Abstract
Objective
To validate the accuracy of the Epidemiology-based Mortality Score in Status Epilepticus (EMSE) in predicting the risk of death at 30 days in a large cohort of patients with status epilepticus (SE) using a machine-learning system.
Methods
We included consecutive patients with SE admitted from 2013 to 2021 at Modena Academic Hospital. A decision tree analysis was performed using the 30-day mortality as a dependent variable and the EMSE predictors as input variables. We evaluated the accuracy of EMSE in predicting 30-day mortality using the area under the receiver operating characteristic curve (AUC ROC), with 95% confidence interval (CI). We performed a subgroup analysis on non-hypoxic SE.
Results
698 patients with SE were included, with a 30-day mortality of 28.9% (202/698). The mean EMSE value in the entire population was 57.1 (SD 36.3); it was lower in surviving compared to deceased patients (47.1, SD 31.7 versus 81.9, SD 34.8; p <0.001). The EMSE was accurate in predicting 30-day mortality, with an AUC ROC of 0.782 (95% CI: 0.747-0.816). Etiology was the most relevant predictor, followed by age, EEG, and EMSE comorbidity group B. The decision tree analysis using EMSE variables correctly predicted the risk of mortality in 77.9% of cases; the prediction was accurate in 85.7% of surviving and in 58.9% of deceased patients within 30 days after the SE. In non-hypoxic SE, the most relevant predictor was age, followed by EEG, and EMSE comorbidity group B; the prediction was correct in 78.9% of all cases (89.6% in survivors and 46.1% in nonsurvivors).
Significance
This validation study using a machine-learning analysis shows that the EMSE is a valuable prognostic tool, and appears particularly accurate and effective in identifying patients with 30-day survival, while its role in predicting 30-day mortality is lower and needs to be further implemented.
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