Department of Neurology, West China Hospital, Sichuan University, Chengdu, P.R. China.
Department of Neurology, Seventh People's Hospital of Chengdu, Chengdu, P.R. China.
Acta Neurol Scand. 2020 Nov;142(5):466-474. doi: 10.1111/ane.13294. Epub 2020 Jun 23.
Guillain-Barré syndrome (GBS) is one of the most common causes of acute flaccid paralysis, with up to 20%-30% of patients requiring mechanical ventilation. The aim of our study was to develop and validate a mechanical ventilation risk nomogram in a Chinese population of patients with GBS.
A total of 312 GBS patients were recruited from January 1, 2015, to June 31, 2018, of whom 17% received mechanical ventilation. The least absolute shrinkage and selection operator (LASSO) regression model was used to select clinicodemographic characteristics and blood markers that were then incorporated, using multivariate logistic regression, into a risk model to predict the need for mechanical ventilation. The model was characterized and assessed using the C-index, calibration plot, and decision curve analysis. The model was validated using bootstrap resampling in a prospective study of 114 patients recruited from July 1, 2018, to July 10, 2019.
The predictive model included hospital stay, glossopharyngeal and vagal nerve deficits, Hughes functional grading scale scores at admission, and neutrophil/lymphocyte ratio (NLR). The model showed good discrimination with a C-index value of 0.938 and good calibration. A high C-index value of 0.856 was reached in the validation group. Decision curve analysis demonstrated the clinical utility of the mechanical ventilation nomogram.
A nomogram incorporating hospital stay, glossopharyngeal and vagal nerve deficits, Hughes functional grading scale scores at admission, and NLR may reliably predict the probability of requiring mechanical ventilation in GBS patients.
吉兰-巴雷综合征（GBS）是急性弛缓性瘫痪的最常见原因之一，多达 20%-30%的患者需要机械通气。我们的研究目的是开发和验证中国 GBS 患者人群的机械通气风险列线图。
共纳入 2015 年 1 月 1 日至 2018 年 6 月 31 日的 312 例 GBS 患者，其中 17%接受了机械通气。使用最小绝对收缩和选择算子（LASSO）回归模型选择临床和实验室特征以及血液标志物，然后使用多变量逻辑回归将其纳入预测机械通气需求的风险模型。使用 C 指数、校准图和决策曲线分析对模型进行特征描述和评估。使用 2018 年 7 月 1 日至 2019 年 7 月 10 日前瞻性纳入的 114 例患者的 bootstrap 重采样对模型进行验证。
预测模型包括住院时间、舌咽和迷走神经缺陷、入院时的 Hughes 功能分级量表评分和中性粒细胞/淋巴细胞比值（NLR）。该模型具有良好的区分度，C 指数值为 0.938，校准良好。验证组的 C 指数值达到 0.856。决策曲线分析表明，机械通气列线图具有临床实用性。
包含住院时间、舌咽和迷走神经缺陷、入院时的 Hughes 功能分级量表评分和 NLR 的列线图可可靠预测 GBS 患者需要机械通气的概率。
