Francesco Brigo1, Gianni Turcato2, Giada Giovannini3,4, Stefano Meletti5,6, Simona Lattanzi7, Niccolò Orlandi3,8, Giulia Turchi3, Arian Zaboli9. 1. Department of Neurology, Hospital of Merano-Meran, Merano-Meran, Italy. 2. Department of Internal Medicine, Hospital of Santorso, Santorso, Italy. 3. Neurology Department, Azienda Ospedaliera-Universitaria di Modena, Modena, Italy. 4. PhD Program in Clinical and Experimental Medicine, University of Modena and Reggio-Emilia, Modena, Italy. 5. Neurology Department, Azienda Ospedaliera-Universitaria di Modena, Modena, Italy. stefano.meletti@unimore.it. 6. Department of Biomedical, Metabolic, and Neural Sciences, University of Modena and Reggio-Emilia, Modena and Reggio-Emilia, Italy. stefano.meletti@unimore.it. 7. Neurological Clinic, Department of Experimental and Clinical Medicine, Marche Polytechnic University, Ancona, Italy. 8. Department of Biomedical, Metabolic, and Neural Sciences, University of Modena and Reggio-Emilia, Modena and Reggio-Emilia, Italy. 9. Department of Emergency Medicine, Hospital of Merano-Meran, Merano-Meran, Italy.
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.
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.
Authors: Andrea O Rossetti; Giancarlo Logroscino; Tracey A Milligan; Costas Michaelides; Christiane Ruffieux; Edward B Bromfield Journal: J Neurol Date: 2008-09-03 Impact factor: 4.849
Authors: Eugen Trinka; Hannah Cock; Dale Hesdorffer; Andrea O Rossetti; Ingrid E Scheffer; Shlomo Shinnar; Simon Shorvon; Daniel H Lowenstein Journal: Epilepsia Date: 2015-09-04 Impact factor: 5.864