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

ICU-Electroencephalogram Unit Improves Outcome in Status Epilepticus Patients: A Retrospective Before-After Study

OBJECTIVES

Continuous electroencephalogram (cEEG) monitoring is recommended for status epilepticus (SE) management in ICU but is still underused due to resource limitations and inconclusive evidence regarding its impact on outcome. Furthermore, the term "continuous monitoring" often implies continuous recording with variable intermittent review. The establishment of a dedicated ICU-electroencephalogram unit may fill this gap, allowing cEEG with nearly real-time review and multidisciplinary management collaboration. This study aimed to evaluate the effect of ICU-electroencephalogram unit establishing on SE outcome and management.

DESIGN

Single-center retrospective before-after study.

SETTING

Neuro-ICU of a Swiss academic tertiary medical care center.

PATIENTS

Adult patients treated for nonhypoxic SE between November 1, 2015, and December 31, 2023.

INTERVENTIONS

None.

MEASUREMENT AND MAIN RESULTS

Data from all SE patients were assessed, comparing those treated before and after ICU-electroencephalogram unit introduction. Primary outcomes were return to premorbid neurologic function, ICU mortality, SE duration, and ICU SE management. Secondary outcomes were SE type and etiology. Two hundred seven SE patients were included, 149 (72%) before and 58 (38%) after ICU-electroencephalogram unit establishment. ICU-electroencephalogram unit introduction was associated with increased detection of nonconvulsive SE (p = 0.003) and SE due to acute symptomatic etiology (p = 0.019). Regression analysis considering age, comorbidities, SE etiology, and SE semeiology revealed a higher chance of returning to premorbid neurologic function (p = 0.002), reduced SE duration (p = 0.024), and a shift in SE management with increased use of antiseizure medications (p = 0.007) after ICU-electroencephalogram unit introduction.

CONCLUSIONS

Integrating neurology expertise in the ICU setting through the establishment of an ICU-electroencephalogram unit with nearly real-time cEEG review, shortened SE duration, and increased likelihood of returning to premorbid neurologic function, with an increased number of antiseizure medications used. Further studies are warranted to validate these findings and assess long-term prognosis.

Cannabidiol Anticonvulsant Effects Against Lithium-Pilocarpine-Induced Status Epilepticus in Male Rats Are Mediated by Neuroinflammation Modulation and Cannabinoids 1 (CB1), But Not CB2 and GABAA Receptors

Background: Status epilepticus (SE) is a series of seizures that can lead to serious neurological damages. Cannabidiol (CBD) is extracted from the cannabis plant, which has been approved as an antiseizure medication. This study aimed to determine the efficacy of various doses of CBD on lithium-pilocarpine-induced SE in rats and possible involvement of multiple pharmacological pathways. We hypothesized that cannabinoid receptors type 1 (CB1) and CB2, as well as GABAA receptors, might have important roles in the anticonvulsant effects of CBD against SE by its anti-inflammatory effects. Methods: SE was induced by intraperitoneal (i.p.) injection of lithium (127 mg/kg, i.p.) and pilocarpine (60 mg/kg, i.p., 20 h after lithium). Forty-two male rats were divided into seven groups (including control and sham groups), and the treated groups received different doses of CBD (1, 3, 5, 10, and 25 mg/kg, i.p.). SE score was recorded over the next 2 h following pilocarpine injection. Then, we measured the levels of pro-inflammatory cytokines, including interleukin (IL)-lβ and tumor necrosis factor (TNF)-α, using ELISA kits. Also we analyzed the expression of CB1, CB2, and GABAA receptors using the Western blot technique. Results: CBD at 5 mg/kg significantly reduced Racine's scale and duration of seizures, and increased the onset time of seizure. Moreover, CBD 5 mg/kg caused significant reductions in the elevated levels of IL-lβ and TNF-α, as well as a significant increase in the decreased level of CB1 receptor expression compared to the control group. In other word, CBD reverted the effects of SE in terms of neuroinflammation and CB1 receptor. Based on the obtained results, CBD was not able to restore the declined levels of CB2 or GABAA receptors. Conclusion: Our study found anticonvulsant effects of CBD on the SE rat model induced by lithium-pilocarpine with probable involvement of CB1 receptors and anti-inflammatory effects by reducing IL-1β and TNF-α markers independent of CB2 and GABAA receptors.

Clinical Outcomes and Systemic Complications Related to the Severity and Etiology of Status Epilepticus Using a Common Data Model

BACKGROUND

Status epilepticus (SE) is a critical neurological emergency in patients with neurological and nonneurological diseases. Mortality rises with SE severity. However, whether brain injury or systemic organ dysfunction causes death after SE remains unclear. We studied clinical outcomes and systemic dysfunctions associated with SE using standardized data from the common data model. This model includes clinical evaluations and treatments that provide real-world evidence for standard practice.

METHODS

This retrospective cohort study used the common data model database of a single tertiary academic medical center. Patients diagnosed with SE (corresponding to G41 of the International Classification of Diseases 10 and administration of antiseizure medication) between January 1, 2001, and January 1, 2018, were enrolled. Demographics, classifications of SE severity, and outcomes were collected as operational definitions by using a common data model format. Systemic complications were defined based on the Sequential Organ Failure Assessment criteria.

RESULTS

The electronic medical records of 1,825,196 patients were transformed into a common data model, and 410 patients were enrolled. The proportion of patients classified as having nonrefractory SE was 65.4% (268/410), followed by refractory (28.5%, 117/410) and super-refractory SE (6.1%, 25/410). Patients with more severe SE had longer intensive care unit and hospital stays. Renal dysfunction and thrombocytopenia were higher in the in-hospital death group (P = 0.002 and 0.003, respectively). In multivariable analysis, the Acute Physiology and Chronic Health Evaluation II score and platelet count were significantly different in the in-hospital death group (odds ratio, 1.169, P = 0.004; and 0.989, P = 0.043).

CONCLUSIONS

Systemic complications after SE, especially low platelet counts, were linked to worse outcomes and increased mortality in a common data model. The common data model offers expandability and comprehensive analysis, making it a potentially valuable tool for SE research.

Impact of frailty, biomarkers and basic biochemical parameters on outcomes of comatose patients in status epilepticus: a single-center prospective pilot study

BACKGROUND

Status epilepticus (SE) is a severe acute condition in neurocritical care with high mortality. Searching for risk factors affecting the prognosis in SE remains a significant issue. The primary study's aim was to test the predictive values of the Clinical Frailty Scale (CFS) and the Modified 11-item Frailty Index (mFI-11), the biomarkers and basic biochemical parameters collected at ICU on the Glasgow Outcome Scale (GOS) assessed at hospital discharge (hosp), and three months later (3 M), in comatose patients with SE. The secondary aim was to focus on the association between the patient's state at admission and the duration of mechanical ventilation, the ICU, and hospital stay.

METHODS

In two years single-centre prospective pilot study enrolling 30 adult neurocritical care patients with SE classified as Convulsive SE, A.1 category according to the International League Against Epilepsy (ILAE) Task Force without an-/hypoxic encephalopathy, we evaluated predictive powers of CFS, mFI-11, admission Status Epilepticus Severity Score (STESS), serum protein S100, serum Troponin T and basic biochemical parameters on prognosticating GOS using univariate linear regression, logistic regression and Receiver Operating Characteristic (ROC) analysis.

RESULTS

Our study included 60% males, with a mean age of 57 ± 16 years (44-68) and a mean BMI of 27 ± 5.6. We found CFS, mFI-11, STESS, and age statistically associated with GOS at hospital discharge and three months later. Among the biomarkers, serum troponin T level affected GOS hosp (p = 0.027). Serum C-reactive protein significance in prognosticating GOS was found by logistic regression (hosp p = 0.008; 3 M p = 0.004), and serum calcium by linear regression (hosp p = 0.028; 3 M p = 0.015). In relation to secondary outcomes, we found associations between the length of hospital stay and each of the following: age (p = 0.03), STESS (p = 0.009), and serum troponin T (p = 0.029) parameters.

CONCLUSIONS

This pilot study found promising predictive powers of two frailty scores, namely CFS and mFI-11, which were comparable to age and STESS predictors regarding the GOS at hospital discharge and three months later in ICU patients with SE. Among biomarkers and biochemical parameters, only serum troponin T level affected GOS at hospital discharge.

Comparison of induction agents for rapid sequence intubation in refractory status epilepticus: A single-center retrospective analysis

Endotracheal intubation, frequently required during management of refractory status epilepticus (RSE), can be facilitated by anesthetic medications; however, their effectiveness for RSE control is unknown. We performed a single-center retrospective review of patients admitted to a neurocritical care unit (NCCU) who underwent in-hospital intubation during RSE management. Patients intubated with propofol, ketamine, or benzodiazepines, termed anti-seizure induction (ASI), were compared to patients who received etomidate induction (EI). The primary endpoint was clinical or electrographic seizures within 12 h post-intubation. We estimated the association of ASI on post-intubation seizure using logistic regression. A sub-group of patients undergoing electroencephalography during intubation was identified to evaluate the immediate effect of ASI on RSE. We screened 697 patients admitted to the NCCU for RSE and identified 148 intubated in-hospital (n = 90 ASI, n = 58 EI). There was no difference in post-intubation seizure (26 % (n = 23) ASI, 29 % (n = 17) EI) in the cohort, however, there was increased RSE resolution with ASI in 24 patients with electrographic RSE during intubation (ASI: 61 % (n = 11/18) vs EI: 0 % (n = 0/6), p =.016). While anti-seizure induction did not appear to affect post-intubation seizure occurrence overall, a sub-group of patients undergoing electroencephalography during intubation had a higher incidence of seizure cessation, suggesting potential benefit in an enriched population.

Interpretable machine learning models for predicting 90-day death in patients in the intensive care unit with epilepsy

PURPOSE

This study aims to develop a machine learning-based model for predicting mortality risk in patients with epilepsy admitted to the intensive care unit (ICU), providing clinicians with an accurate prognostic tool to guide individualized treatment.

METHODS

We collected clinical data from clinical databases (MIMIC IV and eICU-CRD) of epilepsy patients 24 h after ICU admission. The clinical characteristics of ICU patients with epilepsy were carefully feature selected and processed. MIMIC IV as the training set and eICU-CRD database as the test set. Six models were developed and validated, and the best LightGBM model was selected by performance comparison and analysed for interpretability.

RESULTS

The final cohort comprised 429 patients for training and 1217 for testing. The training set exhibited a 90-day mortality rate of 9.32 %, and the test set had an in-hospital 90-day mortality rate of 4.10 %. Utilizing the LightGBM model, we achieved an AUC of 0.956 in the training set. External validation demonstrated promising results with accuracy of 0.898, precision of 0.975, AUC of 0.781, F1 score of 0.945, highlighting the model's potential for guiding clinical decision-making. Significant factors influencing model performance included the severity of illness, as measured by the OASIS score, and clinical parameters like heart rate and body temperature.

CONCLUSION

This study introduces a machine learning-based approach to predict mortality risk in ICU epilepsy patients, offering a valuable tool for clinicians to identify high-risk individuals and devise personalized treatment strategies, thus improving patient prognosis and treatment outcomes.

Nonconvulsive status epilepticus in neurocritical care: A critical reappraisal of outcome prediction scores

OBJECTIVE

Nonconvulsive status epilepticus (NCSE) is a frequent condition in the neurocritical care unit (NCCU) patient population, with high morbidity and mortality. We aimed to assess the validity of available outcome prediction scores for prognostication in an NCCU patient population in relation to their admission reason (NCSE vs. non-NCSE related).

METHODS

All 196 consecutive patients diagnosed with NCSE during the NCCU stay between January 2010 and December 2020 were included. Demographics, Simplified Acute Physiology Score II (SAPS II), NCSE characteristics, and in-hospital and 3-month outcome were extracted from the electronic charts. Status Epilepticus Severity Score (STESS), Epidemiology-Based Mortality Score in Status Epilepticus (EMSE), and encephalitis, NCSE, diazepam resistance, imaging features, and tracheal intubation score (END-IT) were evaluated as previously described. Univariable and multivariable analysis and comparison of sensitivity/specificity/positive and negative predictive values/accuracy were performed.

RESULTS

A total of 30.1% died during the hospital stay, and 63.5% of survivors did not achieve favorable outcome at 3 months after onset of NCSE. Patients admitted primarily due to NCSE had longer NCSE duration and were more likely to be intubated at diagnosis. The receiver operating characteristic (ROC) for SAPS II, EMSE, and STESS when predicting mortality was between .683 and .762. The ROC for SAPS II, EMSE, STESS, and END-IT when predicting 3-month outcome was between .649 and .710. The accuracy in predicting mortality/outcome was low, when considering both proposed cutoffs and optimized cutoffs (estimated using the Youden Index) as well as when adjusting for admission reason.

SIGNIFICANCE

The scores EMSE, STESS, and END-IT perform poorly when predicting outcome of patients with NCSE in an NCCU environment. They should be interpreted cautiously and only in conjunction with other clinical data in this particular patient group.