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A model to predict the probability of acute inflammatory demyelinating polyneuropathy
Cheng-Yin Tan1, Yukari Sekiguchi2, Khean-Jin Goh1
1Division of Neurology, Department of Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia.
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.
