Automated Ascertainment of Typhlitis From the Electronic Health Record

Miller TP, Li Y, Masino AJ, Vallee E, Burrows E, Ramos M, Alonzo TA, Gerbing R, Castellino SM, Hawkins DS, Lash TL, Aplenc R, Grundmeier RW. Automated Ascertainment of Typhlitis From the Electronic Health Record. JCO Clin Cancer Inform. 2022 Sep;6:e2200081. doi: 10.1200/CCI.22.00081. PubMed PMID: 36198128; PubMed Central PMCID: PMC9848554.

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. 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.