Predicting falls in multiple sclerosis: Do electrophysiological measures have a better predictive accuracy compared...

Area of Science:

• Neurology
• Neurophysiology

Background:

• People with Multiple Sclerosis (MS) experience a significantly higher risk of falls due to neurological impairments affecting balance, coordination, and mobility.
• Falls in MS patients exacerbate disease progression, increase dependency, and negatively impact quality of life.
• Accurate prediction of fall risk is essential for implementing timely preventive strategies, as current clinical tests are suboptimal.

Purpose of the Study:

• To identify precise measures for predicting future falls in patients with Multiple Sclerosis (MS).
• To investigate the prevalence and clinical characteristics of falls within the MS population.
• To evaluate the predictive accuracy of various clinical and neurophysiological tests for falls in MS.

Main Methods:

• A prospective cohort study involving 113 Multiple Sclerosis (MS) patients was conducted over two years.
• Baseline data included fall history, Expanded Disability Status Scale (EDSS), Timed 25-foot Walk (T25FW), and balance confidence questionnaires (ABC, FESI, MSWS12).
• Neurophysiological tests, including P13/N23 cervical VEMP, N10 oVEMP, and P37 lower limb SEP latencies, were recorded. Patients were followed for one year to document falls.

Main Results:

• A high fall prevalence was observed, with 72% of patients experiencing at least one fall during the one-year follow-up.
• P13 cervical VEMP latency demonstrated the highest predictive accuracy (AUC = 0.820) for future falls.
• N10 ocular VEMP latency (AUC = 0.794) and P37 SEP latency (AUC = 0.732) also showed significant predictive value.

Conclusions:

• Vestibular Evoked Myogenic Potential (VEMP) and Somatosensory Evoked Potential (SEP) latencies are highly accurate predictors of future falls in Multiple Sclerosis (MS) patients.
• P13 VEMP latency, N10 VEMP latency, and P37 SEP latency significantly outperform traditional clinical measures in fall prediction.
• These neurophysiological measures offer a promising tool for early identification of high-risk MS individuals, enabling targeted interventions.