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 (LDA), two existing electrodiagnostic criteria sets (Hadden et al., 1998; Rajabally et al., 2015) and one we propose that additionally evaluates duration of motor responses, sural sparing pattern and defines reversible conduction failure (RCF) in motor and sensory nerves at second study.
RESULTS: At first study the misclassification error rates, compared to reference diagnoses, were: 15.3% for sparse LDA, 30% for our criteria, 45% for Rajabally's and 48% for Hadden's. Sparse LDA identified seven most powerful electrophysiological variables differentiating demyelinating and axonal subtypes and assigned to each patient the diagnostic probability of belonging to either subtype. At second study 46.6% of axonal GBS patients showed RCF in two motor and 8.8% in two sensory nerves.
CONCLUSIONS: Based on a single study, sparse LDA showed the highest diagnostic accuracy. RCF is present in a considerable percentage of axonal patients.
SIGNIFICANCE: Sparse LDA, a supervised statistical method of classification, should be introduced in the electrodiagnostic practice.
METHODS: The derivation cohort included 90 Malaysian GBS patients with two sets of NCS performed early (1-20days) and late (3-8 weeks). Potential predictors of AIDP were considered in univariate and multivariate logistic regression models to develop a predictive model. The model was externally validated in 102 Japanese GBS patients.
RESULTS: Median motor conduction velocity (MCV), ulnar distal motor latency (DML) and abnormal ulnar/normal sural pattern were independently associated with AIDP at both timepoints (median MCV: p = 0.038, p = 0.014; ulnar DML: p = 0.002, p = 0.003; sural sparing: p = 0.033, p = 0.009). There was good discrimination of AIDP (area under the curve (AUC) 0.86-0.89) and this was valid in the validation cohort (AUC 0.74-0.94). Scores ranged from 0 to 6, and corresponded to AIDP probabilities of 15-98% at early NCS and 6-100% at late NCS.
CONCLUSION: The probabilities of AIDP could be reliably predicted based on median MCV, ulnar DML and ulnar/sural sparing pattern that were determined at early and late stages of GBS.
SIGNIFICANCE: A simple and valid model was developed which can accurately predict the probability of AIDP.
METHODS: Prospectively collected clinical and EDx data were available in 957 IGOS patients from 115 centers. Only the first EDx study was included in the current analysis.
RESULTS: Median timing of the EDx study was 7 days (interquartile range 4-11) from symptom onset. Methodology varied between centers, countries and regions. Reference values from the responding 103 centers were derived locally in 49%, from publications in 37% and from a combination of these in the remaining 15%. Amplitude measurement in the EDx studies (baseline-to-peak or peak-to-peak) differed from the way this was done in the reference values, in 22% of motor and 39% of sensory conduction. There was marked variability in both motor and sensory reference values, although only a few outliers accounted for this.
CONCLUSIONS: Our study showed extensive variation in the clinical practice of EDx in GBS patients among IGOS centers across the regions.
SIGNIFICANCE: Besides EDx variation in GBS patients participating in IGOS, this diversity is likely to be present in other neuromuscular disorders and centers. This underlines the need for standardization of EDx in future multinational GBS studies.