Background: and Purpose Several variants of Guillain-Barré syndrome (GBS) and Miller Fisher syndrome (MFS) exist, but their frequencies vary in different populations and do not always meet the inclusion criteria of the existing diagnostic criteria. However, the GBS classification criteria by Wakerley and colleagues recognize and define the clinical characteristics of each variant. We applied these criteria to a GBS and MFS cohort with the aim of determining their utility. Methods: Consecutive GBS and MFS patients presenting to our center between 2010 and 2020 were analyzed. The clinical characteristics, electrophysiological data, and antiganglioside antibody profiles of the patients were utilized in determining the clinical classification. Results: This study classified 132 patients with GBS and its related disorders according to the new classification criteria as follows: 64 (48.5%) as classic GBS, 2 (1.5%) as pharyngeal-cervical-brachial (PCB) variant, 7 (5.3%) as paraparetic GBS, 29 (22%) as classic MFS, 3 (2.3%) as acute ophthalmoparesis, 2 (1.5%) as acute ataxic neuropathy, 2 (1.5%) as Bickerstaff brainstem encephalitis (BBE), 17 (12.9%) as GBS/MFS overlap, 4 (3%) as GBS/BBE overlap, 1 (0.8%) as MFS/PCB overlap, and 1 (0.8%) as polyneuritis cranialis. The electrodiagnosis was demyelinating in 55% of classic GBS patients but unclassified in 79% of classic MFS patients. Anti-GM1, anti-GD1a, anti-GalNAc-GD1a, and anti-GD1b IgG ganglioside antibodies were more commonly detected in the axonal GBS subtype, whereas the anti-GQ1b and anti-GT1a IgG ganglioside antibodies were more common in classic MFS and its subtypes. Conclusions Most of the patients in the present cohort met the criteria of either classic GBS or MFS, but variants were seen in one-third of patients. These findings support the need to recognize variants of both syndromes in order to achieve a more-complete case ascertainment in GBS.

Background: and Purpose Several variants of Guillain-Barré syndrome (GBS) and Miller Fisher syndrome (MFS) exist, but their frequencies vary in different populations and do not always meet the inclusion criteria of the existing diagnostic criteria. However, the GBS classification criteria by Wakerley and colleagues recognize and define the clinical characteristics of each variant. We applied these criteria to a GBS and MFS cohort with the aim of determining their utility. Methods: Consecutive GBS and MFS patients presenting to our center between 2010 and 2020 were analyzed. The clinical characteristics, electrophysiological data, and antiganglioside antibody profiles of the patients were utilized in determining the clinical classification. Results: This study classified 132 patients with GBS and its related disorders according to the new classification criteria as follows: 64 (48.5%) as classic GBS, 2 (1.5%) as pharyngeal-cervical-brachial (PCB) variant, 7 (5.3%) as paraparetic GBS, 29 (22%) as classic MFS, 3 (2.3%) as acute ophthalmoparesis, 2 (1.5%) as acute ataxic neuropathy, 2 (1.5%) as Bickerstaff brainstem encephalitis (BBE), 17 (12.9%) as GBS/MFS overlap, 4 (3%) as GBS/BBE overlap, 1 (0.8%) as MFS/PCB overlap, and 1 (0.8%) as polyneuritis cranialis. The electrodiagnosis was demyelinating in 55% of classic GBS patients but unclassified in 79% of classic MFS patients. Anti-GM1, anti-GD1a, anti-GalNAc-GD1a, and anti-GD1b IgG ganglioside antibodies were more commonly detected in the axonal GBS subtype, whereas the anti-GQ1b and anti-GT1a IgG ganglioside antibodies were more common in classic MFS and its subtypes. Conclusions Most of the patients in the present cohort met the criteria of either classic GBS or MFS, but variants were seen in one-third of patients. These findings support the need to recognize variants of both syndromes in order to achieve a more-complete case ascertainment in GBS.