Song Yanna, Liu Shan, Qiu Wei, Liu Kangding, Zhang Hong-Liang
Department of Neurology, First Hospital of Jilin University, Jilin University, Changchun, China.
Department of Neurology, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong Province, China.
Heliyon. 2024 May 1;10(9):e30524. doi: 10.1016/j.heliyon.2024.e30524. eCollection 2024 May 15.
Respiratory failure requiring mechanical ventilation (MV) is a common and severe complication of Guillain-Barré syndrome (GBS) with a reported incidence ranging from 20 % to 30 %. Thus, we aim to develop a nomogram to evaluate the risk of MV in patients with GBS at admission and tailor individualized care and treatment.
A total of 633 patients with GBS (434 in the training set, and 199 in the validation set) admitted to the First Hospital of Jilin University, Changchun, China from January 2010 to January 2021 were retrospectively enrolled. Subjects (n = 71) from the same institution from January 2021 to May 2022 were prospectively collected and allocated to the testing set. Multivariable logistic regression analysis was applied to build a predictive model incorporating the optimal features selected in the least absolute shrinkage and selection operator (LASSO) in the training set. The predictive model was validated using internal bootstrap resampling, an external validation set, and a prospective testing set, and the model's performance was assessed by using the concordance index (C-index), calibration curves, and decision curve analysis (DCA). Finally, we established a multivariable logistic model by using variables of the Erasmus GBS Respiratory Insufficiency Score (EGRIS) and did the same analysis to compare the performance of our predictive model with the EGRIS model.
Variables in the final model selected by LASSO included time from onset to admission, facial and/or bulbar weakness, Medical Research Council sum score at admission, neutrophil-to-lymphocyte ratio, and platelet-lymphocyte ratio. The model presented as a nomogram displaying favorable discriminative ability with a C-index of 0.914 in the training set, 0.903 in the internal validation set, 0.953 in the external validation set, and 0.929 in the testing set. The model was well-calibrated and clinically useful as assessed by the calibration curve and DCA. As compared with the EGRIS model, our predictive model displayed satisfactory performance.
We constructed a nomogram for early prediction of the risk of MV in patients with GBS. This model had satisfactory performance and appeared more efficient than the EGRIS model in Chinese patients with GBS.
需要机械通气（MV）的呼吸衰竭是吉兰-巴雷综合征（GBS）常见且严重的并发症，报道的发病率在20%至30%之间。因此，我们旨在开发一种列线图，以评估GBS患者入院时发生MV的风险，并制定个性化的护理和治疗方案。
回顾性纳入2010年1月至2021年1月在中国长春吉林大学第一医院收治的633例GBS患者（训练集434例，验证集199例）。前瞻性收集2021年1月至2022年5月来自同一机构的受试者（n = 71）并分配到测试集。应用多变量逻辑回归分析建立预测模型，纳入训练集中在最小绝对收缩和选择算子（LASSO）中选择的最佳特征。使用内部自助重采样、外部验证集和前瞻性测试集对预测模型进行验证，并通过一致性指数（C指数）、校准曲线和决策曲线分析（DCA）评估模型性能。最后，我们使用伊拉斯谟GBS呼吸功能不全评分（EGRIS）的变量建立了多变量逻辑模型，并进行了相同的分析，以比较我们的预测模型与EGRIS模型的性能。
LASSO选择的最终模型中的变量包括起病至入院时间、面部和/或延髓肌无力、入院时医学研究委员会总分、中性粒细胞与淋巴细胞比值以及血小板与淋巴细胞比值。该模型以列线图形式呈现，在训练集中C指数为0.914，内部验证集中为0.903，外部验证集中为0.953，测试集中为0.929，显示出良好的判别能力。通过校准曲线和DCA评估，该模型校准良好且具有临床实用性。与EGRIS模型相比，我们的预测模型表现令人满意。
我们构建了一个列线图用于早期预测GBS患者发生MV的风险。该模型表现令人满意，在中国GBS患者中似乎比EGRIS模型更有效。
