ArticleAnnals of clinical and translational neurology2024
Radiologic grading scores enhance clinical model's prognostic ability for Guillain-Barré syndrome.
Article in Annals of clinical and translational neurology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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7 authors at 3 institutions in 1 country.
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Abstract
objectiveTo assess the value of magnetic resonance imaging (MRI) grading scores based on lumbosacral muscle denervation edema in predicting the course of Guillain-Barré syndrome (GBS).
methodsWe collected data from 354 GBS patients and developed MRI grading criteria (5-point scale) based on the transverse area and longitudinal length of lumbosacral edema. Univariate and multivariate logistic regression analyses were conducted to identify factors associated with GBS prognosis among 12 demographic and radiological features. Clinical models and clinical-MRI models were separately trained and validated by data from Institution 1. External test was performed using data from Institution 2. Differences between the models were assessed using the z-test.
resultsFour clinical factors (sex, albumin cytological dissociation in cerebrospinal fluid, medical research council [MRC] sum score at admission, and MRC sum score at discharge [odds ratio, 0.24-5.15; all p < 0.001]) and MRI grading scores (odds ratio, 2.44; p < 0.001) are independent prognostic factors for GBS patients. The shallow neural network achieved the best prognostic performance both clinical model (accuracy of external test cohort, 83.96%) and clinical-MRI model (accuracy of external test cohort, 90.56%). A significant difference between clinical and clinical-MRI model was also found (clinical model vs. clinical-MRI model, area under the receiver operating curve, 0.84 (95% CI: [0.71, 0.91]) vs. 0.97 (95% CI: [0.86, 0.99]), p < 0.001).
interpretationThe MRI grading scores for muscle denervation edema may serve as a potential prognostic risk factor for GBS. Furthermore, they significantly improve the prognostic performance of standalone clinical model in predicting GBS prognosis.
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