Development of a machine learning model for prognostic prediction of severe sudden sensorineural hearing loss with hyperbaric oxygen therapy.

ABSTRACT:

Background

Severe sudden sensorineural hearing loss (SSNHL) has heterogeneous causes and variable outcomes, making individualized prognosis difficult. We aimed to develop and evaluate a machine-learning (ML) model to predict recovery in severe SSNHL while treating hyperbaric oxygen therapy (HBOT) as an exposure feature rather than inferring causal treatment effects.

Methods

In a single-center retrospective cohort, we analyzed clinical and audiometric data from 231 in patients with severe SSNHL treated between January 2015 and January 2024. Recovery was defined by Siegel's criteria; eligibility required ≥70 dB loss and treatment initiation within 1 month of onset. Candidate predictors included demographics, comorbidities, baseline thresholds, time to treatment, and HBOT varia

SUBMITTER: Koo HB 

PROVIDER: S-EPMC12833283 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature