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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

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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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Data Availability

On request from qualified investigators, we will share anonymized data.

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Funding

This research received funding from the Italian MOH (“Status epilepticus: improving therapeutic and quality of care intervention in the Emilia-Romagna region”; project code: RF-2016–02,361,365) and by the MIUR (grant “Dipartimenti di eccellenza 2018–2022” to the Department of Biomedical, Metabolic, and Neural Sciences, University of Modena and Reggio-Emilia).

Author information

Author notes

  1. Francesco Brigo, Gianni Turcato, Giada Giovannini and Stefano Meletti have Contributed equally to the work.

Authors and Affiliations

  1. Department of Neurology, Hospital of Merano-Meran, Merano-Meran, Italy

    Francesco Brigo

  2. Department of Internal Medicine, Hospital of Santorso, Santorso, Italy

    Gianni Turcato

  3. Neurological Clinic, Department of Experimental and Clinical Medicine, Marche Polytechnic University, Ancona, Italy

    Simona Lattanzi

  4. Neurology Department, Azienda Ospedaliera-Universitaria di Modena, Modena, Italy

    Niccolò Orlandi, Giulia Turchi, Giada Giovannini & Stefano Meletti

  5. Department of Biomedical, Metabolic, and Neural Sciences, University of Modena and Reggio-Emilia, Modena and Reggio-Emilia, Italy

    Niccolò Orlandi & Stefano Meletti

  6. Department of Emergency Medicine, Hospital of Merano-Meran, Merano-Meran, Italy

    Arian Zaboli

  7. PhD Program in Clinical and Experimental Medicine, University of Modena and Reggio-Emilia, Modena, Italy

    Giada Giovannini

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  1. Francesco Brigo
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Contributions

FB: conceptualization, methodology, original draft preparation. GT: conceptualization, formal analysis, methodology, original draft preparation. SL: methodology, review and editing. NO: data curation, investigation, review and editing. GT: data curation, investigation, review and editing. AZ: methodology, visualization, review and editing. GG: data curation, investigation, review and editing. SM: review and editing, supervision, validation.

Corresponding author

Correspondence to Stefano Meletti.

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None of the authors has any conflicts of interest to disclose.

Ethical Approval/Informed Consent

The study was approved by the local ethical committee (ethics committee approval number 556/2018/OSS/AOUMO–RF‐2016‐02,361,365) and was conducted according to the ethical principles for medical research involving human subjects in the Declaration of Helsinki.

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Brigo, F., Turcato, G., Lattanzi, S. et al. 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. Neurocrit Care 38, 254–262 (2023). https://doi.org/10.1007/s12028-022-01610-3

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