Harvard Neuroendocrine Unit, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA.
Neurelis, San Diego, California, USA.
Epilepsia. 2023 Jun;64(6):1507-1515. doi: 10.1111/epi.17588. Epub 2023 Mar 26.
We assessed whether (1) women with statistical clustering of daily seizure counts (DSCs) or seizure intervals (SIs) also showed clinical clustering, defined separately by ≥2 (≥2-SC) and ≥3 (≥3-SC) seizures on any single day; and (2) how these classifiers might apply to catamenial epilepsy.
This is a retrospective case-control analysis of data from 50 women with epilepsy (WWE). We assessed the relationships of the four classifiers to each other and to catamenial versus noncatamenial epilepsy using chi-squared, correlation, logistic regression, and receiver operating characteristic (ROC) analyses.
≥3-SC, not ≥2-SC, was more frequent in WWE who had statistical DSC clustering versus those who did not (21/25 [84.0%] vs. 11/25 [44.0%], p = .007). Logistic regression (p = .006) and ROC (p = .015) identified ≥3-SC, not ≥2-SC, as a predictor of statistical DSC clustering, but ≥4-SC was more accurate. ≥3-SC correlated with the average daily seizure frequencies (ADSFs) of the subjects (p = .01). ROC optimal sensitivity-specificity cut-point for ADSF prediction of ≥3-SC (.372) was 64.6% higher than for ≥2-SC (.226). SI clustering was more common in WWE who had catamenial versus noncatamenial epilepsy (p = .013). Logistic regression identified statistical SI clustering as the only significant classifier (p = .043). ROC analysis offered only marginal support (p = .056), because specificity was low (42.1%).
The findings lend statistical support for (1) the utility of clinical ≥3-SC as a predictor of convulsive status epilepticus, (2) consideration of ADSFs in defining clustering, and (3) ≥4-SC as a more accurate clinical predictor of statistical DSC clustering. Statistical SI clustering occurred more frequently in women with catamenial than noncatamenial epilepsy (90.3% vs. 57.9%, p = .013). Although sensitivity was high (90.3%, 28/31), specificity was only 42.1% (8/19). Algorithms that test patterns and periodicities of clusters are more applicable.
我们评估了（1）每日发作次数（DSC）或发作间隔（SI）有统计学聚集的女性是否也表现出临床聚集，分别定义为在任何一天发作≥2 次（≥2-SC）和≥3 次（≥3-SC）；以及（2）这些分类器如何适用于月经性癫痫。
这是一项对 50 名癫痫女性（WWE）数据的回顾性病例对照分析。我们使用卡方检验、相关性、逻辑回归和接收者操作特征（ROC）分析评估了这四个分类器之间的关系，以及与月经性和非月经性癫痫的关系。
与没有统计学 DSC 聚集的女性相比，有统计学 DSC 聚集的 WWE 患者中≥3-SC 的发生率更高（21/25 [84.0%] vs. 11/25 [44.0%]，p=0.007）。逻辑回归（p=0.006）和 ROC（p=0.015）分析确定≥3-SC 而非≥2-SC 是统计学 DSC 聚集的预测因子，但≥4-SC 更准确。≥3-SC 与受试者的平均每日发作频率（ADSFs）相关（p=0.01）。ROC 对≥3-SC 预测 ADSF 的最佳敏感性-特异性截断值（0.372）比≥2-SC （0.226）高 64.6%。SI 聚集在有月经性和非月经性癫痫的 WWE 患者中更常见（p=0.013）。逻辑回归确定统计学 SI 聚集是唯一显著的分类器（p=0.043）。ROC 分析仅提供了边缘支持（p=0.056），因为特异性较低（42.1%）。
这些发现为（1）临床≥3-SC 作为癫痫持续状态的预测因子的效用提供了统计学支持，（2）在定义聚集时考虑 ADSFs，以及（3）≥4-SC 作为统计学 DSC 聚集更准确的临床预测因子提供了统计学支持。有月经性癫痫的女性中统计学 SI 聚集的发生率高于非月经性癫痫（90.3% vs. 57.9%，p=0.013）。虽然敏感性很高（90.3%，28/31），但特异性仅为 42.1%（8/19）。测试聚类模式和周期性的算法更适用。
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