Construction and evaluation of a prognostic prediction model based on the mEGOS score for patients with Guillain-Barré syndrome

Gaojie Xue; Yani Zhang; Ruochen Wang; Yue Yang; Huihui Wang; Jiangping Li; Xuexian He; Qing Zhang; Xiao Yang

doi:10.3389/fneur.2023.1303243

Construction and evaluation of a prognostic prediction model based on the mEGOS score for patients with Guillain-Barré syndrome

Front Neurol. 2023 Nov 30:14:1303243. doi: 10.3389/fneur.2023.1303243. eCollection 2023.

Authors

Gaojie Xue^#¹, Yani Zhang^#¹, Ruochen Wang¹, Yue Yang¹, Huihui Wang², Jiangping Li², Xuexian He³, Qing Zhang^#¹, Xiao Yang^#¹

Affiliations

¹ Department of Neurology, General Hospital of Ningxia Medical University, Yinchuan, China.
² School of Public Health and Management, Ningxia Medical University, Yinchuan, China.
³ Department of Cerebrospinal Fluid Laboratory, General Hospital of Ningxia Medical University, Yinchuan, China.

^# Contributed equally.

Abstract

Background: Guillain-Barré syndrome (GBS) is an immune-mediated acute peripheral neuropathy in which up to 20% patients remain unable to walk independently after 6 months of onset. This study aimed to develop a clinical prognostic model based on the modified Erasmus GBS Outcome Score (mEGOS) for predicting the prognosis of GBS patients at 6 months of onset.

Methods: The clinical data of 201 GBS patients were retrospectively analyzed. According to the GBS disability score (GBS-DS) at 6 months of onset, patients were divided into a good prognosis group (GBS-DS <3 points) and a poor prognosis group (GBS-DS≥3 points). Univariate and multivariate analysis was used to screen out independent risk factors for poor prognosis, and a prediction model was accordingly constructed for GBS prognosis.

Results: The mEGOS score, serum albumin (ALB) and fasting plasma glucose (FPG) were independent risk factors for poor prognosis in patients with GBS, and the above risk factors were used to construct a prognostic model of mEGOS-I and a nomogram. The receiver operating characteristic (ROC) curve showed that the area under curve (AUC) of mEGOS-I at admission and at 7 days of admission to predict poor prognosis at 6 months of GBS onset was 0.891 and 0.916, respectively, with sensitivities of 82.7% and 82.6% and specificities of 86.5% and 86.6%, respectively. Decision curve analysis showed that the nomogram had a very high clinical benefit.

Conclusion: To our knowledge, this is the first report of the construction of a prognostic prediction model based on the mEGOS score, ALB, and FPG that can accurately and stably predict the prognosis of GBS patients at 6 months of onset.

Keywords: Guillain-Barré syndrome; albumin; fasting plasma glucose; mEGOS score; prognosis.

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was supported by grants from the 2021 Innovation and Entrepreneurship Project for Returned Overseas researchers in Ningxia (2021-352). The role of the funding body was to provide financial support.