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March 5, 2026Journal of Clinical Medicine1 citationsOpen Access

Patient-Level Modeling of Ménière’s Disease vs. Vestibular Migraine: Performance of Speech Discrimination and Caloric-vHIT Dissociation

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NPNicolás Pérez-FernándezLALorea Arbizu

Key Points

  • This research aims to determine if speech discrimination scores can differentiate Ménière’s disease from vestibular migraine and assess if caloric-vHIT dissociation improves this differentiation.
  • Conducted a retrospective cohort study including patients with definite Ménière’s disease and vestibular migraine.
  • Trained logistic regression models with 5-fold validation, excluding pure-tone thresholds to avoid incorporation bias.
  • Assessed discrimination performance using ROC-AUC and implemented decision-curve analysis.
  • The SDS-only model achieved an AUC of 0.866, indicating strong discrimination between conditions.
  • CalHiT-A alone had a lower AUC of 0.674, showing weaker performance.
  • Combining SDS with CalHiT-A did not enhance diagnostic power, with AUC at 0.844.
  • CalHiT-A was significantly more common in Ménière’s disease cases compared to vestibular migraine.

Abstract

Background: Differentiating Ménière’s disease (MD) from vestibular migraine (VM) remains difficult because current diagnostic frameworks are predominantly clinical and incorporate pure-tone thresholds, risking incorporation bias. We asked whether speech discrimination scores (SDS) alone can separate MD from VM at the patient level and whether adding a prespecified vestibular marker, the caloric–vHIT dissociation, pattern A (abnormal calorics with normal horizontal vHIT), improves performance. Methods: In a retrospective cohort (2015–2018) including definite MD (n = 60) and definite VM (n = 40) by Bárány/ICHD criteria, we trained patient-level logistic regression models with 5-fold out-of-fold validation and in-fold preprocessing. To avoid incorporation bias, PTA was excluded from all models. Predefined feature sets were as follows: (1) SDS-only (bilateral SDS), (2) CalHiT-A-only (Yes/No; canal paresis ≥22% with horizontal-canal vHIT gain ≥0.80 in either ear), and (3) SDS+CalHiT-A. Discrimination was assessed by ROC–AUC with bootstrap 95% CIs; calibration and decision-curve analysis (DCA) are reported. An exploratory model encoded SDS as “affected/healthy.” Results: The SDS-only model achieved AUC 0.866 (95% CI 0.787–0.937). CalHiT-A-only yielded AUC 0.674 (0.561–0.778). Adding CalHiT-A to SDS did not improve discrimination (SDS+CalHiT-A AUC 0.844 0.760–0.913). The exploratory “affected/healthy” SDS encoding underperformed (AUC 0.801 0.706–0.882). CalHiT-A was significantly more prevalent in MD than in VM (56.7% 34/60 vs. 17.5% 7/40; Fisher’s exact p = 1.49 × 10−4). Calibration favored SDS-only, and DCA showed the highest net benefit for SDS-only across thresholds p = 0.05–0.40. Conclusions: Bilateral SDS alone provides robust, well-calibrated discrimination between MD and VM and outperforms CalHiT-A and the affected/healthy SDS encoding. In this cohort, vestibular test dissociation did not add diagnostic value beyond SDS at the patient level, supporting SDS-centered diagnostic workflows while reserving CalHiT-A for adjudication and phenotyping rather than primary classification.

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Cite This Study

Pérez-Fernández et al. (2026) studied this question.

synapsesocial.com/papers/69a91de0d6127c7a504c12c8https://doi.org/10.3390/jcm15051908
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