Electrodiagnostic data-driven clustering identifies a prognostically different subgroup of patients with chronic inflammatory demyelinating polyneuropathy. Issue 6 (23rd March 2019)

• Record Type: Journal Article
• Authors: Baek, Seol-Hee; Hong, Yoon-Ho; Choi, Seok-Jin; Ahn, So Hyun; Park, Kee Hong; Shin, Je-Young; Sung, Jung-Joon
• Is Part Of: Journal of neurology, neurosurgery and psychiatry. Volume 90:Issue 6(2019)
• Page Start: 674
• Page End: 680
• Publication Date: 2019-03-23
• Subjects: chronic inflammatory demyelinating polyneuropathy -- electrodiagnosis -- clustering -- treatment; Neurology -- Periodicals; Nervous system -- Surgery -- Periodicals; Psychiatry -- Periodicals; 616.8
• DOI: 10.1136/jnnp-2018-319758
• Languages: English
• ISSNs: 0022-3050

Abstract

Abstract : Objective: This study aimed to explore the correlations between electrodiagnostic (EDX) features in patients with chronic inflammatory demyelinating polyneuropathy (CIDP) and to investigate whether EDX data-driven clustering can identify a distinct subgroup regarding clinical phenotype and treatment response. Methods: We reviewed clinical and EDX data of 56 patients with definite CIDP fulfilling the 2010 European Federation of Neurological Societies and Peripheral Nerve Society criteria at two teaching hospitals. A hierarchical agglomerative clustering algorithm with complete linkage was used to partition the patients into subgroups with similar EDX features. A stepwise logistic regression analysis was performed to evaluate predictors of the long-term outcome. Results: EDX data-driven clustering partitioned the patients into two clusters, identifying a distinct subgroup characterised by coexistence of prominent conduction slowing and markedly reduced distally evoked compound muscle action potential (CMAP) amplitudes. This cluster of patients was significantly over-represented by an atypical subtype (distal acquired demyelinating symmetric polyneuropathy) compared with the other cluster (70% vs 26.1%, p=0.042). Furthermore, patients in this cluster invariably showed favourable long-term treatment outcome (100% vs 63%, p=0.023). In logistic regression analyses, the initial disability (OR 6.1, 95% CI 2.4 to 25.4), F-wave latency (OR 0.93, 95% CI 0.86 to 0.98) and distal CMAP duration (OR 0.96, 95% CI 0.91 to 0.99) were significant predictors of the poor long-term outcome. Conclusion: Our results show that EDX data-driven clustering could differentiate a pattern of EDX features with prognostic implication in patients with CIDP. Reduced distally evoked CMAPs may not necessarily predict poor responses to treatment, and active treatment is warranted when prominent slowing of conduction is accompanied in the distal segments.