8. Predicting motor disorders from nerve excitability studies. Issue 4 (April 2018) - Li, T.; Howells, J.; Lin, C.S.Y.; Garg, N.; Kiernan, M.C.; Park, S.B.
Abstract : Objective: To investigate whether statistical models of nerve excitability study (NES) parameters are able to distinguish between motor disorders with overlapping clinical features. Methods: 81 motor NES in median nerve were undertaken in patients (55 males, mean age: 55.9 ± 13.1) with clinically diagnosed motor neuron disease (MND; N = 25), chronic inflammatory demyelinating polyneuropathy (CIDP; N = 32), multifocal motor neuropathy (MMN; N = 13) and Kennedy's disease (KD; N = 11). Multinomial regression models were built using the Akaike information criterion (AIC) method to balance model complexity with goodness of fit and likelihood ratio tests to identify which NES output variables make the best predictive model. Results: The final model was able to predict a patient's diagnosis of MND (sensitivity 68%, specificity 87%), CIDP (sensitivity 77%, specificity 82%), MMN (sensitivity 88%, specificity 94%) and KD (sensitivity 41%, specificity 96%) incorporating the NES output variables of TEd undershoot, superexcitability (7 ms), subexcitability, stimulus threshold, latency and gender. Conclusions: Motor NES profiles differ between motor disorders, enabling statistical modelling to separate different groups of patients with motor disorders. Significance: Statistical modelling of NES data may aid clinicians in the differential diagnosis of motor disorders.