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Related Experiment Video

Updated: Jun 17, 2026

Semi-Automated Analysis of Peak Amplitude and Latency for Auditory Brainstem Response Waveforms Using R
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Semi-Automated Analysis of Peak Amplitude and Latency for Auditory Brainstem Response Waveforms Using R

Published on: December 9, 2022

Predicting abnormal auditory brainstem response outcomes using risk factors.

Maria Leno1, Sima Sharghi2, Julie L Wei3

  • 1Division of Otolaryngology/Audiology, Akron Children's Hospital, 1 Perkins Square, Akron, OH, 44308, USA.

International Journal of Pediatric Otorhinolaryngology
|June 15, 2026
PubMed
Summary

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This summary is machine-generated.

A new predictive model helps identify children at higher risk for abnormal auditory brainstem response (ABR) testing. This can optimize operating room use and reduce wait times for pediatric hearing loss diagnosis.

Area of Science:

  • Pediatric Audiology
  • Medical Informatics

Background:

  • Auditory brainstem response (ABR) testing is crucial for diagnosing hearing loss in non-cooperative children.
  • Current ABR scheduling uses fixed 60-minute operating room (OR) blocks, leading to inefficiency and long wait times (average 82 days).
  • A significant portion of ABR tests (63%) show normal hearing and complete quickly, indicating potential for optimized scheduling.

Purpose of the Study:

  • To develop and validate a predictive model for identifying children at higher risk of abnormal ABR results.
  • To improve operating room (OR) block utilization and patient access to ABR testing.
  • To reduce wait times for pediatric hearing loss evaluations.

Main Methods:

  • A retrospective study of 239 children undergoing sedated ABR testing was conducted.
Keywords:
Auditory brainstem responseClinical decision supportHearing lossPediatric audiologyPredictive modelingRisk stratification

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  • Clinical risk factors (e.g., autism, NICU stay, syndrome diagnosis, UNHS referral) were extracted from electronic health records.
  • Logistic regression was employed, with models evaluated for discrimination, calibration, and clinical utility.

    • Main Results:

    • The predictive model showed moderate discrimination (AUC ≈ 0.68).
    • Universal newborn hearing screening (UNHS) referral and syndrome diagnosis were associated with increased ABR risk; autism diagnosis with decreased risk.
    • At a 35% risk threshold, the model identified 53% of abnormal ABR cases with 75% specificity.

    Conclusions:

    • A pre-test risk stratification model can effectively identify children at higher risk for abnormal ABR.
    • Implementing such a model has the potential to enhance OR block utilization, improve patient access, and streamline workflow efficiency.