Estimating the likelihood of epilepsy from clinically noncontributory electroencephalograms using computational analysis: A retrospective, multisite case–control study

IF 6.6 1区医学 Q1 CLINICAL NEUROLOGY

Epilepsia Pub Date : 2024-05-23 DOI:10.1111/epi.18024

Luke Tait, Lydia E. Staniaszek, Elizabeth Galizia, David Martin-Lopez, Matthew C. Walker, Al Anzari Abdul Azeez, Kay Meiklejohn, David Allen, Chris Price, Sophie Georgiou, Manny Bagary, Sakh Khalsa, Francesco Manfredonia, Phil Tittensor, Charlotte Lawthom, Benjamin B. Howes, Rohit Shankar, John R. Terry, Wessel Woldman

{"title":"Estimating the likelihood of epilepsy from clinically noncontributory electroencephalograms using computational analysis: A retrospective, multisite case–control study","authors":"Luke Tait, Lydia E. Staniaszek, Elizabeth Galizia, David Martin-Lopez, Matthew C. Walker, Al Anzari Abdul Azeez, Kay Meiklejohn, David Allen, Chris Price, Sophie Georgiou, Manny Bagary, Sakh Khalsa, Francesco Manfredonia, Phil Tittensor, Charlotte Lawthom, Benjamin B. Howes, Rohit Shankar, John R. Terry, Wessel Woldman","doi":"10.1111/epi.18024","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Objective</h3>\n \n <p>This study was undertaken to validate a set of candidate biomarkers of seizure susceptibility in a retrospective, multisite case–control study, and to determine the robustness of these biomarkers derived from routinely collected electroencephalography (EEG) within a large cohort (both epilepsy and common alternative conditions such as nonepileptic attack disorder).</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>The database consisted of 814 EEG recordings from 648 subjects, collected from eight National Health Service sites across the UK. Clinically noncontributory EEG recordings were identified by an experienced clinical scientist (<i>N</i> = 281; 152 alternative conditions, 129 epilepsy). Eight computational markers (spectral [<i>n</i> = 2], network-based [<i>n</i> = 4], and model-based [<i>n</i> = 2]) were calculated within each recording. Ensemble-based classifiers were developed using a two-tier cross-validation approach. We used standard regression methods to assess whether potential confounding variables (e.g., age, gender, treatment status, comorbidity) impacted model performance.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>We found levels of balanced accuracy of 68% across the cohort with clinically noncontributory normal EEGs (sensitivity =61%, specificity =75%, positive predictive value =55%, negative predictive value =79%, diagnostic odds ratio =4.64, area under receiver operated characteristics curve =.72). Group level analysis found no evidence suggesting any of the potential confounding variables significantly impacted the overall performance.</p>\n </section>\n \n <section>\n \n <h3> Significance</h3>\n \n <p>These results provide evidence that the set of biomarkers could provide additional value to clinical decision-making, providing the foundation for a decision support tool that could reduce diagnostic delay and misdiagnosis rates. Future work should therefore assess the change in diagnostic yield and time to diagnosis when utilizing these biomarkers in carefully designed prospective studies.</p>\n </section>\n </div>","PeriodicalId":11768,"journal":{"name":"Epilepsia","volume":null,"pages":null},"PeriodicalIF":6.6000,"publicationDate":"2024-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/epi.18024","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Epilepsia","FirstCategoryId":"3","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/epi.18024","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CLINICAL NEUROLOGY","Score":null,"Total":0}

引用次数: 0

Abstract

Objective

This study was undertaken to validate a set of candidate biomarkers of seizure susceptibility in a retrospective, multisite case–control study, and to determine the robustness of these biomarkers derived from routinely collected electroencephalography (EEG) within a large cohort (both epilepsy and common alternative conditions such as nonepileptic attack disorder).

Methods

The database consisted of 814 EEG recordings from 648 subjects, collected from eight National Health Service sites across the UK. Clinically noncontributory EEG recordings were identified by an experienced clinical scientist (N = 281; 152 alternative conditions, 129 epilepsy). Eight computational markers (spectral [n = 2], network-based [n = 4], and model-based [n = 2]) were calculated within each recording. Ensemble-based classifiers were developed using a two-tier cross-validation approach. We used standard regression methods to assess whether potential confounding variables (e.g., age, gender, treatment status, comorbidity) impacted model performance.

Results

We found levels of balanced accuracy of 68% across the cohort with clinically noncontributory normal EEGs (sensitivity =61%, specificity =75%, positive predictive value =55%, negative predictive value =79%, diagnostic odds ratio =4.64, area under receiver operated characteristics curve =.72). Group level analysis found no evidence suggesting any of the potential confounding variables significantly impacted the overall performance.

Significance

These results provide evidence that the set of biomarkers could provide additional value to clinical decision-making, providing the foundation for a decision support tool that could reduce diagnostic delay and misdiagnosis rates. Future work should therefore assess the change in diagnostic yield and time to diagnosis when utilizing these biomarkers in carefully designed prospective studies.

Abstract Image

查看原文本刊更多论文

利用计算分析法从临床无影响的脑电图中估计癫痫的可能性：一项回顾性多地点病例对照研究。

研究目的本研究旨在通过一项回顾性、多地点病例对照研究验证一组癫痫易感性候选生物标志物，并确定这些生物标志物的稳健性，这些生物标志物来自大型队列（包括癫痫和常见的其他疾病，如非癫痫发作障碍）中常规收集的脑电图（EEG）：该数据库由来自英国八个国民健康服务机构的 648 名受试者的 814 份脑电图记录组成。一名经验丰富的临床科学家对临床无影响的脑电图记录进行了鉴定（N = 281；152 种其他病症，129 种癫痫）。在每个记录中计算八个计算标记（频谱标记 [n = 2]、基于网络的标记 [n = 4] 和基于模型的标记 [n = 2]）。我们使用双层交叉验证方法开发了基于集合的分类器。我们使用标准回归方法来评估潜在的混杂变量（如年龄、性别、治疗状态、合并症等）是否会影响模型性能：我们发现整个队列中临床无影响的正常脑电图的平衡准确率为 68%（灵敏度 =61%，特异性 =75%，阳性预测值 =55%，阴性预测值 =79%，诊断几率比 =4.64，接收者操作特征曲线下面积 =.72）。小组层面的分析没有发现任何证据表明任何潜在的混杂变量会对整体性能产生重大影响：这些结果证明，这组生物标志物可为临床决策提供额外价值，为决策支持工具奠定基础，从而减少诊断延误和误诊率。因此，未来的工作应评估在精心设计的前瞻性研究中利用这些生物标志物时诊断率和诊断时间的变化。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Epilepsia 医学-临床神经学

CiteScore

10.90

自引率

10.70%

发文量

319

审稿时长

2-4 weeks

期刊介绍： Epilepsia is the leading, authoritative source for innovative clinical and basic science research for all aspects of epilepsy and seizures. In addition, Epilepsia publishes critical reviews, opinion pieces, and guidelines that foster understanding and aim to improve the diagnosis and treatment of people with seizures and epilepsy.