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

<b>Background</b>: 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. <b>Methods</b>: 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." <b>Results</b>: 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 <i>p</i> = 1.49 × 10<sup>-4</sup>). Calibration favored SDS-only, and DCA showed the highest net benefit for SDS-only across thresholds <i>p</i> = 0.05-0.40. <b>Conclusions</b>: 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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Publication Details

Journal
Journal of Clinical Medicine
Published
2026-03-03
DOI
https://doi.org/10.3390/jcm15051908
Primary Topic
Vestibular and auditory disorders
Type
article
Field-Weighted Citation Impact
0.00

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article

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

Lorea Arbizu, Nicolás Pérez-Fernández
Journal of Clinical Medicine
Vestibular and auditory disorders
article

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

Lorea Arbizu, Nicolás Pérez-Fernández
article en

Abstract

<b>Background</b>: 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. <b>Methods</b>: 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." <b>Results</b>: 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 <i>p</i> = 1.49 × 10<sup>-4</sup>). Calibration favored SDS-only, and DCA showed the highest net benefit for SDS-only across thresholds <i>p</i> = 0.05-0.40. <b>Conclusions</b>: 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.

Journal of Clinical MedicineVol. 15(5)
Complejo Hospitalario de Navarra (ES), Clinica Universidad de Navarra (ES), Universidad de Navarra (ES)
Universidad de Navarra
Reduced inequalities
Openalex Percentile: Top 49%
Vestibular and auditory disorders
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