A comparison of statistical versus clinical classifiers of seizure clustering in women with catamenial and noncatamenial epilepsy.

OBJECTIVE: 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.

METHODS: 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.

RESULTS: ≥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%).

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 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.