{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T09:53:38Z","timestamp":1785405218465,"version":"3.56.0"},"reference-count":16,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T00:00:00Z","timestamp":1778803200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100002429","name":"Amgen","doi-asserted-by":"publisher","award":["N\/A"],"award-info":[{"award-number":["N\/A"]}],"id":[{"id":"10.13039\/100002429","id-type":"DOI","asserted-by":"publisher"}]},{"award":["N\/A"],"award-info":[{"award-number":["N\/A"]}],"id":[{"id":"https:\/\/ror.org\/03g03ge92","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"abstract":"<jats:p>Objectives: To validate claims-based algorithms for identifying thyroid eye disease (TED) cases and assess whether machine learning can improve case identification in a large, linked real-world dataset. Methods: Using a large, linked database from Komodo Health\u00ae and Academy IRIS\u00ae Registry, we evaluated six rule-based algorithms incorporating Graves\u2019 disease (GD), eye symptoms and signs. The IRIS Registry\u2019s curated data, based on confirmed TED diagnoses from medical notes, served as the reference standard. Additionally, we developed supervised machine learning models using demographic, diagnostic, procedural, and medication data. Feature selection was performed using recursive feature elimination to rank predictive codes and construct a simplified, interpretable model. Cross-validation was used to assess model performance and compare performance with the rule-based algorithms. Results: The rule-based algorithms demonstrated a trade-off between sensitivity and specificity, with some achieving high specificity but limited sensitivity. Algorithm 1 had the highest sensitivity (48.7%) but lower specificity (59.9%) and PPV (75.8%). Algorithms 2\u20135 demonstrated higher specificity (87.2\u201393.5%) but lower sensitivity (17.8\u201327.0%). Algorithm 6 improved sensitivity (33.4%) compared to Algorithms 2\u20135 while maintaining high specificity (86.8%) and a strong PPV (86.7%). Machine learning models demonstrated similar trade-offs. One model achieved improved specificity (77.2%) with sensitivity of 49.3%, outperforming Algorithm 1 in specificity while matching its sensitivity. Another model maximized specificity (91.7%) and PPV (89.8%) at a reduced sensitivity of 28.5%. These results highlight the flexibility of machine learning models in adjusting performance to address different research objectives. Conclusions: This study evaluated existing rule-based algorithms for identifying TED cases in claims data, revealing trade-offs between sensitivity and specificity. Machine learning models provide additional flexibility, allowing performance to be tailored to specific research use cases. While no single method consistently outperformed others across all metrics, both rule-based and machine learning approaches demonstrated value in improving TED case identification using real-world data sources.<\/jats:p>","DOI":"10.3390\/jcm15103836","type":"journal-article","created":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T12:36:40Z","timestamp":1779107800000},"page":"3836","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Validation and Development of Claims-Based Algorithms for Identifying Thyroid Eye Disease Using the IRIS Registry-Komodo Linked Database"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6249-876X","authenticated-orcid":false,"given":"Junjie","family":"Ma","sequence":"first","affiliation":[{"name":"Center for Observational Research, Amgen, Inc., Thousand Oaks, CA 91320, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wendy W.","family":"Lee","sequence":"additional","affiliation":[{"name":"Bascom Palmer Eye Institute, Miller School of Medicine, University of Miami, Miami, FL 33136, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maurice Alan","family":"Brookhart","sequence":"additional","affiliation":[{"name":"Department of Population Health Sciences, Duke University School of Medicine, Durham, NC 27701, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Madhura A.","family":"Tamhankar","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Juan","family":"Ayala-Haedo","sequence":"additional","affiliation":[{"name":"Medical Affairs, Amgen, Inc., Thousand Oaks, CA 91320, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4185-9079","authenticated-orcid":false,"given":"Fang","family":"He","sequence":"additional","affiliation":[{"name":"Center for Observational Research, Amgen, Inc., Thousand Oaks, CA 91320, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1827-5163","authenticated-orcid":false,"given":"Haridarshan","family":"Patel","sequence":"additional","affiliation":[{"name":"Center for Observational Research, Amgen, Inc., Thousand Oaks, CA 91320, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,5,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Bartalena, L., Piantanida, E., Gallo, D., Lai, A., and Tanda, M.L. 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