Li Gongfei, Liu Xiao, Wang Minghui, Yu Tingting, Ren Jiechuan, Wang Qun
Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China.
Acta Neurol Scand. 2022 Aug;146(2):137-143. doi: 10.1111/ane.13619. Epub 2022 Apr 4.
To establish a model in order to predict the functional outcomes of patients with anti-leucine-rich glioma-inactivated 1 (LGI1) encephalitis and identify significant predictive factors using a random forest algorithm.
Seventy-nine patients with confirmed LGI1 antibodies were retrospectively reviewed between January 2015 and July 2020. Clinical information was obtained from medical records and functional outcomes were followed up in interviews with patients or their relatives. Neurological functional outcome was assessed using a modified Rankin Scale (mRS), the cutoff of which was 2. The prognostic model was established using the random forest algorithm, which was subsequently compared with logistic regression analysis, Naive Bayes and Support vector machine (SVM) metrics based on the area under the curve (AUC) and the accuracy.
A total of 79 patients were included in the final analysis. After a median follow-up of 24 months (range, 8-60 months), 20 patients (25%) experienced poor functional outcomes. A random forest model consisting of 16 variables used to predict the poor functional outcomes of anti-LGI1 encephalitis was successfully constructed with an accuracy of 83% and an F1 score of 60%. In addition, the random forest algorithm demonstrated a more precise predictive performance for poor functional outcomes in patients with anti-LGI1 encephalitis compared with three other models (AUC, 0.90 vs 0.80 vs 0.70 vs 0.64).
The random forest model can predict poor functional outcomes of patients with anti-LGI1 encephalitis. This model was more accurate and reliable than the logistic regression, Naive Bayes, and SVM algorithm.
建立一个模型，以预测抗富含亮氨酸胶质瘤失活1（LGI1）脑炎患者的功能预后，并使用随机森林算法识别重要的预测因素。
回顾性分析2015年1月至2020年7月期间79例确诊为LGI1抗体的患者。从病历中获取临床信息，并通过对患者或其亲属的访谈随访功能预后。使用改良Rankin量表（mRS）评估神经功能预后，其临界值为2。使用随机森林算法建立预后模型，随后基于曲线下面积（AUC）和准确性与逻辑回归分析、朴素贝叶斯和支持向量机（SVM）指标进行比较。
最终分析共纳入79例患者。中位随访24个月（范围8 - 60个月）后，20例患者（25%）功能预后较差。成功构建了一个由16个变量组成的随机森林模型，用于预测抗LGI1脑炎患者的功能预后较差，准确率为83%，F1分数为60%。此外，与其他三种模型相比，随机森林算法在预测抗LGI1脑炎患者功能预后较差方面表现出更精确的预测性能（AUC分别为0.90、0.80、0.70和0.64）。
随机森林模型可以预测抗LGI1脑炎患者的功能预后较差。该模型比逻辑回归、朴素贝叶斯和支持向量机算法更准确可靠。
