Time-Continuous Prognostic Mapping of Bell's Palsy Using Yanagihara Scores and Electroneurography.

Area of Science:

• Neurology
• Medical Imaging and Diagnostics
• Biostatistics

Background:

• Bell's palsy significantly impacts social functioning, necessitating accurate prognostic assessment.
• Current methods like clinical grading and electroneurography (ENoG) have limited predictive accuracy.
• A dynamic, individualized approach is needed for better prognosis in Bell's palsy.

Purpose of the Study:

• To develop and validate a time-continuous probabilistic model for Bell's palsy prognosis.
• To integrate longitudinal Yanagihara scores and ENoG for dynamic, individualized recovery estimation.
• To enhance the accuracy of prognostic assessment beyond conventional methods.

Main Methods:

• Retrospective longitudinal study of 128 Bell's palsy patients (≥15 years) treated with corticosteroids.
• Utilized longitudinal Yanagihara scores and early electroneurography (ENoG) values.
• Employed multivariable logistic regression and Monte Carlo/Bayesian procedures for recovery probability maps.

Main Results:

• Electroneurography (ENoG) was the sole significant predictor of recovery.
• Generated novel probability maps for individualized, time-continuous prognosis visualization.
• The model accurately estimated recovery likelihood even with irregular follow-up intervals.

Conclusions:

• Integrating longitudinal Yanagihara scores improves Bell's palsy recovery prediction accuracy.
• The dynamic, probabilistic approach offers intuitive risk visualization for clinicians.
• This method supports enhanced prognostic counseling and patient management in clinical practice.