{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,22]],"date-time":"2026-06-22T18:26:31Z","timestamp":1782152791952,"version":"3.54.5"},"reference-count":42,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2019,11,26]],"date-time":"2019-11-26T00:00:00Z","timestamp":1574726400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"name":"National Institutes of Health grant numbers","award":["NHLBI U01HL121518"],"award-info":[{"award-number":["NHLBI U01HL121518"]}]},{"name":"National Institutes of Health grant numbers","award":["NHLBI L40HL133929"],"award-info":[{"award-number":["NHLBI L40HL133929"]}]},{"name":"National Institutes of Health grant numbers","award":["NICHD T32HD040128"],"award-info":[{"award-number":["NICHD T32HD040128"]}]},{"name":"National Institutes of Health grant numbers","award":["NICHD K12HD047349"],"award-info":[{"award-number":["NICHD K12HD047349"]}]},{"name":"National Institutes of Health grant numbers","award":["NLM R01LM010090"],"award-info":[{"award-number":["NLM R01LM010090"]}]},{"name":"National Institutes of Health grant numbers","award":["NCATS U01TR002623"],"award-info":[{"award-number":["NCATS U01TR002623"]}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,2,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Objective<\/jats:title><jats:p>Real-world data (RWD) are increasingly used for pharmacoepidemiology and regulatory innovation. Our objective was to compare adverse drug event (ADE) rates determined from two RWD sources, electronic health records and administrative claims data, among children treated with drugs for pulmonary hypertension.<\/jats:p><\/jats:sec><jats:sec><jats:title>Materials and Methods<\/jats:title><jats:p>Textual mentions of medications and signs\/symptoms that may represent ADEs were identified in clinical notes using natural language processing. Diagnostic codes for the same signs\/symptoms were identified in our electronic data warehouse for the patients with textual evidence of taking pulmonary hypertension-targeted drugs. We compared rates of ADEs identified in clinical notes to those identified from diagnostic code data. In addition, we compared putative ADE rates from clinical notes to those from a healthcare claims dataset from a large, national insurer.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Analysis of clinical notes identified up to 7-fold higher ADE rates than those ascertained from diagnostic codes. However, certain ADEs (eg, hearing loss) were more often identified in diagnostic code data. Similar results were found when ADE rates ascertained from clinical notes and national claims data were compared.<\/jats:p><\/jats:sec><jats:sec><jats:title>Discussion<\/jats:title><jats:p>While administrative claims and clinical notes are both increasingly used for RWD-based pharmacovigilance, ADE rates substantially differ depending on data source.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Pharmacovigilance based on RWD may lead to discrepant results depending on the data source analyzed. Further work is needed to confirm the validity of identified ADEs, to distinguish them from disease effects, and to understand tradeoffs in sensitivity and specificity between data sources.<\/jats:p><\/jats:sec>","DOI":"10.1093\/jamia\/ocz194","type":"journal-article","created":{"date-parts":[[2019,10,21]],"date-time":"2019-10-21T19:19:52Z","timestamp":1571685592000},"page":"294-300","source":"Crossref","is-referenced-by-count":23,"title":["Adverse drug event rates in pediatric pulmonary hypertension: a comparison of real-world data sources"],"prefix":"10.1093","volume":"27","author":[{"given":"Alon","family":"Geva","sequence":"first","affiliation":[{"name":"Computational Health Informatics Program, Boston Children\u2019s Hospital, Boston, Massachusetts, USA"},{"name":"Division of Critical Care Medicine, Department of Anesthesiology, Critical Care, and Pain Medicine, Boston Children\u2019s Hospital, Boston, Massachusetts, USA"},{"name":"Department of Anaesthesia, Harvard Medical 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