Comparison of scoring tools for the prediction of in-hospital mortality in status epilepticus. (March 2018)

• Record Type: Journal Article
• Authors: Reindl, Caroline; Knappe, Ruben U.; Sprügel, Maximilian I.; Sembill, Jochen A.; Mueller, Tamara M.; Hamer, Hajo M.; Huttner, Hagen B.; Madžar, Dominik
• Is Part Of: Seizure. Volume 56(2018)
• Page Start: 92
• Page End: 97
• Publication Date: 2018-03
• Subjects: CI confidence interval -- mRS modified Rankin Scale -- SE status epilepticus -- STESS Status Epilepticus Severity Score -- mSTESS modified STESS -- EMSE-EAL Epidemiology-based Mortality Score in Status Epilepticus – Etiology, Age, Level of Consciousness END-ITEncephalitis-NCSE-Diazepam resistance-Image abnormalities-Tracheal intubation; Status epilepticus -- Mortality -- Outcome -- STESS -- EMSE -- END-IT -- mSTESS; Epilepsy -- Periodicals; Epilepsy -- Periodicals; Seizures -- Periodicals; Épilepsie -- Périodiques; Electronic journals; Electronic journals; 616.853
• DOI: 10.1016/j.seizure.2018.01.024
• Languages: English
• ISSNs: 1059-1311

Abstract

Highlights: AUC comparisons did not reveal significant differences between the scoring tools. EMSE-EAL-40 tended towards highest diagnostic accuracy. END-IT-3 showed most balanced sensitivity-specificity ratio. Abstract: Purpose: Several scoring tools have been developed for the prognostication of outcome after status epilepticus (SE). In this study, we compared the performances of STESS (Status Epilepticus Severity Score), mSTESS (modified STESS), EMSE-EAL (Epidemiology-based Mortality Score in Status Epilepticus- Etiology, Age, Level of Consciousness) and END-IT (Encephalitis-NCSE-Diazepam resistance-Image abnormalities-Tracheal intubation) in predicting in-hospital mortality after SE. Method: Data collected retrospectively from a cohort of 287 patients with SE were used to calculate STESS, mSTESS, EMSE-EAL, and END-IT scores. The differences between the scores' performances were determined by means of area under the ROC curve (AUC) comparisons and McNemar testing. Results: The in-hospital mortality rate was 11.8%. The AUC of STESS (0.628; 95% confidence interval (CI), 0.529–0.727) was similar to that of mSTESS (0.620; 95% CI, 0.510–0.731), EMSE-EAL (0.556; 95% CI, 0.446–0.665), and END-IT (0.659; 95% CI, 0.550–0.768; p > .05 for each comparison) in predicting in-hospital mortality. STESS with a cutoff of 3 was found to have lowest specificity and number of correctly classified episodes. EMSE-EAL with a cutoff at 40 had highest specificity and showed a trend towards more correctly classified episodes while sensitivity tended to be low. END-IT with a cutoff of 3 had the most balanced sensitivity-specificity ratio. Conclusions: EMSE-EAL is as easy to calculate as STESS and tended towards higher diagnostic accuracy. Adding information on premorbid functional status to STESS did not enhance outcome prediction. END-IT was not superior to other scores in prediction of in-hospital mortality despite including information of diagnostic work-up and response to initial treatment.