Predictive value of the Status Epilepticus Severity Score (STESS) and its components for long-term survival.

Preben Aukland,Ole Vilholm,Christoph Patrick Beier,Elsebeth Bruun Christiansen,Martin Lando

doi:10.1186/s12883-016-0730-0

Abstract

BackgroundThe “Status Epilepticus Severity Score” (STESS) is the most important clinical score to predict in-hospital mortality of patients with status epilepticus (SE), but its prognostic relevance for long-term survival is unknown. This study therefore examined if STESS and its components retain their prognostic relevance beyond acute treatment.MethodsOne hundred twenty-five non-anoxic patients with SE were retrospectively identified in two hospitals between 2008 and 2014 (39.2 % refractory SE). Patients’ treatment, demographic data, date of death, aetiology of SE, and the components of the STESS (age, history of seizures, level of consciousness and worst seizure type) were determined based on the patients’ records.ResultsIn 94.4 % of patients, SE was treated successfully; in-hospital mortality rate was 12 %. The overall mortality was 42 % after median follow-up of 28.1 months. The survival plateaued after about 3 years, all patients with progressive brain diseases (n = 4) died within one year. In-hospital mortality correlated highly significantly with STESS, the optimal cut-off was 4. With respect to long-term outcome, STESS correlated significantly with overall mortality though with lower odds ratios. When looking only at patients that survived the acute phase of treatment, only the STESS components “level of consciousness” (at admission), “coma” as worst seizure type, and “age” reached a statistical significant association with mortality. In these patients, STESS with a cut-off of 4 was not significantly associated with survival/mortality. Aetiology of SE was insufficient to explain the weak association and the high mortality after discharge alone.ConclusionSTESS at onset of SE reliably assessed in-hospital mortality, and was indicative for overall survival. However, STESS did not allow correct estimation of mortality after discharge. The high mortality after discharge and high overall mortality of patients diagnosed with SE was not explained by progressive brain disorders alone. Further research is needed to understand the causes for high overall mortality after SE and putative prognostic factors.Electronic supplementary materialThe online version of this article (doi:10.1186/s12883-016-0730-0) contains supplementary material, which is available to authorized users.

Highlights

The “Status Epilepticus Severity Score” (STESS) is the most important clinical score to predict in-hospital mortality of patients with status epilepticus (SE), but its prognostic relevance for long-term survival is unknown
The aetiology was classified according to the International League Against Epilepsy (ILAE) categorization [19] and spread among the categories, though very few of the patients had symptomatic seizures due to progressive CNS disorders
STESS and in-hospital mortality Seizures could be terminated in 94.6 % of the patients. 22 % of the patients were treated in the intensive care units (ICU) with deep sedation typically resulting in a burst-suppression pattern in the EEG (Table 1)

Summary

Introduction

The “Status Epilepticus Severity Score” (STESS) is the most important clinical score to predict in-hospital mortality of patients with status epilepticus (SE), but its prognostic relevance for long-term survival is unknown. This study examined if STESS and its components retain their prognostic relevance beyond acute treatment. Status epilepticus (SE) is a serious neurological condition with significant acute mortality of 7–39 % and early treatment is of crucial importance [1,2,3,4,5,6]. Because of the clinical heterogeneity of the affected patients [9] and the lack of established prognostic factors, the prediction of the clinical outcome and survival of SE remains difficult. Rossetti et al developed the “Status Epilepticus Severity Score” (STESS, Additional file 1: Table S1) in the purpose to predict in-hospital mortality [10].

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