Multimodal invasive monitoring in status epilepticus: what is the evidence it has a place?

Severity scores for status epilepticus in the ICU: systemic illness also matters

BACKGROUND

Current prognostic scores for status epilepticus (SE) may not be adequate for patients in ICU who usually have more severe systemic conditions or more refractory episodes of SE. We aimed to compare the prognostic performance of two SE scores, Status Epilepticus Severity Score (STESS) and Epidemiology-Based Mortality Score in Status Epilepticus (EMSE) score, with four systemic severity scores, Acute Physiology and Chronic Health Evaluation 2 (APACHE-2), Simplified Acute Physiology Score 2 (SAPS-2), Sequential Organ Failure Assessment (SOFA) score, and Inflammation, Nutrition, Consciousness, Neurologic function and Systemic condition (INCNS) score in critically ill patients with SE.

METHODS

This retrospective observational study of a prospectively identified SE cohort was conducted in the ICU at a tertiary-care center. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, positive predictive value, negative predictive value, accuracy, and associations with outcomes of STESS, EMSE, INCNS, APACHE-2, SAPS-2, and SOFA score for the prediction of in-hospital mortality and no return to baseline condition were assessed.

RESULTS

Between January 2015 and December 2020, 166 patients with SE in ICU were included in the study. In predicting in-hospital death, APACHE-2 (0.72), SAPS-2 (0.73), and SOFA score (0.71) had higher AUCs than STESS (0.58) and EMSE (0.69). In predicting no return to baseline condition, the AUC of APACHE-2 (0.75) was the highest, and the AUC of INCNS (0.55) was the lowest. When the specificity approached 90%, the sensitivity values of these scores were not quite acceptable (< 40%). Neither SE scores nor systemic severity scores had desirable prognostic power. In the multivariate logistic regression analyses, the best combinations of scores always included at least one or more systemic severity scores.

CONCLUSIONS

STESS and EMSE were insufficient in outcome prediction for SE patients in ICU, and EMSE was marginally better than STESS. Systemic illness matters in ICU patients with SE, and SE scores should be modified to achieve better accuracy in this severely ill population. This study mostly refers to severely ill patients in the ICU.

Multimodal Autoencoder Predicts fNIRS Resting State From EEG Signals

In this work, we introduce a deep learning architecture for evaluation on multimodal electroencephalographic (EEG) and functional near-infrared spectroscopy (fNIRS) recordings from 40 epileptic patients. Long short-term memory units and convolutional neural networks are integrated within a multimodal sequence-to-sequence autoencoder. The trained neural network predicts fNIRS signals from EEG, sans a priori, by hierarchically extracting deep features from EEG full spectra and specific EEG frequency bands. Results show that higher frequency EEG ranges are predictive of fNIRS signals with the gamma band inputs dominating fNIRS prediction as compared to other frequency envelopes. Seed based functional connectivity validates similar patterns between experimental fNIRS and our model's fNIRS reconstructions. This is the first study that shows it is possible to predict brain hemodynamics (fNIRS) from encoded neural data (EEG) in the resting human epileptic brain based on power spectrum amplitude modulation of frequency oscillations in the context of specific hypotheses about how EEG frequency bands decode fNIRS signals.