Predictive performances of STESS and EMSE in a Norwegian adult status epilepticus cohort
Line Bédos Ulvin1, Erik Taubøll2, Ketil Berg Olsen3
1Department of Neurology, Oslo University Hospital, Oslo, Norway; ERGO - Epilepsy Research Group of Oslo, Oslo University Hospital, Oslo, Norway.
Purpose:
"Status Epilepticus Severity Score" (STESS) and "Epidemiology-based Mortality Score in Status Epilepticus" (EMSE) are two clinical scoring systems aiming to predict mortality in status epilepticus (SE). The objective of this study was to compare their predictive performances in a cohort of 151 SE-patients from Oslo University Hospital in the period 2001-2017.
Method:
Variables used to calculate STESS (age, previous seizures, worst SE-semiology, level of consciousness) and two different versions of EMSE, EMSE-EAC (etiology, age, comorbidities) and EMSE-EACE (etiology, age, comorbidities, EEG-pattern), as well as outcome were collected retrospectively. Receiver Operating Characteristic (ROC)-analyses, determination of best cut-off values, sensitivity (Se), specificity (Sp), positive predictive value (PPV) and negative predictive value (NPV) were performed. In addition, Precision-Recall curves (PRC) were produced, plotting PPV as a function of Se.
Results:
Thirteen patients (9%) died during their hospital stay. STESS did not accurately predict mortality, with a ROC-curve showing an area under the curve (AUC) of 0.625(95%CI = 0.472-0.783), p = 0.15. EMSE-EAC performed better with an AUC of 0.714(95%CI = 0.552-0.873), p = 0.01 and a best cut-off value of 37. Se was 69.2%, Sp 72.1%, PPV 19% and NPV 96.2%. EMSE-EACE performed best with an AUC of 0.855(95%CI = 0.736-0.976), p < 0.0005 and a best cut-off value of 79. Se was 77.8%, Sp 87.8%, PPV 36.8% and NPV 97.7%. The PRC showed areas under the PRC of 0.23 for EMSE-EAC and 0.46 for EMSE-EACE.
Conclusions:
EMSE-EAC and EMSE-EACE performed better than STESS and may be useful in identifying the patients at risk of death in SE. PRC may give a more relevant visual representation of predictive utility than ROC-curves in situations of imbalanced datasets.
More Related Videos
07:13Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
Published on: April 18, 2025
08:53Preparation of Single-cohort Colonies and Hormone Treatment of Worker Honeybees to Analyze Physiology Associated with Role and/or Endocrine System
Published on: September 6, 2016
Related Concept Videos
Predicting Molecular Geometry
Marcia's Theory of Identity Status
Prediction Intervals
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
End Point Prediction: Gran Plot
For potentiometric titration, the Gran plot is created by plotting...
Sensitivity, Specificity, and Predicted Value
Sensitivity is the...
Predicting Reaction Outcomes
