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基于体液特征区分癫痫与非癫痫性精神障碍事件。

Differentiation of epilepsy and psychogenic nonepileptic events based on body fluid characteristics.

机构信息

Department of Neurology, West China Hospital, Sichuan University, Chengdu, China.

College of Chemistry, Sichuan University, Chengdu, China.

出版信息

Epilepsia Open. 2023 Sep;8(3):959-968. doi: 10.1002/epi4.12775. Epub 2023 Jun 26.

Abstract

OBJECTIVE

Differential diagnosis between epileptic seizures and psychogenic nonepileptic events (PNEEs) is a worldwide problem for neurologists. The present study aims to identify important characteristics from body fluid tests and develop diagnostic models based on them.

METHODS

This is a register-based observational study in patients with a diagnosis of epilepsy or PNEEs at West China Hospital of Sichuan University. Data from body fluid tests between 2009 and 2019 were used as a training set. We constructed models with a random forest approach in eight training subsets divided by sex and categories of tests, including electrolyte, blood cell, metabolism, and urine tests. Then, we collected data prospectively from patients between 2020 and 2022 to validate our models and calculated the relative importance of characteristics in robust models. Selected characteristics were finally analyzed with multiple logistic regression to establish nomograms.

RESULTS

A total of 388 patients, including 218 with epilepsy and 170 with PNEEs, were studied. The AUROCs of random forest models of electrolyte and urine tests in the validation phase achieved 80.0% and 79.0%, respectively. Carbon dioxide combining power, anion gap, potassium, calcium, and chlorine in electrolyte tests and specific gravity, pH, and conductivity in urine tests were selected for the logistic regression analysis. C (ROC) of the electrolyte and urine diagnostic nomograms achieved 0.79 and 0.85, respectively.

SIGNIFICANCE

The application of routine indicators of serum and urine may help in the more accurate identification of epileptic and PNEEs.

摘要

目的

癫痫发作与非癫痫性发作(PNEE)的鉴别诊断是全世界神经病学家面临的一个问题。本研究旨在确定体液检查中的重要特征,并基于这些特征建立诊断模型。

方法

这是一项基于四川大学华西医院患者的登记观察性研究,患者被诊断为癫痫或 PNEE。2009 年至 2019 年的体液检查数据被用作训练集。我们使用随机森林方法,在按性别和检查类别划分的 8 个训练子集中构建模型,包括电解质、血细胞、代谢和尿液检查。然后,我们在 2020 年至 2022 年期间前瞻性收集患者数据以验证我们的模型,并计算稳健模型中特征的相对重要性。最后,使用多因素逻辑回归分析选择的特征来建立列线图。

结果

共纳入 388 例患者,其中癫痫 218 例,PNEE 170 例。在验证阶段,电解质和尿液检查的随机森林模型的 AUROC 分别达到 80.0%和 79.0%。电解质检查中的二氧化碳结合力、阴离子间隙、钾、钙和氯以及尿液检查中的比重、pH 值和电导率被选入逻辑回归分析。电解质和尿液诊断列线图的 C(ROC)分别为 0.79 和 0.85。

意义

血清和尿液常规指标的应用可能有助于更准确地鉴别癫痫和 PNEE。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fdb2/10472377/f607a0bef499/EPI4-8-959-g001.jpg

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