Prediction of functional outcome in patients with convulsive status epilepticus: the END-IT score.

BACKGROUND: Prediction of the functional outcome for patients with convulsive status epilepticus (CSE) has been a challenge. The aim of this study was to characterize the prognostic factors and functional outcomes of patients after CSE in order to develop a practicable scoring system for outcome prediction. METHODS: We performed a retrospective explorative analysis on consecutive patients diagnosed with CSE between March, 2008 and November, 2014 in a tertiary academic medical center in northwest China. The modified Rankin Scale (mRS) was used to measure the functional outcome at three months post discharge. RESULTS: A total of 132 CSE patients was included, with a median age of 25.5 years and 60.6% were male. Three months post discharge, an unfavorable outcome with mRS of 3-6 was seen in 62 (47.0%) patients, 25 (18.9%) of whom died. Logistic regression analysis revealed that encephalitis (p = 0.029), nonconvulsive SE (p = 0.018), diazepam resistance (p = 0.005), image abnormalities (unilateral lesions, p = 0.027; bilateral lesions or diffuse cerebral edema, p < 0.001) and tracheal intubation (p = 0.032) were significant independent predictors for unfavorable outcomes. Based on the coefficients in the model, these predictors were assigned a value of 1 point each, with the exception of the image, creating a 6-point scoring system, which we refer to as END-IT, for the outcome prediction of CSE. The area under the receiver operating characteristic curve for the END-IT score was 0.833 and using a cut-off point of 3 produced the highest sum sensitivity (83.9%) and specificity (68.6%). Compared with status epilepticus severity score (STESS) and Epidemiology-based Mortality score in SE (EMSE), END-IT score showed better discriminative power and predictive accuracy for the outcome prediction. CONCLUSIONS: We developed an END-IT score with a strong discriminative power for predicting the functional outcome of CSE patients. External prospective validation in different cohorts is needed for END-IT score.

Development of a simple score to predict outcome for unresponsive wakefulness syndrome.

INTRODUCTION: Accurate assessment of prognosis for patients with unresponsive wakefulness syndrome (UWS; formerly vegetative state) may help clinicians and families guide the type and intensity of therapy; however, there is no suitable and accurate means to predict the outcome so far. We aimed to develop a simple bedside scoring system to predict the likelihood of awareness recovery in patients with UWS. METHODS: We prospectively enrolled 56 patients (age range 10 to 73 years) with UWS 3 to 12 weeks post-onset. We collected demographic data and performed neurological, serological and neurophysiological tests at study entry. Each patient received a one year follow-up, during which awareness recovery was assessed by experienced physicians on the basis of clinical criteria. Univariate and multivariable analyses were employed to assess the relationships between predictors and awareness recovery. RESULTS: A total of 56 participants were included in the study; of these, 24 patients recovered awareness, 3 with moderate disabilities, 8 with severe disabilities, 12 were in a minimally conscious state, and 1 died after recovery. During the study, 23 patients remained in UWS and 9 died in UWS. Motor response, type of brain injury, electroencephalogram reactivity, sleep spindles and N20 were shown to be independent predictors for awareness recovery. Based on their coefficients in the model, we assigned these predictors with 1 point each and created a 5-point score for prediction of awareness recovery. The resulting score showed good predictive accuracy in the derivation cohort. The area under the receiver operating characteristic curve for the score was 0.918 with 87.50% sensitivity. CONCLUSION: This simple bedside prognostic score can be used to predict the probability of awareness recovery in UWS, thus provide families and clinicians with useful outcome information.

Long-Term Outcomes among Patients with Prolonged Disorders of Consciousness.

PURPOSE: To evaluate the long-term survival and functional outcomes of patients with prolonged disorders of consciousness (pDoC) 1-8 years after brain injuries. METHODS: Retrospective study to assess the long-term survival and functional outcomes of patients with pDoC was conducted. We performed Cox regression and multivariate logistic regression to calculate hazard ratios (HRs) for the outcome of survival and to identify risk factors of the functional outcome. RESULTS: We recruited 154 patients with pDoC. The duration of follow-up from disease onset was 1-8 years. The median age was 46 years (IQR, 32-59), and 65.6% (n = 101) of them were men. During the follow-up period, one hundred and ten patients (71.4%) survived; among them, 52 patients had a good outcome. From the overall survival curve, the 1-, 3-, and 8-year survival rates of patients were about 80.5%, 72.0%, and 69.7%, respectively. Cox regression analysis revealed a significant association between the lower APACHE II score (p = 0.005) (cut-off score ≥ 18) and the presence of sleep spindles (p = 0.001) with survival. Logistic regression analysis demonstrated a higher CRS-R score (cut-off score ≥ 7), and presence of sleep spindles were related to a favorable outcome among patients with pDoC. CONCLUSIONS: Sleep spindles are correlated with both long-term survival and long-term functional outcome in pDoC patients.

Detecting residual brain networks in disorders of consciousness: A resting-state fNIRS study.

Functional near infrared spectroscopy (fNIRS) is an emerging non-invasive technique that allows bedside measurement of blood oxygenation level-dependent hemodynamic signals. We aimed to examine the efficacy of resting-state fNIRS in detecting the residual functional networks in patients with disorders of consciousness (DOC). We performed resting-state fNIRS in 23 DOC patients of whom 12 were in minimally conscious state (MCS) and 11 were in unresponsive wakefulness state (UWS). Ten regions of interest (ROIs) in the prefrontal cortex (PFC) were selected: both sides of Brodmann area (BA) 9, BA10, BA44, BA45, and BA46. Graph-theoretical analysis and seed-based correlation analyses were used to investigate the network topology and the strength of pairwise connections between ROIs and channels. MCS and UWS exhibited varying degrees of the loss of topological architecture, and the regional nodal properties of BA10 were significantly different between them (Nodal degree, PLeft BA10 = 0.01, PRight BA10 < 0.01; nodal efficiency, PLeft BA10 = 0.03, PRight BA10 < 0.01). Compared to healthy controls, UWS had impaired functions in both short- and long-distance connectivity, however, MCS had significantly impaired functions only in long-distance connectivity. The functional connectivity of right BA10 (AUC = 0.88) and the connections between left BA46 and right BA10 (AUC = 0.86) had excellent performance in differentiating MCS and UWS. MCS and UWS have different patterns of topological architecture and short- and long-distance connectivity in PFC. Intraconnections within BA10 and interhemispheric connections between BA10 and 46 are excellent resting-state fNIRS classifiers for distinguishing between MCS and UWS.

Development and validation of a nomogram to provide individualized predictions of functional outcomes in patients with convulsive status epilepticus at 3 months: The modified END-IT tool.

AIMS: The prediction of outcomes in convulsive status epilepticus (CSE) remains a constant challenge. The Encephalitis-Nonconvulsive Status Epilepticus-Diazepam Resistance-Image Abnormalities-Tracheal Intubation (END-IT) score was a useful tool for predicting the functional outcomes of CSE patients, excluding cerebral hypoxia patients. With further understanding of CSE, and in view of the deficiencies of END-IT itself, we consider it necessary to modify the prediction tool. METHODS: The prediction model was designed from a cohort of CSE patients from Xijing Hospital (China), between 2008 and 2020. The enrolled subjects were randomly divided into training cohort and validation cohort as a ratio of 2:1. The logistic regression analysis was performed to identify the predictors and construct the nomogram. The performance of the nomogram was assessed by calculating the concordance index, and creating calibration plots to check the consistency between the predicted probabilities of poor prognosis and the actual outcomes of CSE. RESULTS: The training cohort included 131 patients and validation cohort included 66 patients. Variables included in the nomogram were age, etiology of CSE, non-convulsive SE, mechanical ventilation, abnormal albumin level at CSE onset. The concordance index of the nomogram in the training and validation cohorts was 0.853 (95% CI, 0.787-0.920) and 0.806 (95% CI, 0.683-0.923), respectively. The calibration plots showed an adequate consistency between the reported and predicted unfavorable outcomes of patients with CSE at 3 months after discharge. CONCLUSIONS: A nomogram for predicting the individualized risks of poor functional outcomes in CSE was constructed and validated, which has been an important modification of END-IT score.

Prognostic roles of sleep electroencephalography pattern and circadian rhythm biomarkers in the recovery of consciousness in patients with coma: a prospective cohort study.

OBJECTIVE: To investigate the potential prognostic value of sleep electroencephalography (EEG) pattern and serum circadian rhythm biomarkers in the recovery of consciousness in patients at the acute stage of coma. METHODS: A prospective observational study which included 75 patients with coma was conducted. Twenty-four-hour continuous polysomnography (PSG) was performed to determine the sleep EEG pattern according to the modified Valente's Grade (mVG) that we proposed. Serum levels of melatonin and orexin-A at four consecutive time points during the PSG were examined. Patients were then followed for one month to determine their level of consciousness. Multivariate logistic regression analysis was performed to examine associations between demographics, aetiology, baseline clinical features (pupillary and corneal reflex, and neuron-specific enolase [NSE]), clinical scores (Glasgow Coma Scale-Motor Response [GCS-M], Full Outline of Unresponsiveness [FOUR] scale, Acute Physiology and Chronic Health Evaluation II [APACHE II] scale), mVG, serum circadian biomarkers, and recovery of consciousness within one month. RESULTS: Within one month of enrolment, 34 patients regained consciousness and 36 patients remained non-conscious. Spearman rank correlation revealed a significant association between mVG and state of consciousness after one month. Significant variation in serum melatonin or orexin-A was not detected in either the conscious or non-conscious groups. Hypoxic aetiology, APACHE II, and mVG were independently associated with recovery of consciousness within one month. CONCLUSION: Sleep EEG structure, hypoxic aetiology, and APACHE II can independently predict recovery of consciousness in patients with acute coma. Taken together, we encourage neurologists to use sleep elements to assess patients with acute coma.

Development and validation of a new score for predicting functional outcome of neurocritically ill patients: The INCNS score.

AIMS: To develop and validate a novel score for prediction of 3-month functional outcome in neurocritically ill patients. METHODS: The development of the novel score was based on two widely used scores for general critical illnesses (Acute Physiology and Chronic Health Evaluation II, APACHE II; Simplified Acute Physiology Score II, SAPS II) and consideration of the characteristics of neurocritical illness. Data from consecutive patients admitted to neurological ICU (N-ICU) between January 2013 and June 2016 were used for the validation. The modified Rankin Scale (mRS) was used to evaluate 3-month functional outcomes. APACHE II scores, SAPS II scores, and our novel scores at 24 hours and 72 hours in N-ICU were obtained. We compared the prognostic performance of our score with APACHE II and SAPS II. RESULTS: We developed a 44-point scoring system named the INCNS score, and it includes 19 items which were categorized into five parts: inflammation (I), nutrition (N), consciousness (C), neurological function (N), and systemic function (S). We validated the INCNS score with a cohort of 941 N-ICU patients. The 72-hours INCNS score achieved an area under the receiver operating characteristic curve (AUC) of 0.828 (95% CI: 0.802-0.854), and the 24-hours INCNS score achieved an AUC of 0.788 (95% CI: 0.759-0.817). The INCNS score exhibited significantly better discriminative and prognostic performance than APACHE II and SAPS II at both 24 hours and 72 hours in N-ICU. CONCLUSION: We developed an INCNS score with superior predictive power for functional outcome of neurocritically ill patients.

Predictive value of EEG-awakening for behavioral awakening from coma.

BACKGROUND: A reliable predictor for early recovery of consciousness in comatose patients is of great clinical significance. Here we aimed to investigate the potentially prognostic value of electroencephalogram-reactivity (EEG-R) in combination with sleep spindles, termed EEG-awakening, for behavioral awakening in etiologically diverse comatose patients. METHODS: We performed a prospectively observational study on a sample of patients, all of whom were in coma lasting longer than 3 days. Continuous EEG monitoring was performed for at least 24 h to detect the presence of EEG-R and sleep spindles. We then followed patients for 1 month to determine their subsequent level of consciousness, classifying them as either awakened or non-awakened. Finally, Univariate and multivariate analyses were employed to assess the association of predictors with consciousness recovery. RESULTS: One hundred and six patients with different etiologies leading to coma were included in the study. Of these, 48 patients (45.3 %) awoke and 58 patients (54.7 %) did not awake in the month after the onset of the study. Of note, 26 patients (24.5 %) had a good neurological outcome, and 31 patients (29.3 %) died. Univariate analysis revealed that the Glasgow Coma Scale (GCS) score, EEG-R, sleep spindles, and EEG-awakening were all associated with one-month awakening. Comparisons of the area under the receiving operator characteristic curve (AUC) showed that EEG-awakening (0.839; 0.757-0.921) was superior to all of the following: EEG-R (0.798; 0.710-0.886), sleep spindles (0.772; 0.680-0.864), and GCS scores (0.720; 0.623-0.818). However, age, gender, etiology, and pupillary light reflex did not correlate significantly with one-month awakening. Further logistic regression analysis showed that only EEG-awakening and GCS scores at study entry were significant independent predictors of awakening and that the prognostic model containing these two variables yielded an outstanding predictive performance with an AUC of 0.903. CONCLUSIONS: EEG-awakening incorporates both EEG-R and sleep spindles and is an excellent predictor for early behavioral awakening in comatose patients. The prognostic model combining EEG-awakening and GCS scores shows an outstanding discriminative power for awakening.

Brain response to thermal stimulation predicts outcome of patients with chronic disorders of consciousness.

OBJECTIVE: To study the role of brain responses to thermal stimulation in outcome prediction of patients in either vegetative or minimally conscious states. METHODS: We performed a prospective study with 22 patients and used functional magnetic resonance imaging (fMRI) and EEG reactivity (EEG-R) tests in conjunction with thermal stimulation. We conducted thermal stimulation on patients by stimulating either their feet (fMRI) or hands (EEEG-R) with warm water (42±2°C). Each patient received a 1-year follow-up. RESULTS: Among the 22 patients, 1 was lost to follow- up, 10 had improved outcomes, and the remaining 11 patients showed no improvement. Thermal stimulation induced three different fMRI brain activation patterns: (1) high-order activation in 4 patients, (2) primary activation in 6 patients, and (3) no activation in 11 patients. Eight of the 10 patients with either high-order or primary activation had an improved outcome. Contrastingly, only 2 of the 11 patients with no activation pattern showed improvement. EEG-R was elicited in 11 patients and 9 of them showed improved outcomes. However, among the 10 patients with no EEG-R, 9 patients did not improve. CONCLUSIONS: Using fMRI and EEG to measure brain responses to thermal stimulation is capable of predicting patient outcomes with a high degree of predictive accuracy. SIGNIFICANCE: Thermal stimulation can be used as an objective and quantifiable somatosensory stimulation mode for clinical EEG-R and fMRI tests.

Neuroprotective effects of (-)-epigallocatechin-3-gallate (EGCG) on paraquat-induced apoptosis in PC12 cells.

Green tea, owing to its beneficial effect on health, is becoming more and more popular worldwide. (-)-Epigallocatechin-3-gallate (EGCG), the main ingredient of green tea polyphenols, is a known protective effect on injured neurons in neurodegenerative disease, such as Alzheimer's disease and Parkinson's disease. Paraquat (PQ) is a widely used herbicide that possesses a similar structure to MPP(+) and is toxic to mesencephalic dopaminergic neurons. In the present study, PQ-injured PC12 cells were chosen as an in vitro cell model of Parkinson's disease and the neuroprotective effects of EGCG were investigated. The results showed that EGCG attenuated apoptosis of PC12 cells induced by PQ. The possible mechanism may be associated with maintaining mitochondrial membrane potential, inhibiting caspase-3 activity and downregulating the expression of pro-apoptotic protein Smac in cytosol. The present study supports the notion that EGCG could be used as a neuroprotective agent for treatment of neurodegenerative diseases.