{"title":"基于临床和实验室指标的格林-巴勒综合征鉴别诊断模型的建立和验证：一项回顾性研究","authors":"Wencan Jiang, Xiaotong Li, Yifei Wang, Chenxu Wang, Panpan Feng, Xiaoxuan Yin, Xin Luan, Yaowei Ding, Haoran Li, Kelin Chen, Siwen Li, Lijuan Wang, Yuxin Chen, Guojun Zhang","doi":"10.1155/ane/2317870","DOIUrl":null,"url":null,"abstract":"<p><b>Objective:</b> This study is aimed at developing a differential diagnostic model for Guillain–Barré syndrome (GBS) from other central nervous system diseases based on clinical and laboratory indicators.</p><p><b>Materials and Methods:</b> A retrospective approach was conducted for the GBS patients and patients with other neurological diseases (non-GBS group, including viral encephalitis, peripheral neuropathy, multiple sclerosis, transverse myelitis, neuromyelitis optica spectrum disorders, and myasthenia gravis). The least absolute shrinkage and selection operator (LASSO) technique was integrated with multivariable logistic regression to perform predictor selection. The logistic regression model was established as the predictive framework, followed by the application of the Shapley additive explanation (SHAP) framework to quantify contributions of selected variables within the model. After that, patient data were collected for model validation.</p><p><b>Results:</b> A total of 161 patients with GBS and 644 patients with non-GBS diseases were enrolled. Upper limb weakness, visual impairment, areflexia, hyperreflexia, total bilirubin (TBIL), mean corpusular hemoglobin (MCH), platelet large cell ratio (P-LCR), cerebral spinal fluid–protein (CSF-protein), dyslipidemia index, and oligoclonal band-serum/cerebral spinal fluid (SOB-CSF) emerged as independent predictors of GBS development. The logistic regression classifier demonstrated robust predictive performance, achieving an area under the curve (AUC) of 0.915 in the testing set, with an accuracy of 0.876, sensitivity of 0.823, and specificity of 0.889.</p><p><b>Conclusion:</b> We developed and validated a logistic regression model incorporating multiple clinical indicators to differentiate GBS from other inflammatory neurological disorders (including MS, NMOSD, MG, TM, VE, and PN). The model demonstrated high diagnostic accuracy (AUC 0.92), supporting its potential as a supplementary tool for clinical decision-making.</p>","PeriodicalId":6939,"journal":{"name":"Acta Neurologica Scandinavica","volume":"2025 1","pages":""},"PeriodicalIF":2.7000,"publicationDate":"2025-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/ane/2317870","citationCount":"0","resultStr":"{\"title\":\"Development and Validation of a Differential Diagnostic Models for Guillain–Barré Syndrome Based on Clinical and Laboratory Indicators: A Retrospective Study\",\"authors\":\"Wencan Jiang, Xiaotong Li, Yifei Wang, Chenxu Wang, Panpan Feng, Xiaoxuan Yin, Xin Luan, Yaowei Ding, Haoran Li, Kelin Chen, Siwen Li, Lijuan Wang, Yuxin Chen, Guojun Zhang\",\"doi\":\"10.1155/ane/2317870\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><b>Objective:</b> This study is aimed at developing a differential diagnostic model for Guillain–Barré syndrome (GBS) from other central nervous system diseases based on clinical and laboratory indicators.</p><p><b>Materials and Methods:</b> A retrospective approach was conducted for the GBS patients and patients with other neurological diseases (non-GBS group, including viral encephalitis, peripheral neuropathy, multiple sclerosis, transverse myelitis, neuromyelitis optica spectrum disorders, and myasthenia gravis). The least absolute shrinkage and selection operator (LASSO) technique was integrated with multivariable logistic regression to perform predictor selection. The logistic regression model was established as the predictive framework, followed by the application of the Shapley additive explanation (SHAP) framework to quantify contributions of selected variables within the model. After that, patient data were collected for model validation.</p><p><b>Results:</b> A total of 161 patients with GBS and 644 patients with non-GBS diseases were enrolled. Upper limb weakness, visual impairment, areflexia, hyperreflexia, total bilirubin (TBIL), mean corpusular hemoglobin (MCH), platelet large cell ratio (P-LCR), cerebral spinal fluid–protein (CSF-protein), dyslipidemia index, and oligoclonal band-serum/cerebral spinal fluid (SOB-CSF) emerged as independent predictors of GBS development. The logistic regression classifier demonstrated robust predictive performance, achieving an area under the curve (AUC) of 0.915 in the testing set, with an accuracy of 0.876, sensitivity of 0.823, and specificity of 0.889.</p><p><b>Conclusion:</b> We developed and validated a logistic regression model incorporating multiple clinical indicators to differentiate GBS from other inflammatory neurological disorders (including MS, NMOSD, MG, TM, VE, and PN). The model demonstrated high diagnostic accuracy (AUC 0.92), supporting its potential as a supplementary tool for clinical decision-making.</p>\",\"PeriodicalId\":6939,\"journal\":{\"name\":\"Acta Neurologica Scandinavica\",\"volume\":\"2025 1\",\"pages\":\"\"},\"PeriodicalIF\":2.7000,\"publicationDate\":\"2025-08-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1155/ane/2317870\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Acta Neurologica Scandinavica\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1155/ane/2317870\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"CLINICAL NEUROLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Acta Neurologica Scandinavica","FirstCategoryId":"3","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1155/ane/2317870","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CLINICAL NEUROLOGY","Score":null,"Total":0}
Development and Validation of a Differential Diagnostic Models for Guillain–Barré Syndrome Based on Clinical and Laboratory Indicators: A Retrospective Study
Objective: This study is aimed at developing a differential diagnostic model for Guillain–Barré syndrome (GBS) from other central nervous system diseases based on clinical and laboratory indicators.
Materials and Methods: A retrospective approach was conducted for the GBS patients and patients with other neurological diseases (non-GBS group, including viral encephalitis, peripheral neuropathy, multiple sclerosis, transverse myelitis, neuromyelitis optica spectrum disorders, and myasthenia gravis). The least absolute shrinkage and selection operator (LASSO) technique was integrated with multivariable logistic regression to perform predictor selection. The logistic regression model was established as the predictive framework, followed by the application of the Shapley additive explanation (SHAP) framework to quantify contributions of selected variables within the model. After that, patient data were collected for model validation.
Results: A total of 161 patients with GBS and 644 patients with non-GBS diseases were enrolled. Upper limb weakness, visual impairment, areflexia, hyperreflexia, total bilirubin (TBIL), mean corpusular hemoglobin (MCH), platelet large cell ratio (P-LCR), cerebral spinal fluid–protein (CSF-protein), dyslipidemia index, and oligoclonal band-serum/cerebral spinal fluid (SOB-CSF) emerged as independent predictors of GBS development. The logistic regression classifier demonstrated robust predictive performance, achieving an area under the curve (AUC) of 0.915 in the testing set, with an accuracy of 0.876, sensitivity of 0.823, and specificity of 0.889.
Conclusion: We developed and validated a logistic regression model incorporating multiple clinical indicators to differentiate GBS from other inflammatory neurological disorders (including MS, NMOSD, MG, TM, VE, and PN). The model demonstrated high diagnostic accuracy (AUC 0.92), supporting its potential as a supplementary tool for clinical decision-making.
期刊介绍：
Acta Neurologica Scandinavica aims to publish manuscripts of a high scientific quality representing original clinical, diagnostic or experimental work in neuroscience. The journal''s scope is to act as an international forum for the dissemination of information advancing the science or practice of this subject area. Papers in English will be welcomed, especially those which bring new knowledge and observations from the application of therapies or techniques in the combating of a broad spectrum of neurological disease and neurodegenerative disorders. Relevant articles on the basic neurosciences will be published where they extend present understanding of such disorders. Priority will be given to review of topical subjects. Papers requiring rapid publication because of their significance and timeliness will be included as ''Clinical commentaries'' not exceeding two printed pages, as will ''Clinical commentaries'' of sufficient general interest. Debate within the speciality is encouraged in the form of ''Letters to the editor''. All submitted manuscripts falling within the overall scope of the journal will be assessed by suitably qualified referees.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
