O123 Optimizing the electrodiagnostic accuracy in Guillain–Barré syndrome subtypes: Sparse linear discriminant analysis versus criteria sets

Abstract

Objective To optimize the electrodiagnostic accuracy for Guillain-Barre (GBS) subtypes at the first test and define the criteria for reversible conduction failure (RCF). Methods The reference electrodiagnosis was obtained in 53 demyelinating and 45 axonal GBS patients on the basis of two serial studies and results of anti-ganglioside antibodies assay. We retrospectively employed sparse linear discriminant analysis (SLDA), a supervised statistical method of classification, two existing electrodiagnostic criteria sets (Hadden et al., 1988; Rajabally et al., 2015) and one we propose that additionally evaluates duration of motor responses, sural sparing pattern and RCF in motor and sensory nerves at the second study. Results At first study the misclassification error rates, compared to reference diagnoses, were: 15.3% for sparse SLDA, 30 % for our criteria, 45% for Rajabally’s and 48% for Hadden’s criteria. SLDA identified seven most powerful electrophysiological variables differentiating demyelinating and axonal subtypes and assigned to each patient the diagnostic probabilityof belonging to either subtype. At second test 46.6% of axonal GBS patients showed RCF in two motor and 8.8% in two sensory nerves. Discussion and conclusions Basing on a single test, SLDA showed the highest diagnostic accuracy. Anyway RCF is present in a considerable percentage of axonal GBS patients and can be assessed only with serial tests. Significance Algorithms for SLDA are already available in several statistical packages and even the electromyographic machines could be easily equipped with the appropriated algorithms providing the physician with a decision support system for the diagnosis of neuropathies.