Outcome prediction in disorders of consciousness: the role of coma recovery scale revised

A Pluridisciplinary Tracheostomy Weaning Protocol for Brain-Injured Patients, Outside of the Intensive Care Unit and Without Instrumental Assessment: Results of Pilot Study

Concurrently to the recent development of percutaneous tracheostomy techniques in the intensive care unit (ICU), the amount of tracheostomized brain-injured patients has increased. Despites its advantages, tracheostomy may represent an obstacle to their orientation towards conventional hospitalization or rehabilitation services. To date, there is no recommendation for tracheostomy weaning outside of the ICU. We created a pluridisciplinary tracheostomy weaning protocol relying on standardized criteria but adapted to each patient's characteristics and that does not require instrumental assessment. It was tested in a prospective, single-centre, non-randomized cohort study. Inclusion criteria were age > 18 years, hospitalized for an acquired brain injury (ABI), tracheostomized during an ICU stay, and weaned from mechanical ventilation. The exclusion criterion was severe malnutrition. Decannulation failure was defined as recannulation within 96 h after decannulation. Thirty tracheostomized ABI patients from our neurosurgery department were successively and exhaustively included after ICU discharge. Twenty-six patients were decannulated (decannulation rate, 90%). None of them were recannulated (success rate, 100%). Two patients never reached the decannulation stage. Two patients died during the procedure. Mean tracheostomy weaning duration (inclusion to decannulation) was 7.6 (standard deviation [SD]: 4.6) days and mean total tracheostomy time (insertion to decannulation) was 42.5 (SD: 24.8) days. Our results demonstrate that our protocol might be able to determine without instrumental assessment which patient can be successfully decannulated. Therefore, it may be used safely outside ICU or a specialized unit. Moreover, our tracheostomy weaning duration is very short as compared to the current literature.

Predictive Factors for Successful Decannulation in Patients with Tracheostomies and Brain Injuries: A Systematic Review

Neurological patients frequently have disorders of consciousness, swallowing disorders, or neurological states that are incompatible with extubation. Therefore, they frequently require tracheostomies during their stay in an intensive care unit. After the acute phase, tracheostomy weaning and decannulation are generally expected to promote rehabilitation. However, few reliable predictive factors (PFs) for decannulation have been identified in this patient population. We sought to identify PFs that may be used during tracheostomy weaning and decannulation in patients with brain injuries. We conducted a systematic review of the literature regarding potential PFs for decannulation; searches were performed on 16 March 2021 and 1 June 2022. The following databases were searched: MEDLINE, EMBASE, CINAHL, Scopus, Web of Science, PEDro, OPENGREY, OPENSIGLE, Science Direct, CLINICAL TRIALS and CENTRAL. We searched for all article types, except systematic reviews, meta-analyses, abstracts, and position articles. Retrieved articles were published in English or French, with no date restriction. In total, 1433 articles were identified; 26 of these were eligible for inclusion in the review. PFs for successful decannulation in patients with acquired brain injuries (ABIs) included high neurological status, traumatic brain injuries rather than stroke or anoxic brain lesions, younger age, effective swallowing, an effective cough, and the absence of pulmonary infections. Secondary PFs included early tracheostomy, supratentorial lesions, the absence of critical illness polyneuropathy/myopathy, and the absence of tracheal lesions. To our knowledge, this is the first systematic review to identify PFs for decannulation in patients with ABIs. These PFs may be used by clinicians during tracheostomy weaning.

Establishment and validation of a bad outcomes prediction model based on EEG and clinical parameters in prolonged disorder of consciousness

Objective

This study aimed to explore the electroencephalogram (EEG) indicators and clinical factors that may lead to poor prognosis in patients with prolonged disorder of consciousness (pDOC), and establish and verify a clinical predictive model based on these factors.

Methods

This study included 134 patients suffering from prolonged disorder of consciousness enrolled in our department of neurosurgery. We collected the data of sex, age, etiology, coma recovery scales (CRS-R) score, complications, blood routine, liver function, coagulation and other laboratory tests, resting EEG data and follow-up after discharge. These patients were divided into two groups: training set (n = 107) and verification set (n = 27). These patients were divided into a training set of 107 and a validation set of 27 for this study. Univariate and multivariate regression analysis were used to determine the factors affecting the poor prognosis of pDOC and to establish nomogram model. We use the receiver operating characteristic (ROC) and calibration curves to quantitatively test the effectiveness of the training set and the verification set. In order to further verify the clinical practical value of the model, we use decision curve analysis (DCA) to evaluate the model.

Result

The results from univariate and multivariate logistic regression analyses suggested that an increased frequency of occurrence microstate A, reduced CRS-R scores at the time of admission, the presence of episodes associated with paroxysmal sympathetic hyperactivity (PSH), and decreased fibrinogen levels all function as independent prognostic factors. These factors were used to construct the nomogram. The training and verification sets had areas under the curve of 0.854 and 0.920, respectively. Calibration curves and DCA demonstrated good model performance and significant clinical benefits in both sets.

Conclusion

This study is based on the use of clinically available and low-cost clinical indicators combined with EEG to construct a highly applicable and accurate model for predicting the adverse prognosis of patients with prolonged disorder of consciousness. It provides an objective and reliable tool for clinicians to evaluate the prognosis of prolonged disorder of consciousness, and helps clinicians to provide personalized clinical care and decision-making for patients with prolonged disorder of consciousness and their families.

EEG connectivity and network analyses predict outcome in patients with disorders of consciousness - A systematic review and meta-analysis

Outcome prediction in prolonged disorders of consciousness (DOC) remains challenging. This can result in either inappropriate withdrawal of treatment or unnecessary prolongation of treatment. Electroencephalography (EEG) is a cheap, portable, and non-invasive device with various opportunities for complex signal analysis. Computational EEG measures, such as EEG connectivity and network metrics, might be ideal candidates for the investigation of DOC, but their capacity in prognostication is still undisclosed. We conducted a meta-analysis aiming to compare the prognostic power of the widely used clinical scale, Coma Recovery Scale-Revised - CRS-R and EEG connectivity and network metrics. We found that the prognostic power of the CRS-R scale was moderate (AUC: 0.67 (0.60-0.75)), but EEG connectivity and network metrics predicted outcome with significantly (p = 0.0071) higher accuracy (AUC:0.78 (0.70-0.86)). We also estimated the prognostic capacity of EEG spectral power, which was not significantly (p = 0.3943) inferior to that of the EEG connectivity and graph-theory measures (AUC:0.75 (0.70-0.80)). Multivariate automated outcome prediction tools seemed to outperform clinical and EEG markers.

Neuroimmune activation is associated with neurological outcome in anoxic and traumatic coma

The pathophysiological underpinnings of critically disrupted brain connectomes resulting in coma are poorly understood. Inflammation is potentially an important but still undervalued factor. Here, we present a first-in-human prospective study using the 18-kDa translocator protein (TSPO) radioligand 18F-DPA714 for PET imaging to allow in vivo neuroimmune activation quantification in patients with coma (n = 17) following either anoxia or traumatic brain injuries in comparison with age- and sex-matched controls. Our findings yielded novel evidence of an early inflammatory component predominantly located within key cortical and subcortical brain structures that are putatively implicated in consciousness emergence and maintenance after severe brain injury (i.e. mesocircuit and frontoparietal networks). We observed that traumatic and anoxic patients with coma have distinct neuroimmune activation profiles, both in terms of intensity and spatial distribution. Finally, we demonstrated that both the total amount and specific distribution of PET-measurable neuroinflammation within the brain mesocircuit were associated with the patient's recovery potential. We suggest that our results can be developed for use both as a new neuroprognostication tool and as a promising biometric to guide future clinical trials targeting glial activity very early after severe brain injury.

Reliability and validation of the Japanese version of the coma recovery scale-revised (CRS-R)

PRIMARY OBJECTIVE

This study aimed to verify the reliability and validity of the Japanese version of the Coma Recovery Scale-Revised (CRS-R).

METHODS

Subjects included 59 patients with disorders of consciousness (DOC) due to acquired brain injury. To validate test-retest reliability, Evaluator A assessed the CRS-R twice on the same day (A1, A2). To examine inter-rater reliability, Evaluators A (A2) and B (B) assessed the CRS-R without a time interval. To test concurrent validity, Evaluator A (A1) assessed the CRS-R, Japan Coma Scale (JCS), and the Glasgow Coma Scale (GCS) consecutively. To validate diagnostic accuracy, we evaluated the degree of agreement between A1 and A2 and between A2 and B in their diagnosis of DOC by CRS-R.

RESULTS

The test-retest (ρ = 0.92) and inter- (ρ = 0.98) reliability of CRS-R were excellent" and Concurrent validity of CRS-R with JCS (ρ =  -0.82) and GCS (ρ = 0.92) were high. Results of DOC diagnosis were consistent for 48/59 cases (κ = 0.82) for A1 and A2 and for 54/59 cases (κ = 0.92) for A2 and B.

CONLCUSION

The Japanese version of the CRS-R may be as reliable and valid as the original English and other language versions.

Evaluation and Management of Disorders of Consciousness in the Acute Care Setting

Acute disorders of consciousness (DOC) are impairments in arousal and awareness that occur within 28 days of an initial injury and can result from a variety of insults. These states range from coma, unresponsive wakefulness, covert consciousness, minimal consciousness, to confusional state. It is important to perform thorough, serial examinations with particular emphasis on the level of consciousness, brainstem reflexes, and motor responses. Evaluation of acute DOC includes laboratory tests, imaging, and electrophysiology testing. Prognostication in the acute phase of DOC must be done cautiously, using open, frequent communication with families, and by acknowledging significant multidimensional uncertainty.

A predictive model for awakening in patients with prolonged disorders of consciousness after craniocerebral injury

This study aimed to develop and validate a nomogram to predict awakening at 1 year in patients with prolonged disorders of consciousness (pDOC). We retrospectively analyzed the data of 381 patients with pDOC at 2 centers. The data were randomly divided into training and validation sets using a ratio of 6:4. For the training set, univariate and multivariate logical regression analyses were used to identify the predictive variables. Receiver operating characteristic curves, calibration curves, and a decision curve analysis were utilized to assess the predictive accuracy, discriminative ability, and clinical utility of the model, respectively. The final model included age, Glasgow Coma Scale score, serum albumin level, and computed tomography midline shift, all of which had a significant effect on awakening after pDOC. For the 1-year awakening in the training set, the model had good discriminative power, with an area under the curve of 0.733 (95% confidence interval: 0.667-0.789). For the validation set, the area under the curve for 1-year awakening was 0.721 (95% confidence interval: 0.617-0.826). Model performance was good for both the training and validation sets according to calibration plots and decision curve analysis. We developed a precise, effective nomogram to assist clinicians in better assessing patients' outcomes, guiding clinical judgment, and personalizing the therapeutic process.

Prognosis and enhancement of recovery in disorders of consciousness

Disorders of consciousness after severe brain injury encompass conditions of coma, vegetative state/unresponsive wakefulness syndrome, and minimally conscious state. DoC clinical presentation pose perplexing challenges to medical professionals, researchers, and families alike. The outcome is uncertain in the first weeks to months after a brain injury, with families and medical providers often making important decisions that require certainty. Prognostication for individuals with these conditions has been the subject of intense scientific investigation that continues to strive for valid prognostic indicators and algorithms for predicting recovery of consciousness. This manuscript aims to provide an overview of the current clinical landscape surrounding prognosis and optimizing recovery in DoC and the current and future research that could improve prognostic accuracy after severe brain injury. Improved understanding of these factors will aid healthcare professionals in providing optimal care, fostering hope, and advocating for ethical practices in the management of individuals with DoC.

Predictors of long-term decannulation in patients with disorders of consciousness

Purpose

The predictors of tracheostomy decannulation in patients with disorders of consciousness (DOC) are not comprehensively understood, making prognosis difficult. The primary objective of this study was to identify predictors of tracheostomy decannulation in patients with disorders of consciousness (DOC). The secondary aim was to evaluate the feasibility and safety of the modified Evans blue dye test (MEBDT) in tracheostomized DOC patients.

Methods

This retrospective study included all patients with disorders of consciousness (DOC) who underwent tracheostomy and were admitted between January 2016 and September 2022. Age, sex, etiology, initial Glasgow coma scale (GCS), initial Coma Recovery Scale-Revised (CRS-R), diagnosis of unresponsive wakefulness syndrome (UWS) or minimal consciousness state (MCS), MEBDT, initial modified Rankin scale (mRS), and initial Functional Oral Intake Scale (FOIS) were collected upon study enrollment. The relationship between clinical characteristics and cannulation status was investigated through a Cox regression model.

Results

A total of 141 patients were included in the study. The average age of these patients was 52.5 ± 16.7 years, with 42 (29.8%) being women. During the study period, 86 subjects (61%) underwent successful decannulation. Univariate analysis revealed that decannulated patients exhibited a significantly better conscious state compared to those without decannulation (CRS-R: p < 0.001; GCS: p = 0.023; MCS vs. UWS: p < 0.001). Additionally, a negative modified Evans blue dye test (MEBDT) result was significantly associated with tracheostomy decannulation (p < 0.001). In the multivariate analysis, successful decannulation was associated with a higher level of consciousness (MCS vs. UWS, p < 0.001, HR = 6.694) and a negative MEBDT result (negative vs. positive, p = 0.006, HR = 1.873). The Kaplan-Meier analysis further demonstrated that MEBDT-negative patients and those in the MCS category had a higher probability of decannulation at 12 months (p < 0.001).

Conclusion

The findings of this study indicate that a negative MEBDT result and a higher level of consciousness can serve as predictive factors for successful tracheostomy decannulation in DOC patients.

Prognostication in Prolonged and Chronic Disorders of Consciousness

Patients with prolonged disorders of consciousness (DOCs) longer than 28 days may continue to make significant gains and achieve functional recovery. Occasionally, this recovery trajectory may extend past 3 (for nontraumatic etiologies) and 12 months (for traumatic etiologies) into the chronic period. Prognosis is influenced by several factors including state of DOC, etiology, and demographics. There are several testing modalities that may aid prognostication under active investigation including electroencephalography, functional and anatomic magnetic resonance imaging, and event-related potentials. At this time, only one treatment (amantadine) has been routinely recommended to improve functional recovery in prolonged DOC. Given that some patients with prolonged or chronic DOC have the potential to recover both consciousness and functional status, it is important for neurologists experienced in prognostication to remain involved in their care.

Electrophysiological characteristics of CM-pf in diagnosis and outcome of patients with disorders of consciousness

BACKGROUND

Deep brain stimulation (DBS) in the centromedian-parafascicular complex (CM-pf) has been reported as a potential therapeutic option for disorders of consciousness (DoC). However, the lack of understanding of its electrophysiological characteristics limits the improvement of therapeutic effect.

OBJECTIVE

To investigate the CM-pf electrophysiological characteristics underlying disorders of consciousness (DoC) and its recovery.

METHODS

We collected the CM-pf electrophysiological signals from 23 DoC patients who underwent central thalamus DBS (CT-DBS) surgery. Five typical electrophysiological features were extracted, including neuronal firing properties, multiunit activity (MUA) properties, signal stability, spike-MUA synchronization strength (syncMUA), and the background noise level. Their correlations with the consciousness level, the outcome, and the primary clinical factors of DoC were analyzed.

RESULTS

11 out of 23 patients (0/2 chronic coma, 5/13 unresponsive wakefulness syndrome/vegetative state (UWS/VS), 6/8 minimally conscious state minus (MCS-)) exhibited an improvement in the level of consciousness after CT-DBS. In CM-pf, significantly stronger gamma band syncMUA strength and alpha band normalized MUA power were found in MCS- patients. In addition, higher firing rates, stronger high-gamma band MUA power and alpha band normalized power, and more stable theta oscillation were correlated with better outcomes. Besides, we also identified electrophysiological properties that are correlated with clinical factors, including etiologies, age, and duration of DoC.

CONCLUSION

We provide comprehensive analyses of the electrophysiological characteristics of CM-pf in DoC patients. Our results support the 'mesocircuit' hypothesis, one proposed mechanism of DoC recovery, and reveal CM-pf electrophysiological features that are crucial for understanding the pathogenesis of DoC, predicting its recovery, and explaining the effect of clinical factors on DoC.

Clinical application of recommendations for neurobehavioral assessment in disorders of consciousness: an interdisciplinary approach

Accurate diagnosis, prognosis, and subsequent rehabilitation care planning for persons with Disorders of Consciousness (DoC) has historically posed a challenge for neurological care professionals. Evidence suggests rates of misdiagnosis may be as high as 40% when informal beside evaluations are used to determine level of consciousness. The presence of myriad medical, neurological, functional (motor, sensory, cognitive) and environmental confounds germane to these conditions complicates behavioral assessment. Achieving diagnostic certainty is elusive but critical to inform care planning, clinical decision making, and prognostication. Standardized neurobehavioral rating scales has been shown to improve accuracy in distinguishing between coma, unresponsive wakefulness syndrome/vegetative state and minimally consciousness state as compared to informal assessment methods. Thus, these scales are currently recommended for use as the informal "gold standard" for diagnostic assessment in DoC. The following paper will present an evidence-based approach to neurobehavioral assessment for use in clinical practice. Strategies for optimizing assessment and aiding in identification and management of confounds that can limit diagnostic accuracy will be provided. Finally, clinical application of an interdisciplinary approach to identifying and managing confounds will be discussed and how assessment results can be used to identify trends in performance and guide prognostic counseling with families.

Coma Recovery Scale-Revised Predicts Disability Rating Scale in Acute Rehabilitation of Severe Traumatic Brain Injury

OBJECTIVE

To explore the prognostic value of the Coma Recovery Scale-Revised (CRS-R) in predicting disability outcomes in patients with severe traumatic brain injury using the Disability Rating Scale (DRS).

DESIGN

Secondary analysis including linear and logistic regressions were performed.

SETTING

Data were collected in a previous clinical trial.

PARTICIPANTS

One hundred eighty-four participants across 3 countries (N=184).

MAIN OUTCOME MEASURES

Disability Rating Scales.

RESULTS

Analyses showed an inverse relation between CRS-R scores obtained at baseline and change in DRS scores at 6 weeks. Similarly, changes in CRS-R scores between baseline and 4 weeks were also found to have an inverse relation to change in DRS scores at 6 weeks.

CONCLUSIONS

This study generates a tool that can be used to predict the probability that a patient with severe traumatic brain injury lands in 1 of 3 disability categories. The CRS-R may be useful in prognostication of disability in patients with severe traumatic brain injury.

A functional near-infrared spectroscopy study on hemodynamic changes of patients with prolonged disorders of consciousness responding to different auditory stimuli

Treating prolonged disorders of consciousness (pDoC) is challenging. Thus, accurate assessment of residual consciousness in patients with pDoC is important for the management and recovery of patients. Functional near-infrared spectroscopy (fNIRS) can be used to detect brain activity through changes of oxygenated hemoglobin/deoxygenated hemoglobin (HbO/HbR) concentrations changes and has recently gained increasing attention for its potential applications in assessing residual consciousness. However, the number of fNIRS studies assessing residual awareness in patients with pDoC is still limited. In this study, fNIRS was used to evaluate the brain function in 18 patients with pDoC, including 14 vegetative states (VS) and 4 minimally conscious states (MCS), and 15 healthy controls (HC). All participants accepted two types of external stimuli, i.e., active stimulation (motor imagery, MI) and passive stimulation (subject's own name, SON). The results showed that the mean concentrations of HbO/HbR in the prefrontal cortex of the HC during the passive stimulation were significantly lower than those of the active stimulation, and the fitting slope was high. However, the hemodynamic responses of the patients with pDoC were opposite to those of the HC. Additionally, the mean concentrations of HbO/HbR increased as the level of consciousness decreased during passive stimulation. Our findings suggest that the residual level of consciousness in pDoC patients can be assessed by measuring brain responses to different stimulations using fNIRS. The present study further demonstrates the feasibility and reliability of fNIRS in assessing residual consciousness in patients with pDoC, providing a basis for its expanded clinical application.

Prognosis prediction for impaired consciousness recovery in stroke patients using videofluoroscopic swallowing study: A retrospective observational study

Prognosis prediction of impaired consciousness is clinically important for establishing therapeutic strategies, determining a rehabilitative goal and functional outcome, and estimating rehabilitative therapy duration. In this study, we investigated the prognosis prediction value of videofluoroscopic swallowing study (VFSS) in recovery of impaired consciousness in stroke patients. Fifty-one patients with impaired consciousness who underwent VFSS during the early stage of stroke between 2017 and 2021 were recruited in this retrospective study. VFSS were performed using modified Logemann protocol, and bonorex was used as the liquid contrast medium. The penetration-aspiration scale (PAS) was graded for all patients, and they were classified into 2 groups depending on the presence of aspiration on liquid material: the aspiration-positive group with a PAS score ≥ 6, and the aspiration-negative group with a PAS score < 6. The coma recovery scale-revised (CRS-R) was used to evaluate patients' conscious state at the time of VFSS and 3 months after. Statistical analysis was performed using independent t test and Pearson's correlation. The increase in total CRS-R score from time of VFSS to 3 months later was greater in aspiration-negative group than in aspiration-positive group (P < .05). A moderate negative correlation was observed between liquid PAS score and the increase in total CRS-R score (r = -0.499, P < .05). Among 6 CRS-R subscales, a strong negative correlation was observed between liquid PAS score and the communication score increase (r = -0.563, P < .05), while moderate negative correlations were detected between liquid PAS score and the increases in auditory (r = -0.465, P < .05), motor (r = -0.372, P < .05), oromotor (r = -0.426, P < .05), and arousal (r = -0.368, P < .05) scores. We observed that patients without aspiration on videofluoroscopic swallowing study showed better recovery of impaired consciousness, and the degree of penetration and aspiration had a predictive value for impaired consciousness prognosis in the early stage of stroke.

Machine learning and network analysis for diagnosis and prediction in disorders of consciousness

BACKGROUND

Prolonged Disorders of Consciousness (PDOC) resulting from severe acquired brain injury can lead to complex disabilities that make diagnosis challenging. The role of machine learning (ML) in diagnosing PDOC states and identifying intervention strategies is relatively under-explored, having focused on predicting mortality and poor outcome. This study aims to: (a) apply ML techniques to predict PDOC diagnostic states from variables obtained from two non-invasive neurobehavior assessment tools; and (b) apply network analysis for guiding possible intervention strategies.

METHODS

The Coma Recovery Scale-Revised (CRS-R) is a well-established tool for assessing patients with PDOC. More recently, music has been found to be a useful medium for assessment of coma patients, leading to the standardization of a music-based assessment of awareness: Music Therapy Assessment Tool for Awareness in Disorders of Consciousness (MATADOC). CRS-R and MATADOC data were collected from 74 PDOC patients aged 16-70 years at three specialist centers in the USA, UK and Ireland. The data were analyzed by three ML techniques (neural networks, decision trees and cluster analysis) as well as modelled through system-level network analysis.

RESULTS

PDOC diagnostic state can be predicted to a relatively high level of accuracy that sets a benchmark for future ML analysis using neurobehavioral data only. The outcomes of this study may also have implications for understanding the role of music therapy in interdisciplinary rehabilitation to help patients move from one coma state to another.

CONCLUSIONS

This study has shown how ML can derive rules for diagnosis of PDOC with data from two neurobehavioral tools without the need to harvest large clinical and imaging datasets. Network analysis using the measures obtained from these two non-invasive tools provides novel, system-level ways of interpreting possible transitions between PDOC states, leading to possible use in novel, next-generation decision-support systems for PDOC.

Sleep in Disorders of Consciousness: A Brief Overview on a Still under Investigated Issue

Consciousness is a multifaceted concept, involving both wakefulness, i.e., a condition of being alert that is regulated by the brainstem, and awareness, a subjective experience of any thoughts or perception or emotion. Recently, the European Academy of Neurology has published international guidelines for a better diagnosis of coma and other disorders of consciousness (DOC) through the investigation of sleep patterns, such as slow-wave and REM, and the study of the EEG using machine learning methods and artificial intelligence. The management of sleep disorders in DOC patients is an increasingly hot topic and deserves careful diagnosis, to allow for the most accurate prognosis and the best medical treatment possible. The aim of this review was to investigate the anatomo-physiological basis of the sleep/wake cycle, as well as the main sleep patterns and sleep disorders in patients with DOC. We found that the sleep characteristics in DOC patients are still controversial. DOC patients often present a theta/delta pattern, while epileptiform activity, as well as other sleep elements, have been reported as correlating with outcomes in patients with coma and DOC. The absence of spindles, as well as REM and K-complexes of NREM sleep, have been used as poor predictors for early awakening in DOC patients, especially in UWS patients. Therefore, sleep could be considered a marker of DOC recovery, and effective treatments for sleep disorders may either indirectly or directly favor recovery of consciousness.

A prediction model of clinical outcomes in prolonged disorders of consciousness: A prospective cohort study

Objective

This study aimed to establish and validate a prediction model for clinical outcomes in patients with prolonged disorders of consciousness (pDOC).

Methods

A total of 170 patients with pDOC enrolled in our rehabilitation unit were included and divided into training (n = 119) and validation sets (n = 51). Independent predictors for improved clinical outcomes were identified by univariate and multivariate logistic regression analyses, and a nomogram model was established. The nomogram performance was quantified using receiver operating curve (ROC) and calibration curves in the training and validated sets. A decision curve analysis (DCA) was performed to evaluate the clinical usefulness of this nomogram model.

Results

Univariate and multivariate logistic regression analyses indicated that age, diagnosis at entry, serum albumin (g/L), and pupillary reflex were the independent prognostic factors that were used to construct the nomogram. The area under the curve in the training and validation sets was 0.845 and 0.801, respectively. This nomogram model showed good calibration with good consistency between the actual and predicted probabilities of improved outcomes. The DCA demonstrated a higher net benefit in clinical decision-making compared to treating all or none.

Conclusion

Several feasible, cost-effective prognostic variables that are widely available in hospitals can provide an efficient and accurate prediction model for improved clinical outcomes and support clinicians to offer suitable clinical care and decision-making to patients with pDOC and their family members.

Predicting Outcome in Patients with Brain Injury: Differences between Machine Learning versus Conventional Statistics

Defining reliable tools for early prediction of outcome is the main target for physicians to guide care decisions in patients with brain injury. The application of machine learning (ML) is rapidly increasing in this field of study, but with a poor translation to clinical practice. This is basically dependent on the uncertainty about the advantages of this novel technique with respect to traditional approaches. In this review we address the main differences between ML techniques and traditional statistics (such as logistic regression, LR) applied for predicting outcome in patients with stroke and traumatic brain injury (TBI). Thirteen papers directly addressing the different performance among ML and LR methods were included in this review. Basically, ML algorithms do not outperform traditional regression approaches for outcome prediction in brain injury. Better performance of specific ML algorithms (such as Artificial neural networks) was mainly described in the stroke domain, but the high heterogeneity in features extracted from low-dimensional clinical data reduces the enthusiasm for applying this powerful method in clinical practice. To better capture and predict the dynamic changes in patients with brain injury during intensive care courses ML algorithms should be extended to high-dimensional data extracted from neuroimaging (structural and fMRI), EEG and genetics.

Rhythmic musical-electrical trigeminal nerve stimulation improves impaired consciousness

Accumulating evidence shows that consciousness is linked to neural oscillations in the thalamocortical system, suggesting that deficits in these oscillations may underlie disorders of consciousness (DOC). However, patient-friendly non-invasive treatments targeting this functional anomaly are still missing and the therapeutic value of oscillation restoration has remained unclear. We propose a novel approach that aims to restore DOC patients' thalamocortical oscillations by combining rhythmic trigeminal-nerve stimulation with comodulated musical stimulation ("musical-electrical TNS"). In a double-blind, placebo-controlled, parallel-group study, we recruited 63 patients with DOC and randomly assigned them to groups receiving gamma, beta, or sham musical-electrical TNS. The stimulation was applied for 40 min on five consecutive days. We measured patients' consciousness before and after the stimulation using behavioral indicators and neural responses to rhythmic auditory speech. We further assessed their outcomes one year later. We found that musical-electrical TNS reliably lead to improvements in consciousness and oscillatory brain activity at the stimulation frequency: 43.5 % of patients in the gamma group and 25 % of patients in the beta group showed an improvement of their diagnosis after being treated with the stimulation. This group of benefitting patients still showed more positive outcomes one year later. Moreover, patients with stronger behavioral benefits showed stronger improvements in oscillatory brain activity. These findings suggest that brain oscillations contribute to consciousness and that musical-electrical TNS may serve as a promising approach to improve consciousness and predict long-term outcomes in patients with DOC.

A simple intervention for disorders of consciousness- is there a light at the end of the tunnel?

Sleep is a physiological state necessary for memory processing, learning and brain plasticity. Patients with disorders of consciousness (DOC) show none or minimal sign of awareness of themselves or their environment but appear to have sleep-wake cycles. The aim of our study was to assess baseline circadian rhythms and sleep in patients with DOC; to optimize circadian rhythm using an intervention combining blue light, melatonin and caffeine, and to identify the impact of this intervention on brain function using event related potentials. We evaluated baseline circadian rhythms and sleep in 17 patients with DOC with 24-h polysomnography (PSG) and 4-hourly saliva melatonin measurements for 48 h. Ten of the 17 patients (5 female, age 30–71) were then treated for 5 weeks with melatonin each night and blue light and caffeine treatment in the mornings. Behavioral assessment of arousal and awareness [Coma recovery scale-revised (CRS-R)], 24-h polysomnography and 4-hourly saliva melatonin measurements, oddball mismatch negativity (MMN) and subject's own name (SON) experiments were performed twice at baseline and following intervention. Baseline sleep was abnormal in all patients. Cosinor analysis of saliva melatonin results revealed that averaged baseline % rhythmicity was low (M: 31%, Range: 13–66.4%, SD: 18.4). However, increase in % Melatonin Rhythm following intervention was statistically significant (p = 0.012). 7 patients showed improvement of CRS-R scores with intervention and this was statistically significant (p = 0.034). All the patients who had improvement of clinical scores also had statistically significant improvement of neurophysiological responses on MMN and SON experiments at group level (p = 0.001). Our study shows that sleep and circadian rhythms are severely deranged in DOC but optimization is possible with melatonin, caffeine and blue light treatment. Clinical and physiological parameters improved with this simple and inexpensive intervention. Optimization of sleep and circadian rhythms should be integrated into rehabilitation programs for people with DOC.

Effects of Acupuncture on Cortical Activation in Patients with Disorders of Consciousness: A Functional Near-Infrared Spectroscopy Study

Background. Disorder of consciousness (DoC) is a clinical condition caused by severe brain damage. Some studies have reported that acupuncture, a traditional Chinese treatment, could facilitate the recovery of the patient’s consciousness. The therapeutic effects of acupuncture may be due to its modulation of facilitating cortex (PFC) activity, but it has not been greatly demonstrated. Objectives. We intended to observe the effects of acupuncture on prefrontal cortical activity, explore the potential correlation between cortical activation and the severity of DoC, and analyze the functional brain network connectivity to provide a theoretical basis for its application in clinical practice. Methods. Participants diagnosed with DoC were included in the study. Before the intervention, we assessed the patient’s state of consciousness using relevant scales, such as the Glasgow coma scale (GCS) and the coma recovery scale-revised (CRS-R). All patients received acupuncture manipulation with the functional near-infrared spectroscopy (fNIRS) system monitored. Result. A total of 16 subjects participated in our study. We observed that the concentration of oxygenated hemoglobin (HbO) in the PFC was increased during the acupuncture manipulation and declined during the resting state. Then, the connection strength of the left cerebral cortex was generally higher than that of the right. Finally, we observed only a weak difference in hemodynamic responses of PFC between the vegetative state (VS) and minimally conscious state (MCS) groups. However, the difference was not statistically significant. Conclusion. Our results indicated that acupuncture can increase the concentration of HbO in the PFC and strengthen the connection strength of the left cerebral cortex. However, our present study did not find a significant correlation between the cortical hemodynamic response and the severity of DoC.

Behavioral Assessment of Patients with Disorders of Consciousness

Severe brain injury is associated with a period of impaired level of consciousness that can last from days to months and results in chronic impairment. Systematic assessment of level of function in patients with disorders of consciousness (DoC) is critical for diagnosis, prognostication, and evaluation of treatment efficacy. Approximately 40% of patients who are thought to be unconscious based on clinical bedside behavioral assessment demonstrate some signs of consciousness on standardized behavioral assessment. This finding, in addition to a growing body of literature demonstrating the advantages of standardized behavioral assessment of DoC, has led multiple professional societies and clinical guidelines to recommend standardized assessment over routine clinical evaluation of consciousness. Nevertheless, even standardized assessment is susceptible to biases and misdiagnosis, and examiners should consider factors, such as fluctuating arousal and aphasia, that may confound evaluation. We review approaches to behavioral assessment of consciousness, recent clinical guideline recommendations for use of specific measures to evaluate patients with DoC, and strategies for mitigating common biases that may confound the examination.

Predicting Outcome of Traumatic Brain Injury: Is Machine Learning the Best Way?

One of the main challenges in traumatic brain injury (TBI) patients is to achieve an early and definite prognosis. Despite the recent development of algorithms based on artificial intelligence for the identification of these prognostic factors relevant for clinical practice, the literature lacks a rigorous comparison among classical regression and machine learning (ML) models. This study aims at providing this comparison on a sample of TBI patients evaluated at baseline (T0), after 3 months from the event (T1), and at discharge (T2). A Classical Linear Regression Model (LM) was compared with independent performances of Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), Naïve Bayes (NB) and Decision Tree (DT) algorithms, together with an ensemble ML approach. The accuracy was similar among LM and ML algorithms on the analyzed sample when two classes of outcome (Positive vs. Negative) approach was used, whereas the NB algorithm showed the worst performance. This study highlights the utility of comparing traditional regression modeling to ML, particularly when using a small number of reliable predictor variables after TBI. The dataset of clinical data used to train ML algorithms will be publicly available to other researchers for future comparisons.

Efficacy of Transcranial Direct Current Stimulation Over Dorsolateral Prefrontal Cortex in Patients With Minimally Conscious State

Background

The treatment of patients in a minimally conscious state (MCS) remains challenging. Transcranial direct current stimulation (tDCS) is a non-invasive therapeutic method in treating neurologic diseases by regulating the cortical excitability. The aim is to investigate the effect of tDCS in patients with MCS in this study.

Methods

Eleven patients in MCS were enrolled in the study. All the patients received 5 daily sessions of 20-min sham tDCS, followed by 10 sessions of 20-min real tDCS. The anodal electrode and cathodal electrodes were placed over the left dorsolateral prefrontal cortex (DLPFC) and the right eyebrow, respectively. Assessment of Coma Recovery Scale-Revised (CRS-R) scores and resting-state functional MRI (rs-fMRI) scans was conducted three times in each patient: before tDCS (baseline, T0), post-sham tDCS at week 1 (T1), and post-real tDCS at week 2 (T2). The whole-brain functional connectivity (FC) was obtained by bilaterally computing FC from six seed regions: precuneus, middle frontal gyrus, supplemental motor area, angular gyrus, superior temporal gyrus, and occipital lobe. One-way repeated measure ANOVA was used to compare the differences of CRS-R scores and FC at T0, T1, and T2. The false discovery rate correction of p &lt; 0.001 was adopted for controlling multiple comparisons in FC analysis.

Results

Five patients with MCS showed obvious clinical improvement represented by increased CRS-R scores post- 2-week real tDCS. The CRS-R scores did not change post- 1-week sham treatment. No side effects were reported during the study. The FC of the bilateral supplementary motor area, right angular gyrus, and right superior temporal gyrus were significantly enhanced after 2-week real tDCS compared with that after 1-week sham-tDCS. In addition, FC of bilateral occipital lobe and right precuneus were significantly enhanced post- 2-week real tDCS compared with the baseline.

Conclusion

Our findings indicated that tDCS over DLPFC could serve as a potentially effective therapy for improving the consciousness state in patients with MCS. The FC in rs-fMRI can be modulated by tDCS at both the stimulation site (left DLPFC) and the distant regions.

One-year outcome of brain injured patients undergoing early neurological rehabilitation: a prospective observational study

Background

The present study intended to analyze the outcome of patients with severe brain injury one-year after discharge from early rehabilitation.

Methods

Early neurological rehabilitation patients admitted to intensive or intermediate care units and discharged between June 2018 and May 2020 were screened for eligibility. The level of consciousness was evaluated using the Coma Recovery Scale-Revised (CRS-R) upon admission and at discharge. At one-year follow-up, the outcome was assessed with the Glasgow Outcome Scale-extended (GOSE). Demographical and clinical data collected during inpatient rehabilitation were used to predict the outcome 1 year after discharge.

Results

Two hundred sixty-four patients (174 males, 90 females) with a median age of 62 years (IQR = 51–75) and a median duration of their disease of 18 days (IQR = 12–28) were included in the study. At follow-up, the mortality rate was 27% (n = 71). Age and discharge CRS-R total score were independent predictors in a Cox proportional hazards model with death (yes/no) as the dependent variable. According to the GOSE interviews, most patients were either dead (n = 71; 27%), in a vegetative state (n = 28; 11%) or had a severe disability (n = 124; 47%), whereas only a few patients showed a moderate disability (n = 18; 7%) or a good recovery (n = 23; 9%) 1 year after discharge. Age, non-traumatic etiology, discharge CRS-R total score and length of stay independently predicted whether the outcome was good or poor at follow-up.

Conclusion

Age was an important predictor for outcome at one-year follow-up, which might be due to altered brain plasticity and more comorbidities in elderly subjects. In addition, the present study demonstrated that the CRS-R total score at discharge might be more important for the prediction of one-year outcome than the initial assessment upon admission.

The neuroethics of disorders of consciousness: a brief history of evolving ideas

Neuroethical questions raised by recent advances in the diagnosis and treatment of disorders of consciousness are rapidly expanding, increasingly relevant, and yet underexplored. The aim of this thematic review is to provide a clinically applicable framework for understanding the current taxonomy of disorders of consciousness and to propose an approach to identifying and critically evaluating actionable neuroethical issues that are frequently encountered in research and clinical care for this vulnerable population. Increased awareness of these issues and clarity about opportunities for optimizing ethically-responsible care in this domain are especially timely given recent surges in critically ill patients with unusually prolonged disorders of consciousness associated with coronavirus disease 2019 (COVID-19) around the world. We begin with an overview of the field of neuroethics: what it is, its history and evolution in the context of biomedical ethics at large. We then explore nomenclature used in disorders of consciousness, covering categories proposed by the American Academy of Neurology, the American Congress of Rehabilitation Medicine, and the National Institute on Disability, Independent Living, and Rehabilitation Research, including definitions of terms such as coma, the vegetative state, unresponsive wakefulness syndrome, minimally conscious state, covert consciousness, and the confusional state. We discuss why these definitions matter, and why there has been such evolution in this nosology over the years, from Jennett and Plum in 1972 to the Multi-Society Task Force in 1994, the Aspen Working Group in 2002 and up until the 2018 American and 2020 European Disorders of Consciousness guidelines. We then move to a discussion of clinical aspects of disorders of consciousness, the natural history of recovery, and ethical issues that arise within the context of caring for persons with disorders of consciousness. We conclude with a discussion of key challenges associated with assessing residual consciousness in disorders of consciousness, potential solutions and future directions, including integration of crucial disability rights perspectives.

Treating Traumatic Brain Injuries with Electroceuticals: Implications for the Neuroanatomy of Consciousness

According to the Centers for Disease Control and Prevention (CDC), traumatic brain injury (TBI) is the leading cause of loss of consciousness, long-term disability, and death in children and young adults (age 1 to 44). Currently, there are no United States Food and Drug Administration (FDA) approved pharmacological treatments for post-TBI regeneration and recovery, particularly related to permanent disability and level of consciousness. In some cases, long-term disorders of consciousness (DoC) exist, including the vegetative state/unresponsive wakefulness syndrome (VS/UWS) characterized by the exhibition of reflexive behaviors only or a minimally conscious state (MCS) with few purposeful movements and reflexive behaviors. Electroceuticals, including non-invasive brain stimulation (NIBS), vagus nerve stimulation (VNS), and deep brain stimulation (DBS) have proved efficacious in some patients with TBI and DoC. In this review, we examine how electroceuticals have improved our understanding of the neuroanatomy of consciousness. However, the level of improvements in general arousal or basic bodily and visual pursuit that constitute clinically meaningful recovery on the Coma Recovery Scale-Revised (CRS-R) remain undefined. Nevertheless, these advancements demonstrate the importance of the vagal nerve, thalamus, reticular activating system, and cortico-striatal-thalamic-cortical loop in the process of consciousness recovery.

Novel approaches to prediction in severe brain injury

PURPOSE OF REVIEW

Recovery after severe brain injury is variable and challenging to accurately predict at the individual patient level. This review highlights new developments in clinical prognostication with a special focus on the prediction of consciousness and increasing reliance on methods from data science.

RECENT FINDINGS

Recent research has leveraged serum biomarkers, quantitative electroencephalography, MRI, and physiological time-series to build models for recovery prediction. The analysis of high-resolution data and the integration of features from different modalities can be approached with efficient computational techniques.

SUMMARY

Advances in neurophysiology and neuroimaging, in combination with computational methods, represent a novel paradigm for prediction of consciousness and functional recovery after severe brain injury. Research is needed to produce reliable, patient-level predictions that could meaningfully impact clinical decision making.

Epidemiology and clinical characteristics of patients discharged from the ICU in a vegetative or minimally conscious state

Purpose

A significant percentage of patients are discharged from intensive care units (ICU) with disorders of counciousness (DoC). The aim of this retrospective, case-control study was to compare patients discharged from the ICU in a vegetative state (VS) or minimally conscious state (MCS) and the rest of ICU survivors, and to identify independent predictors of DoC among ICU survivors.

Methods

Data from 14,368 adult ICU survivors identified in a Silesian Registry of Intensive Care Units (active in the Silesian Region of Poland between October 2010 and December 2019) were analyzed. Patients discharged from the ICU in a VS or MCS were compared to the remaining ICU survivors. Pre-admission and admission variables that independently influence ICU discharge with DoC were identified.

Results

Among the 14,368 analyzed adult ICU survivors, 1,064 (7.4%) were discharged from the ICU in a VS or MCS. The percentage of patients discharged from the ICU with DoC was similar in all age groups. Compared to non- DoC ICU patients, they had a higher mean APACHE II and SAPS III score at admission. Independent variables affecting ICU discharge with DoC included unconsciousness at ICU admission, cardiac arrest and craniocerebral trauma as primary cause of ICU admission, as well as a history of previous chronic neurological disorders and cerebral stroke (p<0.001).

Conclusion

Discharge in a VS and MCS was relatively frequent among ICU survivors. Discharge with DoC was more likely among patients who were unconscious at admission and admitted to the ICU due to cardiac arrest or craniocerebral trauma.

Recovery of Consciousness and Functional Outcome in Moderate and Severe Traumatic Brain Injury

Importance

Traumatic brain injury (TBI) leads to 2.9 million visits to US emergency departments annually and frequently involves a disorder of consciousness (DOC). Early treatment, including withdrawal of life-sustaining therapies and rehabilitation, is often predicated on the assumed worse outcome of disrupted consciousness.

Objective

To quantify the loss of consciousness, factors associated with recovery, and return to functional independence in a 31-year sample of patients with moderate or severe brain trauma.

Design, Setting, and Participants

This cohort study analyzed patients with TBI who were enrolled in the Traumatic Brain Injury Model Systems National Database, a prospective, multiyear, longitudinal database. Patients were survivors of moderate or severe TBI who were discharged from acute hospitalization and admitted to inpatient rehabilitation from January 4, 1989, to June 19, 2019, at 1 of 23 inpatient rehabilitation centers that participated in the Traumatic Brain Injury Model Systems program. Follow-up for the study was through completion of inpatient rehabilitation.

Exposures

Traumatic brain injury.

Main Outcomes and Measures

Outcome measures were Glasgow Coma Scale in the emergency department, Disability Rating Scale, posttraumatic amnesia, and Functional Independence Measure. Patient-related data included demographic characteristics, injury cause, and brain computed tomography findings.

Results

The 17 470 patients with TBI analyzed in this study had a median (interquartile range [IQR]) age at injury of 39 (25-56) years and included 12 854 male individuals (74%). Of these patients, 7547 (57%) experienced initial loss of consciousness, which persisted to rehabilitation in 2058 patients (12%). Those with persisting DOC were younger; had more high-velocity injuries; had intracranial mass effect, intraventricular hemorrhage, and subcortical contusion; and had longer acute care than patients without DOC. Eighty-two percent (n = 1674) of comatose patients recovered consciousness during inpatient rehabilitation. In a multivariable analysis, the factors associated with consciousness recovery were absence of intraventricular hemorrhage (adjusted odds ratio [OR], 0.678; 95% CI, 0.532-0.863; P = .002) and intracranial mass effect (adjusted OR, 0.759; 95% CI, 0.595-0.968; P = .03). Functional improvement (change in total functional independence score from admission to discharge) was +43 for patients with DOC and +37 for those without DOC (P = .002), and 803 of 2013 patients with DOC (40%) became partially or fully independent. Younger age, male sex, and absence of intraventricular hemorrhage, intracranial mass effect, and subcortical contusion were associated with better functional outcome. Findings were consistent across the 3 decades of the database.

Conclusions and Relevance

This study found that DOC occurred initially in most patients with TBI and persisted in some patients after rehabilitation, but most patients with persisting DOC recovered consciousness during rehabilitation. This recovery trajectory may inform acute and rehabilitation treatment decisions and suggests caution is warranted in consideration of withdrawing or withholding care in patients with TBI and DOC.

Somatosensory evoked potential amplitudes correlate with long-term consciousness recovery in patients with unresponsive wakefulness syndrome

OBJECTIVE

To prospectively investigate relationships of cortical somatosensory evoked potential (SEP) amplitudes with consciousness recovery and disability in the year following brain injury in patients with vegetative state/unresponsive wakefulness syndrome (VS/UWS).

METHODS

SEPs of 42 patients with VS/UWS were recorded 51.7 ± 23.3 days post-injury. N20-P25 amplitudes were compared between patients with and without consciousness recovery at 6 months and 1 year post-injury.

RESULTS

SEPs were present in 21 patients and bilaterally absent in 21 patients. N20-P25 amplitudes were significantly higher in patients who recovered consciousness than in those who died or did not recover consciousness at 6 months (median, 4.6 vs. 1.9 μV; P = 0.004) and 1 year (median, 4.6 vs. 2.1 μV; P = 0.02) after injury. The lowest N20-P25 amplitude in a patient who recovered consciousness was 2.15 μV. N20-P25 amplitudes correlated significantly with Coma Recovery Scale-Revised and Disability Rating Scale scores at 6 months and 1 year post-injury (both P < 0.05).

CONCLUSIONS

In patients with VS/UWS, SEP amplitudes are related to consciousness recovery and disability at 6 months and 1 year post-injury.

SIGNIFICANCE

The evaluation of SEP amplitudes can help to refine prognoses for patients with VS/UWS.

Outcome registry of early intensive neurorehabilitation in patients with disorders of consciousness: study protocol of a prospective cohort study

BACKGROUND

Prolonged disorders of consciousness (PDOC) may occur after severe brain injury. Two diagnostic entities are distinguished within PDOC: unresponsive wakefulness syndrome (UWS, previously known as vegetative state) and minimally conscious state (MCS). Patients with PDOC may benefit from early intensive neurorehabilitation (EIN). In the Netherlands, the EIN programme is provided by one designated expert rehabilitation centre and forms the starting point of a dedicated chain of specialised rehabilitation and care for this group. This study project, called DOCTOR: Disorders of Consciousness; Treatment and Outcomes Registry, sets up a registry and systematically investigates multiple short- and long-term outcomes of patients with PDOC who receive EIN.

METHODS

Single-centre prospective cohort study with a 2-year follow-up period. Patients with PDOC due to acute brain injury who receive EIN, aged 16 years and older are included. Measurements will take place at start EIN, in week 5, 10, and at discharge from the EIN programme (duration = max 14 weeks) and at week 28, 40, 52, and 104 after admission to the EIN programme, following patients through the health-care chain. Outcome measures are the changes over time in level of consciousness, using the Coma Recovery Scale-Revised; the frequency and type of medical complications; the mortality rate; level of disability, including the level of motor, cognitive, behavioural and emotional functioning; participation; and quality of life. Secondary outcomes include self-efficacy of caregivers, caregivers' strain and cost-effectiveness of the programme.

DISCUSSION

The DOCTOR study will provide insight in the recovery patterns and predictors of recovery for multiple outcomes in PDOC patients after following EIN. The results of the study will enable us to benchmark and improve EIN and the organisation of the health-care chain, both for patients with PDOC and for their families.

TRIAL REGISTRATION

Netherlands Trial Register, NL 8138 . Retrospectively registered 6 November 2019.

The influence of the CRS-R score on functional outcome in patients with severe brain injury receiving early rehabilitation

Background

The aim of the study was to determine the role of the Coma Recovery Scale-Revised (CRS-R) in the prediction of functional status at the end of neurological early rehabilitative treatment.

Methods

Patients consecutively admitted to intensive or intermediate care units of a neurological rehabilitation center were enrolled in the study. Consciousness and functional status were assessed with the Coma Recovery Scale-Revised (CRS-R) and the Early Rehabilitation Barthel Index (ERBI), respectively. Both assessments were carried out weekly within the first month and at the end of early rehabilitation. Patient and clinical data were entered into a binary logistic regression model to predict functional status at discharge.

Results

327 patients (112 females, 215 males) with a median age of 63 years (IQR = 53–75) and a median disease duration of 18 days (IQR = 12–28) were included. Most patients suffered from stroke (59 %), followed by traumatic brain injury (31 %), and hypoxic ischemic encephalopathy (10 %). Upon admission, 12 % were diagnosed as comatose, 31 % as unresponsive wakefulness syndrome (UWS), 35 % as minimally conscious state (MCS) and 22 % already emerged from MCS (eMCS). Of all patients undergoing complete early rehabilitative treatment (n = 180), 72 % showed improvements in level of consciousness (LOC). In this group, age, initial CRS-R score and gains in CRS-R score after four weeks independently predicted functional outcome at discharge.

Conclusions

The study confirms the relevance of the CRS-R score for functional outcome prediction. High CRS-R scores and young age facilitate functional improvements and increase the probability to continue treatment in subsequent rehabilitation phases. Moreover, results indicate that recovery might occur over a period of time that extends beyond acute care.

When, How, and to What Extent Are Individuals with Unresponsive Wakefulness Syndrome Able to Progress? Neurobehavioral Progress

Accurate estimation of the neurobehavioral progress of patients with unresponsive wakefulness syndrome (UWS) is essential to anticipate their most likely clinical course and guide clinical decision making. Although different studies have described this progress and possible predictors of neurobehavioral improvement in these patients, they have methodological limitations that could restrict the validity and generalization of the results. This study investigates the neurobehavioral progress of 100 patients with UWS consecutively admitted to a neurorehabilitation center using systematic weekly assessments based on standardized measures, and the prognostic factors of changes in their neurobehavioral condition. Our results showed that, during the analyzed period, 34% of the patients were able to progress from UWS to minimally conscious state (MCS), 12% of the total sample (near one third from those who progressed to MCS) were able to emerge from MCS, and 10% of the patients died. Transition to MCS was mostly denoted by visual signs, which appeared either alone or in combination with motor signs, and was predicted by etiology and the score on the Coma Recovery Scale-Revised at admission with an accuracy of 75%. Emergence from MCS was denoted in the same proportion by functional communication and object use. Predictive models of emergence from MCS and mortality were not valid and the identified predictors could not be accounted for.

Decannulation and improvement of responsiveness in patients with disorders of consciousness

Decannulation is a rehabilitation milestone in patients with Disorders of Consciousness (DoC). investigate the relationship between decannulation and improvement of responsiveness (IR) in DoC. 236 tracheostomized patients with severe Acquired Brain Injury and DoC admitted in the Intensive Rehabilitation Unit were retrospectively included. They received personalized interdisciplinary rehabilitation. At discharge, IR was evaluated. The association between IR and demographic/clinical data was investigated using a logistic regression analysis, both in the Unresponsive Wakefulness Syndrome (UWS) and Minimal Consciousness State (MCS) group, divided according to their Coma Recovery Scale-Revised score at admission. In the UWS group (N = 107), only decannulation was associated with IR at discharge (OR: 5.94, CI: 2.08-16.91, p = .001). In the MCS group (N = 129) time post-injury (OR: 0.983, CI: 0.97-0.99, p = .012) and decannulation were associated with IR at discharge (OR: 17.9, CI: 6.39-50.13, p < .001). Decannulation and IR were found to be strongly related, independently from the initial clinical state. While the retrospective nature of the study could not exclude that decannulation may be a consequence of a spontaneous recovery, the obtained results may disclose its potential influence on the clinical history of patients with DoC.

Prognostic models for prolonged disorders of consciousness: an integrative review

Disorders of consciousness (DoC) are acquired conditions of severe altered consciousness. During the past decades, some prognostic models for DoC have been explored on the basis of a variety of predictors, including demographics, neurological examinations, clinical diagnosis, neurophysiology and brain images. In this article, a systematic review of pertinent literature was conducted. We identified and evaluated 21 prognostic models involving a total of 1201 DoC patients. In terms of the reported accuracies of predicting the prognosis of DoC, these 21 models vary widely, ranging from 60 to 90%. Using improvement of consciousness level as favorable outcome criteria, we performed a quantitative meta-analysis, and found that the pooled sensitivity and specificity of the hybrid model that combined more than one technique were both superior to those of any single technique, including EEG and fMRI at the tasks and resting state. These results support the view that any single technique has its own advantages and limitations; and the integrations of multiple techniques, including diverse brain images and different paradigms, have the potential to improve predictive accuracy for DoC. Then, we provide methodological points of view and some prospects about future research. Totally, in comparison to a great many diagnostic methods for the DoC, the research of prognostic models is sparse and preliminary, still largely in its infancy with many challenges and opportunities.

Update on diagnosis in disorders of consciousness

INTRODUCTION

New guidelines regarding the diagnosis of disorders of consciousness (DOC) (such as vegetative state and minimally conscious state) have recently been published by the American Academy of Neurology and the European Academy of Neurology. This follows an impressive number of prospective studies performed on DOC and recent multi-centric studies with larger sample size, which have gathered precious information on the recovery of cohort of patients through years and which now call for a better management of patients with DOC.

AREAS COVERED

This review will discuss recent updates on the clinical entities of DOC, the challenges for an accurate diagnosis and the last developments in diagnostic tools.

EXPERT OPINION

The authors will also discuss the impact of the new guidelines on their way of diagnosing patients and how diagnosis will most likely change in a near future.