Skip to content
DraconiticResearch
Search Sources API
203,203 studies31 sourcesUpdated just now
Peer-reviewedCC BY 4.0PLOS One

Early life factors documented in electronic health records predict recurrent acute otitis media

  • Jillian H. Hurst1
  • Congwen Zhao2
  • Eileen M. Raynor3
  • Janet W. Lee4
  • Sarah A. Gitomer5
  • Christopher W. Woods6
  • Matthew S. Kelly7
  • Michael J. Smith8
  • Benjamin A. Goldstein9
9 affiliations
  1. 1Division of Infectious Diseases, Department of Pediatrics, Duke University School of Medicine, Durham, North Carolina, United States of America
  2. 2Department of Pediatrics, Children’s Health and Discovery Initiative, Duke University School of Medicine, Durham, North Carolina, United States of America
  3. 3Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, North Carolina, United States of America
  4. 4Department of Head and Neck Surgery & Communication Sciences, Duke University School of Medicine, Durham, North Carolina, United States of America
  5. 5Department of Pediatric Otolaryngology-Head and Neck Surgery, Children’s Hospital Colorado, Aurora, Colorado, United States of America
  6. 6Division of Infectious Diseases, Department of Medicine, Duke University School of Medicine, Durham, North Carolina, United States of America
  7. 7Section of Infectious Diseases, Durham Veterans Affairs Healthcare System, Durham, North Carolina, United States of America
  8. 8Duke Clinical Research Institute, Duke University, Durham, North Carolina, United States of America
  9. 9Section of Infectious Diseases, University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America
Open at sourcePDFDOICite

Abstract

Recurrent acute otitis media (rAOM; defined as ≥3 AOM episodes in 6 months or ≥4 episodes in 12 months) affects 10–15% of children in the United States and is a leading cause of healthcare utilization and antibiotic prescriptions. Prospective identification of children at risk of rAOM could help target interventions and identify new risk factors to guide preventive approaches. We therefore sought to develop predictive models to identify children at risk of rAOM using electronic health records (EHR) data. We extracted retrospective EHR data for children who were born at Duke University Health System (DUHS) hospitals between January 1, 2014, and June 30, 2022, and who had at least one AOM episode during the study period. We used LASSO to build predictive models for development of rAOM at each episode and identified factors associated with rAOM. We identified 6,566 children who met the study criteria, including 1,634 (24.8%) who met criteria for rAOM. A model using only data available at the first AOM episode had an area under the curve (AUC) of 0.75 (0.73, 0.77) and an Area Under the Precision Recall Curve (AUPRC) of 0.41 (95% CI 0.37, 0.46), indicating moderate discriminative ability. At the time of the first AOM episode, features associated with subsequent rAOM development included age, number of prior antibiotic prescriptions, and diagnosis of gastroesophageal reflux disease (GERD). Further, children who developed rAOM were more likely to experience treatment failure than children who did not meet rAOM criteria across all episodes. Our findings indicate that clinical exposures and patient characteristics documented in the EHR distinguish children who are at risk of developing rAOM. Such models could be deployed within EHR systems to identify children who would benefit from early evaluation by an otolaryngologist and audiologist.

Related

  1. Enhancing Spectral Embedding through Robust and Flexible Knowledge Transfer in Electronic Health Records
    Feiqing Huang, Zongqi Xia, Rong Ma +1
    arXivPreprintCC BY 4.0Full textStatisticsarXiv
  2. Toward Automated Detection of Documentation Inconsistencies in Electronic Health Records
    Jian Lu, Panyu Chen, Miriam Treggiari +5
    arXivPreprintCC BY-NC-ND 4.0Full textStatisticsarXiv
  3. Counting the Uncounted: Population-Level Surveillance of Documented Pregnancy and Fetal Harm in Police Crash Narratives with a System One Model (Jev)
    Amir Rafe, Subasish Das
    arXivPreprintarXiv licenceFull textStatisticsarXiv
  4. New Records for the Hadamard Maximal Determinant Problem
    Giorgi Butbaia, Justin Tan, Pragatheeswaran Vipulanandan +6
    arXivPreprintCC BY 4.0Full textCombinatorics & discrete mathematicsarXiv
  5. New Snake-in-the-Box Records via Snakepit Surgery and Learned Construction
    Paul Orland, Lucas Fagan, Michele Tarquini +7
    arXivPreprintCC BY 4.0Full textCombinatorics & discrete mathematicsarXiv

Cite

BibTeXRIS
BibTeX
@article{hurst2026early,
  title = {{Early life factors documented in electronic health records predict recurrent acute otitis media}},
  author = {Jillian H. Hurst and Congwen Zhao and Eileen M. Raynor and Janet W. Lee and Sarah A. Gitomer and Christopher W. Woods and Matthew S. Kelly and Michael J. Smith and Benjamin A. Goldstein},
  journal = {PLOS One},
  year = {2026},
  month = sep,
  doi = {10.1371/journal.pone.0345434},
  url = {https://doi.org/10.1371/journal.pone.0345434},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Early life factors documented in electronic health records predict recurrent acute otitis media
AU  - Jillian H. Hurst
AU  - Congwen Zhao
AU  - Eileen M. Raynor
AU  - Janet W. Lee
AU  - Sarah A. Gitomer
AU  - Christopher W. Woods
AU  - Matthew S. Kelly
AU  - Michael J. Smith
AU  - Benjamin A. Goldstein
PY  - 2026
DA  - 2026/09/18
JO  - PLOS One
DO  - 10.1371/journal.pone.0345434
UR  - https://doi.org/10.1371/journal.pone.0345434
AB  - Recurrent acute otitis media (rAOM; defined as ≥3 AOM episodes in 6 months or ≥4 episodes in 12 months) affects 10–15% of children in the United States and is a leading cause of healthcare utilization and antibiotic prescriptions. Prospective identification of children at risk of rAOM could help target interventions and identify new risk factors to guide preventive approaches. We therefore sought to develop predictive models to identify children at risk of rAOM using electronic health records (EHR) data. We extracted retrospective EHR data for children who were born at Duke University Health System (DUHS) hospitals between January 1, 2014, and June 30, 2022, and who had at least one AOM episode during the study period. We used LASSO to build predictive models for development of rAOM at each episode and identified factors associated with rAOM. We identified 6,566 children who met the study criteria, including 1,634 (24.8%) who met criteria for rAOM. A model using only data available at the first AOM episode had an area under the curve (AUC) of 0.75 (0.73, 0.77) and an Area Under the Precision Recall Curve (AUPRC) of 0.41 (95% CI 0.37, 0.46), indicating moderate discriminative ability. At the time of the first AOM episode, features associated with subsequent rAOM development included age, number of prior antibiotic prescriptions, and diagnosis of gastroesophageal reflux disease (GERD). Further, children who developed rAOM were more likely to experience treatment failure than children who did not meet rAOM criteria across all episodes. Our findings indicate that clinical exposures and patient characteristics documented in the EHR distinguish children who are at risk of developing rAOM. Such models could be deployed within EHR systems to identify children who would benefit from early evaluation by an otolaryngologist and audiologist.
SN  - 1932-6203
ER  -