{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T13:52:13Z","timestamp":1753883533419,"version":"3.41.2"},"reference-count":19,"publisher":"American Society of Clinical Oncology (ASCO)","issue":"6","content-domain":{"domain":["ascopubs.org"],"crossmark-restriction":true},"short-container-title":["JCO Clinical Cancer Informatics"],"published-print":{"date-parts":[[2022,9]]},"abstract":"<jats:sec><jats:title>PURPOSE<\/jats:title><jats:p>Adverse events (AEs) on Children's Oncology Group (COG) trials are manually ascertained using Common Terminology Criteria for Adverse Events. Despite significant effort, we previously demonstrated that COG typhlitis reporting sensitivity was only 37% when compared with gold standard physician chart abstraction. This study tested an automated typhlitis identification algorithm using electronic health record data.<\/jats:p><\/jats:sec><jats:sec><jats:title>METHODS<\/jats:title><jats:p>Electronic health record data from children with leukemia age 0-22 years treated at a single institution from 2006 to 2019 were included. Patients were divided into derivation and validation cohorts. Rigorous chart abstraction of validation cohort patients established a gold standard AE data set. We created an automated algorithm to identify typhlitis matching Common Terminology Criteria for Adverse Events v5 that included antibiotics, neutropenia, and non-negated mention of typhlitis in a note. We iteratively refined the algorithm using the derivation cohort and then applied the algorithm to the validation cohort; performance was compared with the gold standard. For patients on trial AAML1031, COG AE report performance was compared with the gold standard.<\/jats:p><\/jats:sec><jats:sec><jats:title>RESULTS<\/jats:title><jats:p>The derivation cohort included 337 patients. The validation cohort included 270 patients (961 courses). Chart abstraction identified 16 courses with typhlitis. The algorithm identified 37 courses with typhlitis; 13 were true positives (sensitivity 81.3%, positive predictive value 35.1%). For patients on AAML1031, chart abstraction identified nine courses with typhlitis, and COG reporting correctly identified 4 (sensitivity 44.4%, positive predictive value 100.0%).<\/jats:p><\/jats:sec><jats:sec><jats:title>CONCLUSION<\/jats:title><jats:p>The automated algorithm identified true cases of typhlitis with higher sensitivity than COG reporting. The algorithm identified false positives but reduced the number of courses needing manual review by 96% (961 to 37) by detecting potential typhlitis. This algorithm could provide a useful screening tool to reduce manual effort required for typhlitis AE reporting.<\/jats:p><\/jats:sec>","DOI":"10.1200\/cci.22.00081","type":"journal-article","created":{"date-parts":[[2022,10,5]],"date-time":"2022-10-05T19:59:29Z","timestamp":1664999969000},"update-policy":"https:\/\/doi.org\/10.1200\/crossmark","source":"Crossref","is-referenced-by-count":1,"title":["Automated Ascertainment of Typhlitis From the Electronic Health Record"],"prefix":"10.1200","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4250-5376","authenticated-orcid":false,"given":"Tamara P.","family":"Miller","sequence":"first","affiliation":[{"name":"Aflac Cancer and Blood Disorders Center, Children's Healthcare of Atlanta, Atlanta, GA"},{"name":"Department of Pediatrics, Emory University School of Medicine, Atlanta, 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