Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA.
Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, Minnesota, USA.
Epilepsia. 2023 Sep;64(9):2421-2433. doi: 10.1111/epi.17678. Epub 2023 Jun 19.
Previous studies suggested that patients with epilepsy might be able to forecast their own seizures. This study aimed to assess the relationships between premonitory symptoms, perceived seizure risk, and future and recent self-reported and electroencephalographically (EEG)-confirmed seizures in ambulatory patients with epilepsy in their natural home environments.
Long-term e-surveys were collected from patients with and without concurrent EEG recordings. Information obtained from the e-surveys included medication adherence, sleep quality, mood, stress, perceived seizure risk, and seizure occurrences preceding the survey. EEG seizures were identified. Univariate and multivariate generalized linear mixed-effect regression models were used to estimate odds ratios (ORs) for the assessment of the relationships. Results were compared with the seizure forecasting classifiers and device forecasting literature using a mathematical formula converting OR to equivalent area under the curve (AUC).
Fifty-four subjects returned 10 269 e-survey entries, with four subjects acquiring concurrent EEG recordings. Univariate analysis revealed that increased stress (OR = 2.01, 95% confidence interval [CI] = 1.12-3.61, AUC = .61, p = .02) was associated with increased relative odds of future self-reported seizures. Multivariate analysis showed that previous self-reported seizures (OR = 5.37, 95% CI = 3.53-8.16, AUC = .76, p < .001) were most strongly associated with future self-reported seizures, and high perceived seizure risk (OR = 3.34, 95% CI = 1.87-5.95, AUC = .69, p < .001) remained significant when prior self-reported seizures were added to the model. No correlation with medication adherence was found. No significant association was found between e-survey responses and subsequent EEG seizures.
Our results suggest that patients may tend to self-forecast seizures that occur in sequential groupings and that low mood and increased stress may be the result of previous seizures rather than independent premonitory symptoms. Patients in the small cohort with concurrent EEG showed no ability to self-predict EEG seizures. The conversion from OR to AUC values facilitates direct comparison of performance between survey and device studies involving survey premonition and forecasting.
先前的研究表明，癫痫患者可能能够预测自己的癫痫发作。本研究旨在评估在自然家庭环境中，癫痫患者的前驱症状、感知到的癫痫发作风险，以及未来和近期自我报告和脑电图（EEG）确认的癫痫发作之间的关系。
从有和没有同时进行 EEG 记录的患者中收集长期电子调查。从电子调查中获得的信息包括药物依从性、睡眠质量、情绪、压力、感知到的癫痫发作风险以及调查前的癫痫发作情况。识别 EEG 癫痫发作。使用单变量和多变量广义线性混合效应回归模型来估计评估这些关系的优势比（OR）。使用将 OR 转换为等效曲线下面积（AUC）的数学公式，将结果与癫痫发作预测分类器和设备预测文献进行比较。
54 名受试者共返回了 10269 份电子调查条目，其中 4 名受试者同时进行了 EEG 记录。单变量分析显示，压力增加（OR=2.01，95%置信区间[CI]为 1.12-3.61，AUC=0.61，p=0.02）与未来自我报告的癫痫发作的相对优势增加有关。多变量分析表明，先前的自我报告的癫痫发作（OR=5.37，95%CI=3.53-8.16，AUC=0.76，p<0.001）与未来的自我报告的癫痫发作最密切相关，当将先前的自我报告的癫痫发作纳入模型时，高感知到的癫痫发作风险（OR=3.34，95%CI=1.87-5.95，AUC=0.69，p<0.001）仍然具有显著性。与药物依从性无相关性。电子调查答复与随后的 EEG 癫痫发作之间没有发现显著关联。
我们的结果表明，患者可能倾向于自我预测发生连续分组的癫痫发作，而情绪低落和压力增加可能是先前癫痫发作的结果，而不是独立的前驱症状。在伴有 EEG 的小队列中，患者没有自我预测 EEG 癫痫发作的能力。将 OR 转换为 AUC 值可促进涉及调查前预感和预测的调查和设备研究之间的性能直接比较。
