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13.10.2022 | Original work

Retrospective External Validation of the Status Epilepticus Severity Score (STESS) to Predict In-hospital Mortality in Adults with Nonhypoxic Status Epilepticus: A Machine Learning Analysis

verfasst von: Francesco Brigo, Gianni Turcato, Simona Lattanzi, Niccolò Orlandi, Giulia Turchi, Arian Zaboli, Giada Giovannini, Stefano Meletti

Erschienen in: Neurocritical Care | Ausgabe 2/2023

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Abstract

Background

The objective of this study was to validate the value of the Status Epilepticus Severity Score (STESS) in the prediction of the risk of in-hospital mortality in patients with nonhypoxic status epilepticus (SE) using a machine learning analysis.

Methods

We included consecutive patients with nonhypoxic SE (aged ≥ 16 years) admitted from 2013 to 2021 at the Modena Academic Hospital. A decision tree analysis was performed using in-hospital mortality as a dependent variable and the STESS predictors as input variables. We evaluated the accuracy of STESS in predicting in-hospital mortality using the area under the receiver operating characteristic curve (AUROC) with 95% confidence interval (CI).

Results

Among 629 patients with SE, the in-hospital mortality rate was 23.4% (147 of 629). The median STESS in the entire cohort was 2.9 (SD 1.6); it was lower in surviving compared with deceased patients (2.7, SD 1.5 versus 3.9, SD 1.6; p < 0.001). Of deceased patients, 82.3% (121 of 147) had scores of 3–6, whereas 17.7% (26 of 147) had scores of 0–2 (p < 0.001). STESS was accurate in predicting mortality, with an AUROC of 0.688 (95% CI 0.641–0.734) only slightly reduced after bootstrap resampling. The most significant predictor was the seizure type, followed by age and level of consciousness at SE onset. Nonconvulsive SE in coma and age ≥ 65 years predicted a higher risk of mortality, whereas generalized convulsive SE and age < 65 years were associated with a lower risk of death. The decision tree analysis using STESS variables correctly classified 90% of survivors and 34% of nonsurvivors after the SE, with an overall risk of error of 23.1%.

Conclusions

This validation study using a machine learning system showed that STESS is a valuable prognostic tool. The score appears particularly accurate and effective in identifying patients who are alive at discharge (high negative predictive value), whereas it has a lower predictive value for in-hospital mortality.

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Titel: Retrospective External Validation of the Status Epilepticus Severity Score (STESS) to Predict In-hospital Mortality in Adults with Nonhypoxic Status Epilepticus: A Machine Learning Analysis
verfasst von: Francesco Brigo
Gianni Turcato
Simona Lattanzi
Niccolò Orlandi
Giulia Turchi
Arian Zaboli
Giada Giovannini
Stefano Meletti
Publikationsdatum: 13.10.2022
Verlag: Springer US
Erschienen in: Neurocritical Care / Ausgabe 2/2023
Print ISSN: 1541-6933
Elektronische ISSN: 1556-0961
DOI: https://doi.org/10.1007/s12028-022-01610-3

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Retrospective External Validation of the Status Epilepticus Severity Score (STESS) to Predict In-hospital Mortality in Adults with Nonhypoxic Status Epilepticus: A Machine Learning Analysis