic  Cleveland Ohio USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7931-0930","authenticated-orcid":false,"given":"Nicholas","family":"Silvestri","sequence":"additional","affiliation":[{"name":"Department of Neurology, Jacobs School of Medicine and Biomedical Sciences University at Buffalo\/SUNY  Buffalo New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4248-8927","authenticated-orcid":false,"given":"Yuebing","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Neurology, Neurological Institute Clevelnd Clinic  Cleveland Ohio USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2022,3,11]]},"reference":[{"key":"e_1_2_11_2_1","doi-asserted-by":"publisher","DOI":"10.1212\/01.WNL.0000050461.05432.C5"},{"issue":"6","key":"e_1_2_11_3_1","first-page":"572","article-title":"Increase in incidence of elderly\u2010onset 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