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A model to predict the probability of acute inflammatory demyelinating polyneuropathy.
Clin Neurophysiol. 2020 01; 131(1):63-69.CN

Abstract

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.

Links

Publisher Full Text (DOI)

Authors+Show Affiliations

Tan CY
Division of Neurology, Department of Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia.
Sekiguchi Y
Department of Neurology, Graduate School of Medicine, Chiba University, Chiba, Japan.
Goh KJ
Division of Neurology, Department of Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia.
Kuwabara S
Department of Neurology, Graduate School of Medicine, Chiba University, Chiba, Japan.
Shahrizaila N
Division of Neurology, Department of Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia. Electronic address: nortina@um.edu.my.

MeSH

AdolescentAdultAgedArea Under CurveDiagnosis, DifferentialFemaleGuillain-Barre SyndromeHumansMalaysiaMaleMedian NerveMiddle AgedModels, NeurologicalNeural ConductionProbabilityRegression AnalysisReproducibility of ResultsSural NerveTime FactorsUlnar NerveYoung Adult

Pub Type(s)

Journal Article
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

31751842

Citation

Tan, Cheng-Yin, et al. "A Model to Predict the Probability of Acute Inflammatory Demyelinating Polyneuropathy." Clinical Neurophysiology : Official Journal of the International Federation of Clinical Neurophysiology, vol. 131, no. 1, 2020, pp. 63-69.
Tan CY, Sekiguchi Y, Goh KJ, et al. A model to predict the probability of acute inflammatory demyelinating polyneuropathy. Clin Neurophysiol. 2020;131(1):63-69.
Tan, C. Y., Sekiguchi, Y., Goh, K. J., Kuwabara, S., & Shahrizaila, N. (2020). A model to predict the probability of acute inflammatory demyelinating polyneuropathy. Clinical Neurophysiology : Official Journal of the International Federation of Clinical Neurophysiology, 131(1), 63-69. https://doi.org/10.1016/j.clinph.2019.09.025
Tan CY, et al. A Model to Predict the Probability of Acute Inflammatory Demyelinating Polyneuropathy. Clin Neurophysiol. 2020;131(1):63-69. PubMed PMID: 31751842.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - A model to predict the probability of acute inflammatory demyelinating polyneuropathy. AU - Tan,Cheng-Yin, AU - Sekiguchi,Yukari, AU - Goh,Khean-Jin, AU - Kuwabara,Satoshi, AU - Shahrizaila,Nortina, Y1 - 2019/11/04/ PY - 2019/06/03/received PY - 2019/07/25/revised PY - 2019/09/11/accepted PY - 2019/11/22/pubmed PY - 2020/7/7/medline PY - 2019/11/22/entrez KW - Acute inflammatory demyelinating polyneuropathy KW - Acute motor axonal neuropathy KW - Axonal GBS KW - Electrodiagnostic criteria KW - Guillain-Barré syndrome KW - Predictive model SP - 63 EP - 69 JF - Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology JO - Clin Neurophysiol VL - 131 IS - 1 N2 - 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. SN - 1872-8952 UR - https://www.unboundmedicine.com/medline/citation/31751842/A_model_to_predict_the_probability_of_acute_inflammatory_demyelinating_polyneuropathy_ DB - PRIME DP - Unbound Medicine ER -