Abstract
Objective
We assessed whether 1) women with statistical clustering of daily seizure counts (DSC) or seizure intervals (SI) 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.
Methods
This is a retrospective case-control analysis of data from 50 women with epilepsy (WWE). We assessed the relationships of the 4 classifiers to each other and to catamenial versus non-catamenial epilepsy using chi-square, correlation, logistic regression and ROC analyses.
Results:
≥3-SC, not ≥2-SC, were more frequent in WWE who had statistical DSC clustering versus those who did not: 21 of 25 (84.0%) vs 11 of 25 (44.0%), p = 0.007. Logistic regression (p = 0.006) and ROC (p = 0.015) 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 = 0.01. ROC optimal sensitivity-specificity cut-point for ADSF prediction of ≥3-SC (0.372) was 64.6% higher than for ≥2-SC (0.226). SI clustering was more common in WWE who had catamenial versus non-catamenial epilepsy, p = 0.013. Logistic regression identified statistical SI clustering as the only significant classifier, p = 0.043. ROC analysis offered only marginal support, p = 0.056 because specificity was low: 42.1%.
Significance
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 non-catamenial epilepsy (90.3% vs 57.9%, p = 0.013). 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.
MAR