Risk factors for 2-year mortality in patients with prolonged disorders of consciousness: An international multicentre study

Vegetative state in two Italian residential facilities: study of prognosis for subjects and related caregivers

BACKGROUND

Vegetative state (VS) is a severe disturbance of consciousness, often caused by cerebral insults, with limited long-term prognosis data.

STUDY OBJECTIVE

to provide a detailed picture of a cohort of patients treated at 2 long-term care facilities in Lombardy starting from 2014.

METHODS

Multicentric observational study. Subjects who met the diagnostic criteria for VS were admitted. A caregiver was identified for each patient and filled two questionnaires to evaluate quality of life (SF-36) and need for psychological support (FSQ-30).

RESULTS

The study included 146 patients (median age: 61.8 years at admission, 60.4 at the event that caused VS). Non-traumatic cerebral hemorrhage was the leading cause of VS (41%), followed by traumatic (19%) and anoxic brain injury (17%), and ischemic stroke (6.8%). The cumulative mortality was 10% at 1 year, 24% at 2 years, 43% at 5 years, 69% at 10 years, and 88% at 15 years after the event that caused VS, with age (HR 1.03) and ischemic stroke (HR 2.86) as risk factors. Among 24 caregivers, 50% had FSQ-30 score suggesting recommended psychological support (R area), 33% were in the SR area (strongly recommended), and 13% in the U area (urgent need). Females had worse FSQ-30 and SF-36 scores than males, particularly in the mental health index (median 60 vs. 74) and mental component scale (45.3 vs. 53.2).

CONCLUSIONS

Hemorrhages were found to be the most common cause of VS, differing from previous studies that identified trauma as the primary cause. Caregivers exhibited significant psychological distress.

Characterization of responders to transcranial direct current stimulation in disorders of consciousness: A retrospective study of 8 clinical trials

The treatment for patients with disorders of consciousness challenges researchers and clinicians. The stimulation of the left dorsolateral prefrontal cortex with transcranial direct current stimulation (tDCS) may enhance behavioral responsiveness of a subset of patients in a minimally conscious state, while having limited effects in unresponsive patients. However, heterogeneity in responses raises questions about the effectiveness of tDCS. Our objective was to explore the characteristics of responders to tDCS based on previously published RCTs and investigate the heterogeneity of treatment effect to better direct future tDCS studies towards patient profiles that appear to be more responsive to the treatment. We explored clinical and demographical differences between responders (i.e., recovery of a new sign of consciousness after active stimulation) and non-responder and the predictors of treatment response with a LASSO logistic regression. We included 131 patients (44 women, 61 traumatic brain injury, 90 minimally conscious, mean age 46.13 years [SD ​= ​16], median time since injury 12.84 months [IQR: 5.25-35.10]) of which 33 responded to tDCS. While 32 ​% of minimally conscious patients responded to tDCS (95%CI 0.24, 0.43), 10 ​% (95%CI 0.04, 0.25) of those unresponsive responded. The regression model, using diagnosis at baseline, Coma Recovery Scale-Revised Index at baseline, age, sex and time since injury correctly discriminated between tDCS responders and non-responders (area under the curve of 0.77). Our findings suggest that patients in minimally conscious state, with a better cognitive profile and longer TSI respond better to tDCS, making them better candidates for the treatment.

The cost of rehabilitation after critical illness: a comparison of hospitalization costs for traumatic brain injury and non-traumatic brain injury patients with disorders of consciousness

Background

This study aims to compare hospitalization costs between traumatic brain injury (TBI) and non-traumatic brain injury (non-TBI) patients with disorders of consciousness (DoC) to explore cost determinants.

Methods

A retrospective analysis was conducted on 210 DoC inpatients admitted to Shenzhen Longcheng Hospital, a tertiary rehabilitation hospital located in China's Pearl River Delta region, between 2015 and 2020. Patients were categorized into TBI (n = 44) and non-TBI (n = 166) groups based on etiology. Demographic, clinical, and hospitalization cost data were collected for each patient. The study compared the cost composition for DoC patients by etiology and used multivariate analysis to identify factors influencing hospitalization costs.

Results

The median length of stay (LOS) and cost for TBI patients were 363.5 days and $57,366.05, respectively, while for non-TBI patients, the medians were 280.5 days and $57,117.64. Across both groups, the highest cost components were rehabilitation, medication, and treatment expenses. Factors associated with higher hospitalization costs included non-TBI etiology, local residents, medical insurance, LOS, self-employed, surgical treatment, and traditional Chinese medicine (TCM) intervention.

Conclusion

Hospitalization cost structures were similar across etiologies, emphasizing value-driven care priorities. Key factors associated with higher hospitalization costs included non-TBI etiology, local residency, medical insurance, LOS, self-employment status, surgery, and TCM. These findings highlight key drivers of healthcare costs in DoC care, emphasizing the need for targeted policy interventions. However, given the limitations of this study, further research with larger, more diverse samples is essential to comprehensively assess the impact of costs on patient outcomes and care quality.

Management of severe acute encephalopathy in the ICU: an expert consensus statement from the french society of intensive care medicine

INTRODUCTION

Acute encephalopathy in the ICU poses significant diagnostic, therapeutic, and prognostic challenges. Standardized expert guidelines on acute encephalopathy are needed to improve diagnostic methods, therapeutic decisions, and prognostication.

METHODS

The experts conducted a review of the literature, analysed it according to the GRADE (Grading of Recommendation, Assessment, Development and Evaluation) methodology and made proposals for guidelines, which were rated by other experts. Only expert opinions with strong agreement were selected.

RESULTS

The synthesis of expert work and the application of the GRADE method resulted in 39 recommendations. Among the 39 formalized recommendations, 1 had a high level of evidence (GRADE 1 +) and 10 had a low level of evidence (GRADE 2 + or 2-). These recommendations describe indication for ICU admission, use of clinical scores and EEG for diagnosis, detection of complications, and prognostication. The remaining 28 recommendations were based on expert consensus. These recomandations describe common indications for blood and CSF studies, neuroimaging, use of neuromonitoring, and provide guidelines for management in the acute phase.

CONCLUSION

This expert consensus statement aims to provide a structured framework to enhance the consistency and quality of care for ICU patients presenting with acute encephalopathy. By integrating high-quality evidence with expert opinion, it offers a pragmatic approach to addressing the complex nature of acute encephalopathy in the ICU, promoting best practices in patient care and facilitating future research in the field.

Predicting Long-Term Outcome of Prolonged Disorder of Consciousness in Children Through Machine Learning Based on Conventional Structural Magnetic Resonance Imaging

BACKGROUND

The prognosis of prolonged disorders of consciousness (pDoC) in children has consistently posed a formidable challenge in clinical decision-making.

OBJECTIVE

This study aimed to develop a machine learning (ML) model based on conventional structural magnetic resonance imaging (csMRI) to predict outcomes in children with pDoC.

METHODS

A total of 196 children with pDoC were included in this study. Based on the consciousness states 1 year after brain injury, the children were categorized into either the favorable prognosis group or the poor prognosis group. They were then randomly assigned to the training set (n = 138) or the test set (n = 58). Semi-quantitative visual assessments of brain csMRI were conducted and Least Absolute Shrinkage and Selection Operator regression was used to identify significant features predicting outcomes. Based on the selected features, support vector machine (SVM), random forests (RF), and logistic regression (LR) were used to develop csMRI, clinical, and csMRI-clinical-merge models, respectively. Finally, the performances of all models were evaluated.

RESULTS

Seven csMRI features and 4 clinical features were identified as important predictors of consciousness recovery. All models achieved satisfactory prognostic performances (all areas under the curve [AUCs] >0.70). Notably, the csMRI model developed using the SVM exhibited the best performance, with an AUC, accuracy, sensitivity, and specificity of 0.851, 0.845, 0.844, and 0.846, respectively.

CONCLUSIONS

A csMRI-based prediction model for the prognosis of children with pDoC was developed, showing potential to predict recovery of consciousness 1 year after brain injury and is worth popularizing in clinical practice.

Advances in neuroimaging in disorders of consciousness

Disorders of consciousness (DoC) are a heterogeneous spectrum of clinical conditions, including coma, unresponsive wakefulness syndrome, and minimally conscious state. DoC are clinically defined on the basis of behavioral cues expressed by the patients, on the assumption that such behavioral responses of the patient are representative of the patient's degree of consciousness impairment. However, many studies have highlighted the issues arising from formulating a DoC diagnosis merely on behavioral assessment. Overcoming the limitations of behavioral assessment, neuroimaging provides a direct window on the cerebral activity of the patient, bypassing the motor, perceptual, or cognitive deficits that might hamper the patient's ability to produce an appropriate behavioral response. This chapter provides an overview of available molecular, functional, and structural neuroimaging evidence in patients with DoC. This chapter introduces the neuroimaging tools available in the clinical settings of nuclear medicine and neuroradiology and presents the evidence on the role of neuroimaging tools to improve the clinical management of DoC patients, from the standpoint of differential diagnosis and prognosis. Last, we outline the open questions in the field, and point at actions that are urgently needed to fully exploit neuroimaging tools to advance scientific understanding and clinical management of DoC.

Neurologic prognostication in coma and disorders of consciousness

Coma and disorders of consciousness (DoC) are clinical syndromes primarily resulting from severe acute brain injury, with uncertain recovery trajectories that often necessitate prolonged supportive care. This imposes significant socioeconomic burdens on patients, caregivers, and society. Predicting recovery in comatose patients is a critical aspect of neurocritical care, and while current prognostication heavily relies on clinical assessments, such as pupillary responses and motor movements, which are far from precise, contemporary prognostication has integrated more advanced technologies like neuroimaging and electroencephalogram (EEG). Nonetheless, neurologic prognostication remains fraught with uncertainty and significant inaccuracies and is impacted by several forms of prognostication biases, including self-fulfilling prophecy bias, affective forecasting, and clinician treatment biases, among others. However, neurologic prognostication in patients with disorders of consciousness impacts life-altering decisions including continuation of treatment interventions vs withdrawal of life-sustaining therapies (WLST), which have a direct influence on survival and recovery after severe acute brain injury. In recent years, advancements in neuro-monitoring technologies, artificial intelligence (AI), and machine learning (ML) have transformed the field of prognostication. These technologies have the potential to process vast amounts of clinical data and identify reliable prognostic markers, enhancing prediction accuracy in conditions such as cardiac arrest, intracerebral hemorrhage, and traumatic brain injury (TBI). For example, AI/ML modeling has led to the identification of new states of consciousness such as covert consciousness and cognitive motor dissociation, which may have important prognostic significance after severe brain injury. This chapter reviews the evolving landscape of neurologic prognostication in coma and DoC, highlights current pitfalls and biases, and summarizes the integration of clinical examination, neuroimaging, biomarkers, and neurophysiologic tools for prognostication in specific disease states. We will further discuss the future of neurologic prognostication, focusing on the integration of AI and ML techniques to deliver more individualized and accurate prognostication, ultimately improving patient outcomes and decision-making process in neurocritical care.

Traumatic brain injury and disorders of consciousness

Trauma is one of the most common causes of disorders of consciousness (DOC) worldwide. Traumatic brain injury (TBI) leads to heterogeneous, multifocal injury via focal brain damage and diffuse axonal injury, causing an acquired network disorder. Recovery occurs through reemergence of dynamic cortical and subcortical networks. Accurate diagnostic evaluation is essential toward promoting recovery and may be more challenging in traumatic than non-traumatic brain injuries. Standardized neurobehavioral assessment is the cornerstone for assessments in the acute, prolonged, and chronic phases of traumatic DOC, while structural and functional neuroimaging, tractography, nuclear medicine studies, and electrophysiologic techniques assist with differentiation of DOC states and prognostication. Prognosis for recovery is better for patients with TBI than those with non-traumatic brain injuries, and the timeline for recovery is longer. The majority of patients experience improvement in their DOC within the first year post-injury, but recovery can continue for five and even ten years after TBI. Pharmacologic therapy and device-related neuromodulation represent important areas for future research.

Mortality of chronic disorders of consciousness in adults and adolescents - a retrospective community based study from Salzburg, Austria

Introduction

Epidemiological data on disorders of consciousness (DoC) is rare and very heterogeneous due to difficulties in case ascertainment and differences in health care pathways between countries. This study reports data on mortality and survival time for DoC patients in Salzburg, Austria.

Methods

All patients with DoC were registered in the health care region of Salzburg North, Austria between 2007 and 2022 and their death data retrieved from the Statistik Austria. The 1- and 5-year mortality was calculated, also in relation to several explanatory variables (age, sex, etiology, diagnosis, CRS-R score, improvement). Furthermore, the incidence, survival functions using the Kaplan-Meier estimator and a Cox-Regression were calculated.

Results

The mean annual incidence is 2.2 DoC/100.000 inhabitants in the Salzburg North region. The crude 1- and 5-year mortality rates were 25.9 and 55.1%, respectively, and the median survival of DoC patients based on the Kaplan-Meier estimator was 6.3 years. Moreover, the mortality was lower in women and in younger patients, those of traumatic etiology, and those with higher CRS-R scores, better diagnosis or an improvement of diagnosis until discharge from hospital.

Conclusion

This article gives a rare insight into epidemiological data on DoC and shows which factors influence the mortality of these patients. Moreover, it is the first community based study on mortality of DoC in Salzburg, Austria.

Exploring end-of-life decision-making in China for disorders of consciousness

OBJECTIVES

We aim to investigate the ethical attitudes of the Chinese population toward withdrawal of life-sustaining treatment (WLST) in disorders of consciousness (DoC) patients.

METHODS

A self-administered questionnaire concerning WLST was distributed to Chinese medical professionals and non-medical participants between February and July 2022. Statistical analysis included chi-square tests and logistic regressions.

RESULTS

A total of 1223 Chinese participants responded to the questionnaire (39% of whom were medical professionals). Less than one third of participants reported positive attitudes towards withdrawing artificial nutrition and hydration (ANH), antibiotics, and do-not-resuscitation (DNR) orders in patients with unresponsive wakefulness syndrome (UWS) (30%, 24%, 24%) and minimally conscious state (MCS) (23%, 19%, 15%). More respondents agreed with WLST in UWS compared to MCS (p < 0.05). Positive attitudes toward DNR orders were associated with participants' older age, religion, monthly income > 5000 RMB and medical profession (p < 0.05). Most participants deemed patient's will (78%), families' wishes (67%), and financial burden (63%) to be crucial factors when considering WLST.

CONCLUSIONS

Chinese respondents exhibit a relatively low propensity to accept WLST in DoC. Ethical attitudes toward WLST resulted to be affected by individual characteristics of responders. These results call for developing better regulations for identifying qualified surrogate decision-makers and reducing legal ambiguities.

A novel nomogram for predicting the prognosis of critically ill patients with EEG patterns exhibiting stimulus-induced rhythmic, periodic, or ictal discharges

OBJECTIVES

To explore the factors associated with poor prognosis in critically ill patients with Electroencephalogram (EEG) patterns exhibiting stimulus-induced rhythmic, periodic, or ictal discharges (SIRPIDs), and to construct a prognostic prediction model.

METHODS

This study included a total of 53 critically ill patients with EEG patterns exhibiting SIRPIDs who were admitted to the First Affiliated Hospital of Chongqing Medical University from May 2023 to March 2024. Patients were divided into two groups based on their Modified Rankin Scale (mRS) scores at discharge: good prognosis group (0-3 points) and poor prognosis group (4-6 points). Retrospective analyses were performed on the clinical and EEG parameters of patients in both groups. Logistic regression analysis was applied to identify the risk factors related to poor prognosis in critically ill patients with EEG patterns exhibiting SIRPIDs; a risk prediction model for poor prognosis was constructed, along with an individualized predictive nomogram model, and the predictive performance and consistency of the model were evaluated.

RESULTS

Multivariate logistic regression analysis revealed that APACHE II score (OR=1.217, 95 %CI=1.030∼1.438), slow frequency bands or no obvious brain electrical activity (OR=8.720, 95 %CI=1.220∼62.313), and no sleep waveforms (OR=9.813, 95 %CI=1.371∼70.223) were independent risk factors for poor prognosis in patients. A regression model established based on multivariate logistic regression analysis had an area under the curve of 0.902. The model's accuracy was 90.60 %, with a sensitivity of 92.86 % and a specificity of 89.70 %. The nomogram model, after internal validation, showed a concordance index of 0.904.

CONCLUSIONS

A high APACHE II score, EEG patterns with slow frequency bands or no obvious brain electrical activity, and no sleep waveforms were independent risk factors for poor prognosis in patients with SIRPIDs. The nomogram model constructed based on these factors had a favorably high level of accuracy in predicting the risk of poor prognosis and held certain reference and application value for clinical neurofunctional assessment and prognostic determination.

A nomogram based on quantitative EEG to predict the prognosis of nontraumatic coma patients in the neuro-intensive care unit

OBJECTIVE

We aimed to establish a quantitative electroencephalography-based prognostic prediction model specifically tailored for nontraumatic coma patients to guide clinical work.

METHODS

This retrospective study included 126 patients with nontraumatic coma admitted to the First Affiliated Hospital of Chongqing Medical University from December 2020 to December 2022. Six in-hospital deaths were excluded. The Glasgow Outcome Scale assessed the prognosis at 3 months after discharge. The least absolute shrinkage and selection operator regression analysis and stepwise regression method were applied to select the most relevant predictors. We developed a predictive model using binary logistic regression and then presented it as a nomogram. We assessed the predictive effectiveness and clinical utility of the model.

RESULTS

After excluding six deaths that occurred within the hospital, a total of 120 patients were included in this study. Three predictor variables were identified, including APACHE II score [39.129 (1.4244-1074.9000)], sleep cycle [OR: 0.006 (0.0002-0.1808)], and RAV [0.068 (0.0049-0.9500)]. The prognostic prediction model showed exceptional discriminative ability, with an AUC of 0.939 (95 % CI: 0.899-0.979).

CONCLUSION

A lack of sleep cycles, smaller relative alpha variants, and higher APACHE II scores were associated with a poor prognosis of nontraumatic coma patients in the neurointensive care unit at 3 months after discharge.

CLINICAL IMPLICATION

This study presents a novel methodology for the prognostic assessment of nontraumatic coma patients and is anticipated to play a significant role in clinical practice.

Prognosis of patients with prolonged disorders of consciousness after brain injury: a longitudinal cohort study

Background

The findings regarding the prognosis of prolonged disorders of consciousness (PDOC) vary widely among different studies. This study aims to investigate the mortality, consciousness recovery and disabilities of patients with PDOC after brain injury.

Methods

A total of 204 patients with PDOC were included in a longitudinal cohort study, including 129 males and 75 females. There were 112 cases of traumatic brain injury (TBI), 62 cases of cerebral hemorrhage (CH), 13 cases of cerebral infarction (CI) and 17 cases of ischemic hypoxic encephalopathy (IHE). The status of consciousness at 1, 2, 3, 6, 12, 18, 24, 36, 48 months of the disease course was assessed or followed up using the Revised Coma Recovery Scale (CRS-R). If the patients were conscious, the disability Rating Scale (DRS) was also performed. The prognosis of different PDOC including coma, vegetative state (VS) and minimal conscious state (MCS) was analyzed. The survival patients were screened for variables and included in multivariate binary Logistic regression to screen the factors affecting the recovery of consciousness.

Results

The mortality rates at 12, 24, 36, and 48 months were 10.7, 23.4, 38.9, and 68.4%, respectively. The median time of death was 18 months (8.75, 29). The probability of MCS regaining consciousness was higher than VS (p < 0.05), with the degree of disability left lower than VS (p < 0.05). There was no significant difference between MCS- and MCS+ groups in terms of the probability of regaining consciousness, the extent of residual disability, and mortality rates (p > 0.05). The mortality rate of coma was higher than that of other PDOC (p < 0.05). The mortality rate of MCS was lower than that of VS, but the difference was not statistically significant (p > 0.05). The probability of consciousness recovery after TBI was the highest and the mortality rate was the lowest. The possibility of consciousness recovery in IHE was the least, and the mortality rate of CI was the highest. The cause of brain injury and initial CRS-R score were the factors affecting the consciousness recovery of patients (p < 0.05).

Conclusion

The prognosis of MCS is more favorable than VS, with comparable outcomes between MCS- and MCS+, while comatose patients was the poorest. TBI has the best prognosis and IHE has the worst prognosis.

Mortality Predictors for Adult Patients with Mild-to-Moderate Traumatic Brain Injury: A Literature Review

Traumatic brain injuries (TBIs) represent a significant public health concern, with mild-to-moderate cases comprising a substantial portion of incidents. Understanding the predictors of mortality among adult patients with mild-to-moderate TBIs is crucial for optimizing clinical management and improving outcomes. This literature review examines the existing research to identify and analyze the mortality predictors in this patient population. Through a comprehensive review of peer-reviewed articles and clinical studies, key prognostic factors, such as age, Glasgow Coma Scale (GCS) score, the presence of intracranial hemorrhage, pupillary reactivity, and coexisting medical conditions, are explored. Additionally, this review investigates the role of advanced imaging modalities, biomarkers, and scoring systems in predicting mortality following a mild-to-moderate TBI. By synthesizing the findings from diverse studies, this review aims to provide clinicians and researchers with valuable insights into the factors influencing mortality outcomes in adult patients with a mild-to-moderate TBI, thus facilitating more informed decision making and targeted interventions in clinical practice.

Medical comorbidities in patients with prolonged disorder of consciousness: A narrative review

BACKGROUND

A few studies specifically addressed medical comorbidities (MCs) in patients with severe acquired traumatic or non-traumatic brain injury and prolonged disorders of consciousness (pDoC; i.e., patients in vegetative state/unresponsive wakefulness syndrome, VS/UWS, or in minimally conscious state, MCS).

OBJECTIVE

To provide an overview on incidence of MCs in patients with pDoC.

METHODS

Narrative review on most impacting MCs in patients with pDoC, both those directly related to brain damage (epilepsy, neurosurgical complications, spasticity, paroxysmal sympathetic hyperactivity, PSH), and those related to severe disability and prolonged immobility (respiratory comorbidities, endocrine disorders, metabolic abnormalities, heterotopic ossifications).

RESULTS

Patients with pDoC are at high risk to develop at least one MC. Moderate or severe respiratory and musculoskeletal comorbidities are the most common MCs. Epilepsy and PSH seem to be more frequent in patients in VS/UWS compared to patients in MCS, likely because of higher severity in the brain damage in VS. Endocrine metabolic, PSH and respiratory complications are less frequent in traumatic etiology, whereas neurogenic heterotopic ossifications are more frequent in traumatic etiology. Spasticity did not significantly differ between VS/UWS and MCS and in the three etiologies. MCs are associated with higher mortality rates, worse clinical improvement and can impact accuracy in the clinical diagnosis.

CONCLUSIONS

The frequent occurrence of several MCs requires a specialized rehabilitative setting with high level of multidisciplinary medical expertise to prevent, appropriately recognize and treat them. Comprehensive rehabilitation could avoid possible progression to more serious complications that can negatively impact clinical outcomes.

Medicolegal issues and disorders of consciousness

BACKGROUND

The tasks and responsibilities that come with clinician involvement in medicolegal proceedings can be daunting and particularly so in challenging areas such as provision of medicolegal opinions in cases involving disorders of consciousness (DoC).

OBJECTIVE

The aim of this narrative review was to provide education and advice to healthcare practitioners who by choice or circumstance are asked and/or required to provide medicolegal opinions in cases involving patients with DoC.

METHODS

A literature search was conducted using PubMed Central and MedlinePlus for articles dealing with clinician involvement in medicolegal cases involving persons with DoC. The information provided also integrates the authors' nearly 40 years of clinical experience, brain injury medicine practice and "trials and tribulations" associated with medicolegal involvement in such cases.

RESULTS

The literature was found to be replete with articles on brain death and withdrawal/withholding of care (which are not the focus of this review). The extant medical literature in brain injury medicine on the other hand is currently lacking in practical information for clinicians working "in the trenches" regarding the challenges and caveats of medicolegal involvement in such cases.

CONCLUSION

This review provides the reader with a big picture overview of the most pertinent medicolegal topics inherent in clinical work with patients with DOC including pertinent nomenclature, caveats regarding forensic consultation including independent medical examinations, testimony tips, discussion of life expectancy/median survival concepts, prognostication in a medicolegal context, documentation and record keeping as well as some of the specific challenges pertinent to these types of brain injury cases that are not per se relevant in less severe injuries.

Long-term clinical evolution of patients with prolonged disorders of consciousness due to severe anoxic brain injury: A meta-analytic study

BACKGROUND AND PURPOSE

The prognosis of prolonged (28 days to 3 months post-onset) disorders of consciousness (pDoC) due to anoxic brain injury is uncertain. The present study aimed to evaluate the long-term outcome of post-anoxic pDoC and identify the possible predictive value of demographic and clinical information.

METHOD

This is a systematic review and meta-analysis. The rates of mortality, any improvement in clinical diagnosis, and recovery of full consciousness at least 6 months after severe anoxic brain injury were evaluated. A cross-sectional approach searched for differences in baseline demographic and clinical characteristics between survivors and non-survivors, patients improved versus not improved, and patients who recovered full consciousness versus not recovered.

RESULTS

Twenty-seven studies were identified. The pooled rates of mortality, any clinical improvement and recovery of full consciousness were 26%, 26% and 17%, respectively. Younger age, baseline diagnosis of minimally conscious state versus vegetative state/unresponsive wakefulness syndrome, higher Coma Recovery Scale Revised total score, and earlier admission to intensive rehabilitation units were associated with a significantly higher likelihood of survival and clinical improvement. These same variables, except time of admission to rehabilitation, were also associated with recovery of full consciousness.

CONCLUSIONS

Patients with anoxic pDoC might improve over time up to full recovery of consciousness and some clinical characteristics can help predict clinical improvement. These new insights could support clinicians and caregivers in the decision-making on patient management.

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.

Sham-controlled randomized multicentre trial of transcranial direct current stimulation for prolonged disorders of consciousness

BACKGROUND AND PURPOSE

Transcranial direct current stimulation (tDCS) has been shown to improve signs of consciousness in a subset of patients with disorders of consciousness (DoC). However, no multicentre study confirmed its efficacy when applied during rehabilitation. In this randomized controlled double-blind study, the effects of tDCS whilst patients were in rehabilitation were tested at the group level and according to their diagnosis and aetiology to better target DoC patients who might repond to tDCS.

METHODS

Patients received 2 mA tDCS or sham applied over the left prefrontal cortex for 4 weeks. Behavioural assessments were performed weekly and up to 3 months' follow-up. Analyses were conducted at the group and subgroup levels based on the diagnosis (minimally conscious state [MCS] and unresponsive wakefulness syndrome) and the aetiology (traumatic or non-traumatic). Interim analyses were planned to continue or stop the trial.

RESULTS

The trial was stopped for futility when 62 patients from 10 centres were enrolled (44 ± 14 years, 37 ± 24.5 weeks post-injury, 18 women, 32 MCS, 39 non-traumatic). Whilst, at the group level, no treatment effect was found, the subgroup analyses at 3 months' follow-up revealed a significant improvement for patients in MCS and with traumatic aetiology.

CONCLUSIONS

Transcranial direct current stimulation during rehabilitation does not seem to enhance patients' recovery. However, diagnosis and aetiology appear to be important factors leading to a response to the treatment. These findings bring novel insights into possible cortical plasticity changes in DoC patients given these differential results according to the subgroups of patients.

The Neurological Rehabilitation of Adults With Coma and Disorders of Consciousness

BACKGROUND

Severe quantitative disorders of consciousness (DoC) due to acute brain injury affect up to 47% of patients upon admission to intensive care and early rehabilitation units. Nevertheless, the rehabilitation of this vulnerable group of patients has not yet been addressed in any German-language guidelines and has only been studied in a small number of randomized clinical trials.

METHODS

In an S3 clinical practice guideline project, a systematic literature search was carried out for interventions that could improve consciousness in patients with coma, unresponsive wakefulness syndrome, or minimally conscious state after acute brain injury, and an evidence-based evaluation of these interventions was performed. Recommendations concerning diagnostic methods and medical ethics were issued by consensus.

RESULTS

Misdiagnoses are common in patients with DoC, with minimal consciousness often going unrecognized. Patients with DoC should, therefore, be repeatedly assessed with standardized instruments, particularly the Coma Recovery Scale-Revised. The literature search yielded 54 clinical trials, mostly of low quality; there were two randomized controlled clinical trials providing level 1 evidence. The best available evidence for the improvement of impaired consciousness is for the administration of amantadine (4 studies) and for anodal transcranial direct-current stimulation of the left dorsolateral prefrontal cortex in patients in the minimal conscious state (8 studies, 2 systematic reviews). Further important components of rehabilitation include positioning methods and sensory stimulation techniques such as music therapy.

CONCLUSION

For the first time, evidence-based German-language clinical practice guidelines have now become available for the neurological rehabilitation of patients with DoC.

Towards modern post-coma care based on neuroscientific evidence

Background

Understanding the mechanisms underlying human consciousness is pivotal to improve the prognostication and treatment of severely brain-injured patients. Consciousness remains an elusive concept and the identification of its neural correlates is an active subject of research, however recent neuroscientific advances have allowed scientists to better characterize disorders of consciousness. These breakthroughs question the historical nomenclature and our current management of post-comatose patients.

Method

This review examines the contribution of consciousness neurosciences to the current clinical management of severe brain injury. It investigates the major impact of consciousness disorders on healthcare systems, the scientific frameworks employed to identify their neural correlates and how evidence-based data from neuroimaging research have reshaped the landscape of post-coma care in recent years.

Results

Our increased ability to detect behavioral and neurophysiological signatures of consciousness has led to significant changes in taxonomy and clinical practice. We advocate for a multimodal framework for the management of severely brain-injured patients based on precision medicine and evidence-based decisions, integrating epidemiology, health economics and neuroethics.

Conclusions

Major progress in brain imaging and clinical assessment have opened the door to a new era of post-coma care based on standardized neuroscientific evidence. We highlight its implications in clinical applications and call for improved collaborations between researchers and clinicians to better translate findings to the bedside.

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.

Assessment and management of pain/nociception in patients with disorders of consciousness or locked-in syndrome: A narrative review

The assessment and management of pain and nociception is very challenging in patients unable to communicate functionally such as patients with disorders of consciousness (DoC) or in locked-in syndrome (LIS). In a clinical setting, the detection of signs of pain and nociception by the medical staff is therefore essential for the wellbeing and management of these patients. However, there is still a lot unknown and a lack of clear guidelines regarding the assessment, management and treatment of pain and nociception in these populations. The purpose of this narrative review is to examine the current knowledge regarding this issue by covering different topics such as: the neurophysiology of pain and nociception (in healthy subjects and patients), the source and impact of nociception and pain in DoC and LIS and, finally, the assessment and treatment of pain and nociception in these populations. In this review we will also give possible research directions that could help to improve the management of this specific population of severely brain damaged patients.

Predicting Long-Term Recovery of Consciousness in Prolonged Disorders of Consciousness Based on Coma Recovery Scale-Revised Subscores: Validation of a Machine Learning-Based Prognostic Index

Prognosis of prolonged Disorders of Consciousness (pDoC) is influenced by patients' clinical diagnosis and Coma Recovery Scale-Revised (CRS-R) total score. We compared the prognostic accuracy of a novel Consciousness Domain Index (CDI) with that of clinical diagnosis and CRS-R total score, for recovery of full consciousness at 6-, 12-, and 24-months post-injury. The CDI was obtained by a combination of the six CRS-R subscales via an unsupervised machine learning technique. We retrospectively analyzed data on 143 patients with pDoC (75 in Minimally Conscious State; 102 males; median age = 53 years; IQR = 35; time post-injury = 1-3 months) due to different etiologies enrolled in an International Brain Injury Association Disorders of Consciousness Special Interest Group (IBIA DoC-SIG) multicenter longitudinal study. Univariate and multivariate analyses were utilized to assess the association between outcomes and the CDI, compared to clinical diagnosis and CRS-R. The CDI, the clinical diagnosis, and the CRS-R total score were significantly associated with a good outcome at 6, 12 and 24 months. The CDI showed the highest univariate prediction accuracy and sensitivity, and regression models including the CDI provided the highest values of explained variance. A combined scoring system of the CRS-R subscales by unsupervised machine learning may improve clinical ability to predict recovery of consciousness in patients with pDoC.

An innovative approach for the evaluation of prolonged disorders of consciousness using NF-L and GFAP biomarkers: a pivotal study

Behavioral assessments during the clinical evaluation in prolonged disorders of consciousness patients could be not sufficient for a correct diagnosis and prognostication. To this aim, we used an innovative approach, involving the ultra-sensitive determination of biological markers, correlating them with imaging parameters to investigate the prolonged disorders of consciousness (pDoC).We assessed the serum concentration of neurofilament light chain(NF-L) and glial fibrillary acidic protein (GFAP) in pDoC (n = 16), and healthy controls (HC, n = 6) as well as several clinical imaging parameters such as Fractional Anisotropy (FA), Whole Brain SUV, and White Matter Hyperintensities volumes (WMH) using PET-MRI acquisition. As for differential diagnosis task, only the imaging WMH volume was able to discriminate between vegetative state/unresponsive wakefulness syndrome (VS/UWS), and minimally conscious state (MCS) patients (p-value < 0.01), while all selected markers (both imaging and in vitro) were able to differentiate between pDoC patients and HC. At subject level, serum NF-L concentrations significantly differ according to clinical progression and consciousness recovery (p-value < 0.01), highlighting a potential play for the longitudinal management of these patients.

Studying death and near-death experiences requires neuroscientific expertise

Parnia et al. recently published suggestions for the study of death and experiences recalled in a near-death context. We have serious reservations about the authors' statements. In this commentary, we discuss the omissions and knowledge gaps inherent to the authors' paper, which among others include incorrect neurological claims about brain death and misunderstandings regarding the terminology of consciousness. Although we believe that (near-)death research deserves a framework guideline, the paper by Parnia and colleagues is misleading and, contrary to the authors' intention, hinders the scientific understanding of near-death experiences and the neural mechanisms occurring in the dying brain.

Predicting outcome of patients with prolonged disorders of consciousness using machine learning models based on medical complexity

Patients with severe acquired brain injury and prolonged disorders of consciousness (pDoC) are characterized by high clinical complexity and high risk to develop medical complications. The present multi-center longitudinal study aimed at investigating the impact of medical complications on the prediction of clinical outcome by means of machine learning models. Patients with pDoC were consecutively enrolled at admission in 23 intensive neurorehabilitation units (IRU) and followed-up at 6 months from onset via the Glasgow Outcome Scale-Extended (GOSE). Demographic and clinical data at study entry and medical complications developed within 3 months from admission were collected. Machine learning models were developed, targeting neurological outcomes at 6 months from brain injury using data collected at admission. Then, after concatenating predictions of such models to the medical complications collected within 3 months, a cascade model was developed. One hundred seventy six patients with pDoC (M: 123, median age 60.2 years) were included in the analysis. At admission, the best performing solution (k-Nearest Neighbors regression, KNN) resulted in a median validation error of 0.59 points [IQR 0.14] and a classification accuracy of dichotomized GOS-E of 88.6%. Coherently, at 3 months, the best model resulted in a median validation error of 0.49 points [IQR 0.11] and a classification accuracy of 92.6%. Interpreting the admission KNN showed how the negative effect of older age is strengthened when patients' communication levels are high and ameliorated when no communication is present. The model trained at 3 months showed appropriate adaptation of the admission prediction according to the severity of the developed medical complexity in the first 3 months. In this work, we developed and cross-validated an interpretable decision support tool capable of distinguishing patients which will reach sufficient independence levels at 6 months (GOS-E > 4). Furthermore, we provide an updated prediction at 3 months, keeping in consideration the rehabilitative path and the risen medical complexity.