A model to predict the probability of acute inflammatory demyelinating polyneuropathy

• Published in: Clinical Neurophysiology
• Main Authors: Tan, Cheng Yin, Sekiguchi, Yukari, Goh, Khean Jin, Kuwabara, Satoshi, Shahrizaila, Nortina
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: Elsevier 2020
• Subjects: R Medicine; DML
• Online Access: http://eprints.um.edu.my/24837/; https://doi.org/10.1016/j.clinph.2019.09.025

Objective: We aimed to develop a model that can predict the probabilities of acute inflammatory demyelinating polyneuropathy (AIDP) based on nerve conduction studies (NCS) done within eight weeks. 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. © 2019 International Federation of Clinical Neurophysiology