PATIENTS AND METHODS: Clinical characteristics and electrophysiological data of sixty-one consecutive patients admitted between 2012 and 2015 were systematically analysed and reclassified according to the new GBS clinical classification. Neurophysiology was evaluated with Hadden et al.'s vs recently proposed Rajabally et al.'s criteria. Functional severity and clinical outcome of various GBS subtypes were ascertained.
RESULTS: All patients initially identified as GBS or related disorders can be sub-classified into having classical GBS (41, 67%), classic Miller-Fisher Syndrome (MFS) (6, 10%), Pharyngeal-cervical-brachial (PCB) (3, 5%), paraparetic GBS (4, 7%), bifacial weakness with paresthesia (3, 5%), acute ophthalmoparesis (AO) (1, 2%) and overlap syndrome (3, 5%): one (2%) with GBS/Bickerstaff brainstem encephalitis overlap and 2 (3%) with GBS/MFS overlap. Greater proportion of axonal classical GBS (67% vs 55%, p=0.372) seen with Rajabally et al.'s criteria and a predominantly axonal form of paraparetic variant (75%) independent of electrodiagnostic criteria were more representative of Asian GBS cohort. Classical GBS patients had lowest admission and discharge Medical Research Council Sum Score (MRCSS), greater functional disability and longest length of in-patient stay. Twenty (20/21, 95%) patients who needed mechanical ventilation had classical GBS. Patients required repeated dose of intravenous immunoglobulin (5/6, 3%) or plasma exchange (4/4, 100%) more frequently had axonal form of classical GBS.
CONCLUSION: Phenotype recognition based on new GBS clinical classification, supported by electrodiagnostic study permits more precise clinical subtypes determination and outcome prognostication.
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.