Abstract：
BACKGROUND: Review of videos (without electroencephalography) to differentiate epileptic seizures (ES) from non-epileptic spells (NES) may be helpful where epilepsy monitoring is not feasible. Previous studies of video-based diagnosis have suffered from variable accuracy, sensitivity, and specificity.
METHODS: We systematically reviewed relevant literature in PubMed, Embase, and Web of Science from inception to September 2022, identifying articles that reported on the video-based diagnosis of ES and NES. In primary analysis, for each study, the most expert group was chosen when different groups of reviewers classified the videos (e.g., epilepsy specialists and general neurologists). In secondary analysis, we compared the diagnostic accuracy of different expertise levels (e.g., epileptologists, general neurologists, residents, medical students). Meta-analysis was performed to obtain pooled estimates of reliability measures.
RESULTS: From 5245 articles identified, 13 met the inclusion criteria, with cumulative data from 683 patients (696 videos) reviewed by 95 independent reviewers in primary analysis. Video alone had a strong ability to differentiate ES from NES as evidenced by the following metrics- area under the curve- 0.9 (considered \"outstanding\"), sensitivity- 82.2% (95% Confidence Interval [C.I], 80.2%-84.0%), specificity- 84.7% (C.I., 82.8%-86.5%), and diagnostic odds ratio- 24.7 (C.I., 11.5-52.9). The secondary analysis showed reviewer-dependent accuracy with epileptologists showing the highest accuracy (DOR 81.2, C.I., 90.0%-94.6%).
CONCLUSIONS: Video alone has reliable diagnostic performance for differentiating ES from NES. Meta-analysis limitations include inter-study heterogeneity including variable video quality and reviewer expertise. Combined video-EEG remains the gold standard for the diagnosis of epilepsy and NES.
METHODS: We systematically reviewed relevant literature in PubMed, Embase, and Web of Science from inception to September 2022, identifying articles that reported on the video-based diagnosis of ES and NES. In primary analysis, for each study, the most expert group was chosen when different groups of reviewers classified the videos (e.g., epilepsy specialists and general neurologists). In secondary analysis, we compared the diagnostic accuracy of different expertise levels (e.g., epileptologists, general neurologists, residents, medical students). Meta-analysis was performed to obtain pooled estimates of reliability measures.
RESULTS: From 5245 articles identified, 13 met the inclusion criteria, with cumulative data from 683 patients (696 videos) reviewed by 95 independent reviewers in primary analysis. Video alone had a strong ability to differentiate ES from NES as evidenced by the following metrics- area under the curve- 0.9 (considered \"outstanding\"), sensitivity- 82.2% (95% Confidence Interval [C.I], 80.2%-84.0%), specificity- 84.7% (C.I., 82.8%-86.5%), and diagnostic odds ratio- 24.7 (C.I., 11.5-52.9). The secondary analysis showed reviewer-dependent accuracy with epileptologists showing the highest accuracy (DOR 81.2, C.I., 90.0%-94.6%).
CONCLUSIONS: Video alone has reliable diagnostic performance for differentiating ES from NES. Meta-analysis limitations include inter-study heterogeneity including variable video quality and reviewer expertise. Combined video-EEG remains the gold standard for the diagnosis of epilepsy and NES.
摘要：
背景：在癫痫监测不可行的情况下，回顾视频（无脑电图）以区分癫痫发作（ES）和非癫痫法术（NES）可能会有所帮助。以前的基于视频的诊断研究受到了不同准确性的影响，灵敏度,和特异性。
方法：我们系统回顾了PubMed，Embase,和WebofScience从成立到2022年9月，确定了报道基于视频的ES和NES诊断的文章。在初步分析中，对于每一项研究，当不同的审稿人群体对视频进行分类时，选择了最多的专家组(例如，癫痫专家和普通神经科医生)。在次要分析中，我们比较了不同专业知识水平的诊断准确性(例如，癫痫学家,普通神经学家,居民,医学生)。进行荟萃分析以获得可靠性测量的汇总估计。
结果：从确定的5245篇文章中，13符合纳入标准，在主要分析中，来自683例患者（696个视频）的累积数据由95名独立审阅者审查。仅视频就有很强的区分ES和NES的能力，如以下度量曲线下的区域-0.9(被认为“杰出”)所证明的那样，敏感性-82.2%(95%置信区间[C.I],80.2%-84.0%），特异性-84.7%（C.I.，82.8%-86.5%），和诊断几率-24.7(C.I.，11.5-52.9）。二次分析显示，与审阅者相关的准确性，癫痫学家的准确性最高（DOR81.2，C.I.，90.0%-94.6%）。
结论：视频在区分ES和NES方面具有可靠的诊断性能。荟萃分析的局限性包括研究间的异质性，包括可变的视频质量和审稿人的专业知识。联合视频脑电图仍然是诊断癫痫和NES的金标准。
方法：我们系统回顾了PubMed，Embase,和WebofScience从成立到2022年9月，确定了报道基于视频的ES和NES诊断的文章。在初步分析中，对于每一项研究，当不同的审稿人群体对视频进行分类时，选择了最多的专家组(例如，癫痫专家和普通神经科医生)。在次要分析中，我们比较了不同专业知识水平的诊断准确性(例如，癫痫学家,普通神经学家,居民,医学生)。进行荟萃分析以获得可靠性测量的汇总估计。
结果：从确定的5245篇文章中，13符合纳入标准，在主要分析中，来自683例患者（696个视频）的累积数据由95名独立审阅者审查。仅视频就有很强的区分ES和NES的能力，如以下度量曲线下的区域-0.9(被认为“杰出”)所证明的那样，敏感性-82.2%(95%置信区间[C.I],80.2%-84.0%），特异性-84.7%（C.I.，82.8%-86.5%），和诊断几率-24.7(C.I.，11.5-52.9）。二次分析显示，与审阅者相关的准确性，癫痫学家的准确性最高（DOR81.2，C.I.，90.0%-94.6%）。
结论：视频在区分ES和NES方面具有可靠的诊断性能。荟萃分析的局限性包括研究间的异质性，包括可变的视频质量和审稿人的专业知识。联合视频脑电图仍然是诊断癫痫和NES的金标准。
