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":"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。","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":"{\"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}","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}
Estimating the likelihood of epilepsy from clinically noncontributory electroencephalograms using computational analysis: A retrospective, multisite case–control study
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.
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.
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