Predicting the functional outcomes of anti-LGI1 encephalitis using a random forest model

IF 2.9 3区医学 Q2 CLINICAL NEUROLOGY

Acta Neurologica Scandinavica Pub Date : 2022-04-04 DOI:10.1111/ane.13619

Gongfei Li, Xiao Liu, Minghui Wang, Tingting Yu, Jiechuan Ren, Qun Wang

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引用次数: 0

Abstract

Objectives

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.

Methods

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.

Results

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).

Conclusions

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.

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使用随机森林模型预测抗lgi1脑炎的功能结局

目的建立抗-富亮氨酸胶质瘤失活1 (LGI1)脑炎患者功能预后预测模型，并利用随机森林算法识别显著预测因素。方法回顾性分析2015年1月~ 2020年7月确诊的79例LGI1抗体患者。从医疗记录中获得临床信息，并通过与患者或其亲属的访谈跟踪功能结果。神经功能预后采用改良Rankin量表(mRS)评估，其截止值为2。采用随机森林算法建立预测模型，并根据曲线下面积(AUC)和准确率与logistic回归分析、朴素贝叶斯和支持向量机(SVM)指标进行比较。结果79例患者纳入最终分析。中位随访24个月(8-60个月)后，20例患者(25%)出现功能不良。我们成功构建了一个由16个变量组成的随机森林模型，用于预测抗lgi1脑炎的不良功能结局，准确率为83%，F1评分为60%。此外，与其他三种模型相比，随机森林算法对抗lgi1脑炎患者的不良功能结局具有更精确的预测性能(AUC, 0.90 vs 0.80 vs 0.70 vs 0.64)。结论随机森林模型可以预测抗lgi1脑炎患者的不良功能结局。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。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Acta Neurologica Scandinavica 医学-临床神经学

CiteScore

6.70

自引率

2.90%

发文量

161

审稿时长

4-8 weeks

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.