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.
- chronic inflammatory demyelinating polyneuropathy
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S-HB and Y-HH contributed equally.
Contributors S-HB: acquisition and statistical analysis of data and drafting of the manuscript. Y-HH: study concept and design, clustering analysis, statistical analysis of data and drafting of the manuscript. S-JC: acquisition of data and clustering analysis. SHA, KHP and J-YS: acquisition of data. J-JS: study concept and design and critical revision of the manuscript.
Funding This study was supported by grants from the Korea Healthcare Technology R&D project, Ministry of Health and Welfare, Republic of Korea (HI14C3347) and the National Research Foundation of Korea (NRF) grant funded by the Korean Government (MSIP) (NRF-2018R1A5A2025964).
Competing interests None declared.
Patient consent for publication Not required.
Ethics approval This study was approved by the institutional review boards of Seoul National University Hospital (IRB H-1603-096-750) and Seoul Metropolitan Government Boramae Medical Center (IRB 16-2016-57).
Provenance and peer review Not commissioned; externally peer reviewed.
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