{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T23:16:58Z","timestamp":1793575018084,"version":"4.5.0"},"reference-count":41,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2025,8,16]],"date-time":"2025-08-16T00:00:00Z","timestamp":1755302400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,8,16]],"date-time":"2025-08-16T00:00:00Z","timestamp":1755302400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"The Ohio State University Clinical and Translational Science Institute","award":["UL1TR002733"],"award-info":[{"award-number":["UL1TR002733"]}]},{"name":"National Center for Advancing Translational Sciences of the National Institutes of Health","award":["UL1TR002733"],"award-info":[{"award-number":["UL1TR002733"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Clin Rheumatol"],"published-print":{"date-parts":[[2025,10]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:sec>\n            <jats:title>Objectives<\/jats:title>\n            <jats:p>To develop a predictive algorithm for diagnosing chronic pediatric rheumatic conditions using patient-reported, historical, and referral data in the electronic health record (EHR) to address current lengthy consultation wait times.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Methods<\/jats:title>\n            <jats:p>All new rheumatology patient evaluations from 2021 to 2023 were retrospectively reviewed to identify the reason for the visit, patient-recorded outcomes, and international classification of disease codes. The data sample was randomly split into 80% derivation and 20% validation sets. Logistic regression evaluated the association of diagnosis and referral data; variables with <jats:italic>p<\/jats:italic>\u2009&lt;\u20090.2 in univariate were included in a multivariate model. Complete data are reported.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>Of the 3139 subjects, 2064 (66%) were female, with a median age of 13 [IQR 8, 16]. Patients diagnosed with inflammatory arthritis numbered 319 (10%), while 55 (2%) were diagnosed with systemic lupus erythematosus (SLE). The median time from the first visit to diagnosing inflammatory arthritis and SLE was 88\u00a0days [35, 210] and 42 [17, 132], respectively. In univariate analysis, a referral reason for swelling was positively associated with a new inflammatory arthritis diagnosis. In contrast, antinuclear antibody positivity, rash, and lupus were positively associated with a new SLE diagnosis. Referral data had low sensitivity and high specificity for both inflammatory arthritis and SLE diagnoses, with areas under the curve of 0.59 and 0.65, respectively.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusion<\/jats:title>\n            <jats:p>Utilizing the EHR to create a predictive algorithm for chronic rheumatic disease presents a promising solution to existing patient care challenges. This approach suggests that integrating such models to the referral process could help expedite access to pediatric rheumatology services.\n<jats:table-wrap>\n                <jats:table>\n                  <jats:tbody>\n                    <jats:tr>\n                      <jats:td colspan=\"2\">\n                        <jats:bold>Key Points<\/jats:bold>\u2022 <jats:italic>Patient referrals to pediatric rheumatology specialists often lead to non-rheumatic diagnosis.<\/jats:italic>\u2022 <jats:italic>Patient-reported data within the electronic health record can be utilized to predict likelihood of rheumatic disease.<\/jats:italic>\u2022 <jats:italic>Electronic algorithms to predict rheumatic disease could expedite patient care access to pediatric rheumatology, which currently has a physician shortage and potentially long wait times.<\/jats:italic>\n                      <\/jats:td>\n                    <\/jats:tr>\n                  <\/jats:tbody>\n                <\/jats:table>\n              <\/jats:table-wrap>\n            <\/jats:p>\n          <\/jats:sec>","DOI":"10.1007\/s10067-025-07631-5","type":"journal-article","created":{"date-parts":[[2025,8,15]],"date-time":"2025-08-15T23:58:20Z","timestamp":1755302300000},"page":"4203-4214","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Utilizing electronic health record pre-consultation data to create a predictive algorithm for diagnosis of chronic pediatric rheumatic conditions"],"prefix":"10.1007","volume":"44","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-0998-4503","authenticated-orcid":false,"given":"Kendra R.","family":"Lauer","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6514-4182","authenticated-orcid":false,"given":"Kyla","family":"Driest","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9990-9032","authenticated-orcid":false,"given":"Laura R.","family":"Pratt","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2429-116X","authenticated-orcid":false,"given":"Alysha","family":"Taxter","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,8,16]]},"reference":[{"issue":"8","key":"7631_CR1","doi-asserted-by":"crossref","first-page":"1439","DOI":"10.1002\/art.23087","volume":"57","author":"JJ Sacks","year":"2007","unstructured":"Sacks JJ et al (2007) Prevalence of and annual ambulatory health care visits for pediatric arthritis and other rheumatologic conditions in the United States in 2001\u20132004. 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