Vegetative State Research Articles

"I don't want hear about that": Treatment wishes/preferences of elderly patients regarding possible treatment goals in acute life-threatening illness or acute organ failure

Abstract Background In many people with acutely life-threatening illnesses, the will to undergo treatment is not known with sufficient precision. It is often unclear whether the person concerned would have consented to life-sustaining measures or would have preferred to die with palliative care. This applies in particular to the vulnerable group of elderly, chronically ill/fragile people. It is not uncommon for people to reject the risks/burdens of medical treatment, even if there is a chance of success. On the other hand, there are many people who fight to continue living, even if this can be associated with great burdens and the outcome is rather uncertain. Aim of the study is to determine the characteristics of cardiologic patients who refuse to discuss treatment wishes/preferences in case of acute emergencies, temporary (e.g. in the context of intensive care unit (ICU) treatment), or permanent (vegetative state) inability to consent. Methods Prospective, questionnaire-based cross-sectional study including Hospital Anxiety and Depression Scale (HADS) and assessment of quality of life using the 12-item Short Form Health Survey (SF-12) on normal cardiology wards. Results 455 cardiologic patients were interviewed (mean age 71.2 ± 12.9 years, 39.6% female), 76.1% had moderate-severe concomitant diseases according to the Carlsson Comorbidity Index (4.3 ± 2.4 points) (see Table I). 23.5 % of respondents did not wish to address the above-mentioned question, they were significantly older (74.6 ± 13.1 vs. 69.9 ± 12.8 years; p=0.0009), had more comorbidities, were less likely to have a high level of education (university degree: 13.0 % vs. 30.6 %; p=0.0021) and were less likely to be employed (11.7 % vs. 25.8 %; p=0.0026). Furthermore, these patients live alone more frequently (45.8% vs. 30.6%; p=0.0048) and are less likely to live in a partnership (52.0% vs. 68.0%; p=0.0043). Refusal of an advance directive was also more common (22.5% vs. 10.7%; p=0.05), quality of life was impaired (42.4 ± 9.8 vs. 45.9 ± 10.5 points; p=0.0022), and the HADS-score levels were higher (15.6 ± 7.3 vs. 12.7 ± 7.8 points; p=0.0007). Conclusions A relevant proportion of our patients refuse to discuss treatment wishes/preferences in case of acute emergencies, temporary or permanent inability to consent. Paradoxically, these were patients for whom it would make particular sense (older, sicker, and more socially isolated patients). Those patients are also prone to anxiety and depression, and have a reduced quality of life.

Predictive power of the eTBI score for 30 day outcome in elderly patients with traumatic brain Injury

Traumatic brain injury (TBI) is a common problem in elderly individuals, with significant morbidity and mortality. The elderly Traumatic Brain Injury (eTBI) score, a novel tool for predicting outcomes in elderly patients with TBI, has shown promising results in previous studies. This study aimed to validate the eTBI score in a larger cohort of elderly patients with TBI in the Middle East. We conducted a retrospective study on 337 TBI patients with a mean age of 73.04 ± 8.73, admitted to a tertiary care hospital between March 2021 and November 2022. Within 30 days of admission, the patients’ conditions, including mortality and entering a vegetative state, were evaluated. The study population was split into three groups based on eTBI score: low, medium, and high risk; then patients were divided into two subgroups based on their Glasgow Outcome Scale (GOS ≤ 2, GOS > 2) in 30 days from hospital admission. Poor outcomes (mortality and entering a vegetative state) occurred in 24.3% of the study population. Within 30 days of hospital admission, 88% of low-risk patients experienced some degree of improvement, while 100% of high-risk patients died or fell into a vegetative state. In the medium-risk group, there was a significant correlation between unresponsive pupil (P = 0.006), initial GCS score (P = 0.003), need for a ventilator device (P = 0.015), need for surgical treatment (P = 0.031) and poor outcomes. Despite having a low sensitivity (21% vs. 57%), the eTBI score performed well in terms of accuracy (81% vs. 88%), specificity (100 vs. 98%), positive predictive value (100% vs. 90%), and negative predictive value (80% vs. 88%) for both eTBI ≤ 0 and eTBI ≤ 3 thresholds. The eTBI score is a reliable tool for predicting outcomes in elderly patients with TBI. This scoring system has a positive predictive value of 100% in the eTBI ≤ 0 group, which shows that 100% of the patients who are predicted by the eTBI score to have a poor outcome will indeed have a poor outcome. Patients in the high-risk group should be closely monitored and provided with intensive care, while those in the low-risk group can be reassured about their prognosis. The eTBI score can also be used in conjunction with other clinical factors to inform treatment decisions for patients in the medium-risk group.

NSE and S100β as serum alarmins in predicting neurological outcomes after cardiac arrest

Cardiac arrest (CA) is a serious health concern that often results in mortality or severe neurological dysfunction in the case of survival. Our aim was to explore the neurological prognostic factors in patients with CA. This retrospective observational study included adult patients with CA. We investigated serum neuron-specific enolase (NSE), S100 calcium-binding protein β (S100β), and indices and parameters at 1, 3, 5, 7 and intensive care unit (ICU) discharge days after CA. The primary study endpoint was the Cerebral Performance Category (CPC) scale score at ICU discharge, which was dichotomized as good neurological outcome (CPC 1–2: full recovery or moderate disability) and poor neurological outcome (CPC 3–5: severe disability, vegetative state, or death). Of the 191 adult patients with CA, 42 (22%) had good neurological outcomes, and 149 (78%) had poor neurological outcomes. NSE at 1,3,5,7 and ICU discharge days showed excellent predictive accuracy for neurological outcomes (area under the curve [AUC]: 0.666, 0.716, 0.870, 0.739, and 0.901, respectively). However, S100β exhibited general predictive power (AUC: 0.666, 0.573, 0.607, 0.594, 0.727). Finally, the early warning model, which combined day 1 NSE, day 1 S100β, cardiac arrest time, SOFA scores, APACHE II scores, and age, was used to screen CA patients with poor neurological prognosis at early stages and had an AUC of 0.792. Serum concentrations of NSE and S100β were significantly elevated in CA patients and could be prognostic biomarkers to predict neurological outcomes. Day 1 NSE and S100β combined with multiple indicators could be a decent early warning model for poor neurological prognosis in patients with CA.

An AI-Driven Model of Consciousness, Its Disorders, and Their Treatment.

Understanding the neural signatures of consciousness and the mechanisms underlying its disorders, such as coma and unresponsive wakefulness syndrome, remains a critical challenge in neuroscience. In this study, we present a novel computational approach for the in silico discovery of neural correlates of consciousness, the mechanisms driving its disorders, and potential treatment strategies. Inspired by generative adversarial networks, which have driven recent advancements in generative artificial intelligence (AI), we trained deep neural networks to detect consciousness across multiple brain areas and species, including humans. These networks were then integrated with a genetic algorithm to optimize a brain-wide mean-field model of neural electrodynamics. The result is a realistic simulation of conscious brain states and disorders of consciousness (DOC), which not only recapitulates known mechanisms of unconsciousness but also predicts novel causes expected to lead to these conditions. Beyond simulating DOC, our model provides a platform for exploring therapeutic interventions, specifically deep brain stimulation (DBS), which has shown promise in improving levels of awareness in DOC in over five decades of study. We systematically applied simulated DBS to various brain regions at a wide range of frequencies to identify an optimal paradigm for reigniting consciousness in this cohort. Our findings suggest that in addition to previously studied thalamic and pallidal stimulation, high-frequency stimulation of the subthalamic nucleus, a relatively underexplored target in DOC, may hold significant promise for restoring consciousness in this set of disorders.

Survival and clinical characteristics of patients with disorders of consciousness in a developing country between 2002 and 2018

ABSTRACT Objective Describe the survival, sociodemographic and clinical characteristics of subjects with disorders of consciousness in a reference rehabilitation center, in a developing country. Methods Patients with disorders of consciousness (DoC) caused by acquired neurological injuries, admitted between the years 2002–2018 in a neurorehabilitation center. Extracted data covered demographics, clinical details, survival time, and discharge information. Cox proportional hazard model and Kaplan-Meier analysis were used to reveal, associations with survival. Result Out of 5064 neurological cases, 159 patients were diagnosed with DoC. The demographic data showed a male dominance (65%), with an average injury age of 42 years. The most common causes were traumatic (41%), anoxic (36%), and vascular (10%), with traffic accidents accounting for 71% of traumatic injuries. The study found that 75% of patients remained in a vegetative state (VS), and 25% in a minimally conscious state (MCS), with an average survival of 2110 days. Conclusion There were no significant differences in survival days between patients in MCS and VS. Patients with traumatic injuries showed a higher survival rate than those with non-traumatic injuries. Age and etiology were identified as factors associated with a higher risk of death.

Exploring bispectral index as an alternative to polysomnography for long-term sleep monitoring in disorders of consciousness.

Sleep plays a crucial role in neuroplasticity and recovery from brain injury, particularly in disorders of consciousness (DoC), including unresponsive wakefulness syndrome (UWS) and minimally conscious state (MCS). Traditional sleep monitoring methods like polysomnography (PSG) are complex and often impractical for long-term use in clinical settings. This study aimed to explore the utility of the Bispectral Index (BIS) as a more practical alternative for monitoring sleep patterns in DoC patients. We conducted simultaneous PSG and BIS monitoring on 38 DoC patients (19 UWS and 19 MCS). The study focused on analyzing sleep timing distribution, the effectiveness of BIS in differentiating sleep stages, and its correlation with consciousness levels. Our findings revealed that DoC patients exhibited irregular and fragmented sleep patterns, necessitating extended monitoring periods. The BIS effectively differentiated various sleep stages, with significant differences in BIS values observed across these stages. However, BIS values did not show significant differences between UWS and MCS patients, indicating that BIS primarily indicates wakefulness rather than cognitive awareness. DoC patients have disturbed sleep-wake cycles that require prolonged monitoring. BIS can well distinguish sleep stages in DoC patients, and the distribution of values is similar to that of normal subjects. However, BIS could not distinguish the level of consciousness of DoC patients. The study demonstrates the potential of BIS as a practical tool for long-term sleep monitoring in DoC patients, offering a less intrusive alternative to traditional methods.

Artificial intelligence and machine learning in disorders of consciousness

Purpose of review As artificial intelligence and machine learning technologies continue to develop, they are being increasingly used to improve the scientific understanding and clinical care of patients with severe disorders of consciousness following acquired brain damage. We here review recent studies that utilized these techniques to reduce the diagnostic and prognostic uncertainty in disorders of consciousness, and to better characterize patients’ response to novel therapeutic interventions. Recent findings Most papers have focused on differentiating between unresponsive wakefulness syndrome and minimally conscious state, utilizing artificial intelligence to better analyze functional neuroimaging and electroencephalography data. They often proposed new features using conventional machine learning rather than deep learning algorithms. To better predict the outcome of patients with disorders of consciousness, recovery was most often based on the Glasgow Outcome Scale, and traditional machine learning techniques were used in most cases. Machine learning has also been employed to predict the effects of novel therapeutic interventions (e.g., zolpidem and transcranial direct current stimulation). Summary Artificial intelligence and machine learning can assist in clinical decision-making, including the diagnosis, prognosis, and therapy for patients with disorders of consciousness. The performance of these models can be expected to be significantly improved by the use of deep learning techniques.

Neuroendoscopic surgery for brainstem hemorrhage: Technical notes and preliminary clinical results

BackgroundBrainstem hemorrhage accounts for a relatively small proportion of spontaneous intracerebral hemorrhages (∼10 %) but tends to occur earlier in life and has poorer prognosis. Numerous studies support the therapeutic potential of minimally invasive hematoma evacuation for intracerebral hemorrhage; however, there have been few assessments of the benefits for brainstem hemorrhage. MethodsWe evaluated the safety and efficacy of a minimally invasive approach under neuroendoscopic guidance with pneumatic arm fixation for removing the hematoma in severe brainstem hemorrhage patients. 14 patients diagnosed with primary brainstem hemorrhage and treated by neuroendoscopy-assisted evacuation at Suzhou Ninth Hospital affiliated to Soochow University were included in the study. Relevant clinical and prognostic date were collected and analyzed. ResultsHematoma volume ranged from 8 to 13 mL according to preoperative CT, while GCS at admission ranged from 4 to 6. The average operative time was 157 min and average intraoperative blood loss was 86 mL. All patients achieved satisfactory hematoma evacuation (over 90 %) according to immediate postoperative CT. Postoperative intensive care unit stay averaged 9.5 days and respiratory support averaged 7.5 days. 11 patients required tracheotomy due to pulmonary infection and absence of pharyngeal reflexes. 9 patients achieved satisfactory functional recovery (GOS score of 4 and 3), while 5 remained in a vegetative state (GOS score of 2). ConclusionNeuroendoscopy provides excellent direct visualization of brainstem hematomas for safe and reliable evacuation. Patients with a new PPH score of 2 or 3 are more likely to benefit from surgical treatment. Large-scale studies are required to identify patients most likely to benefit from this technique.

The Pupillary Light-Off Reflex in Acute Disorders of Consciousness.

In intensive care patients with disorders of consciousness, the pupillary light reflex is a measure of pupillary parasympathetic function. By contrast, the pupillary light-off reflex leads to pupil dilation in response to an abrupt change from light to darkness ("light-off") and reflects combined parasympathetic and sympathetic pupillary function. To our knowledge, this reflex has not been systematically investigated in patients with disorders of consciousness. We hypothesized that the pupillary light-off reflex correlates with consciousness levels after acute brain injury. From November 2022 to March 2023, we enrolled 100 study participants: 25 clinically unresponsive (coma or unresponsive wakefulness syndrome) and 25 clinically low-responsive (minimally conscious state or better) patients from the intensive care units of a tertiary referral center, and 50 age-matched and sex-matched healthy controls. Exclusion criteria were active or chronic eye disease. We used automated pupillometry to assess the pupillary light-off reflex and the pupillary light reflex of both eyes under scotopic conditions in all study participants. The pupillary light-off reflex was strongly correlated with consciousness levels (r = 0.62, p < 0.001), the increase in pupillary diameters being smallest in unresponsive patients (mean ± standard deviation 20% ± 21%), followed by low-responsive patients (mean ± standard deviation 47% ± 26%) and healthy controls (mean ± standard deviation 67% ± 17%; p < 0.001). Similar yet less pronounced patterns were observed for the pupillary light reflex. Twenty-one of 25 (84%) unresponsive patients had preserved pupillary light reflexes, but only seven (28%) had fully preserved pupillary light-off reflexes (p < 0.0001). Of these 7 patients, five (71%) regained awareness. The pupillary light-off reflex may be more sensitive to consciousness levels than the pupillary light reflex. The clinical implications of this finding seem worthy of further investigation, particularly regarding possible benefits for neuromonitoring and prognostication after brain injury.

DOCTer: a novel EEG-based diagnosis framework for disorders of consciousness.

Objective. Accurately diagnosing patients with disorders of consciousness (DOC) is challenging and prone to errors. Recent studies have demonstrated that EEG (electroencephalography), a non-invasive technique of recording the spontaneous electrical activity of brains, offers valuable insights for DOC diagnosis. However, some challenges remain: (1) the EEG signals have not been fully used; and (2) the data scale in most existing studies is limited. In this study, our goal is to differentiate between minimally conscious state (MCS) and unresponsive wakefulness syndrome (UWS) using resting-state EEG signals, by proposing a new deep learning framework.Approach. We propose DOCTer, an end-to-end framework for DOC diagnosis based on EEG. It extracts multiple pertinent features from the raw EEG signals, including time-frequency features and microstates. Meanwhile, it takes clinical characteristics of patients into account, and then combines all the features together for the diagnosis. To evaluate its effectiveness, we collect a large-scale dataset containing 409 resting-state EEG recordings from 128 UWS and 187 MCS cases.Main results. Evaluated on our dataset, DOCTer achieves the state-of-the-art performance, compared to other methods. The temporal/spectral features contributes the most to the diagnosis task. The cerebral integrity is important for detecting the consciousness level. Meanwhile, we investigate the influence of different EEG collection duration and number of channels, in order to help make the appropriate choices for clinics.Significance. The DOCTer framework significantly improves the accuracy of DOC diagnosis, helpful for developing appropriate treatment programs. Findings derived from the large-scale dataset provide valuable insights for clinics.

Spiritual Challenges of Family Caregivers of Patients in a Vegetative State: A Qualitative Content Analysis

Background: Spirituality helps family caregivers of vegetative state patients to overcome the stressful conditions of patient care. However, the illness and caregiver burden may also negatively affect the caregiver’s spirituality. Accordingly, this study aimed to investigate the challenges of family caregivers of patients in a vegetative state. Methods: The present qualitative study was conducted through conventional content analysis from August 2020 to September 2021 in two provinces of Iran (Sistan and Baluchestan and Khorasan Razavi). A total of 20 family caregivers of patients in a vegetative state were included in the study. Semi-structured interviews were employed to collect data. Data were analyzed using the method proposed by Graneheim and Lundman via MAXQDA2020 software. Data collection continued until data saturation and identification of main categories. Results: Based on data analysis, spiritual disintegration and spiritual coherence were identified as the two main categories to describe the spiritual challenges of family caregivers of patients in a vegetative state. The experiences of family caregivers showed that spirituality is effective in accepting the conditions and adapting to the role of the patient caregiver. However, illness and caregiver burden lead to spiritual damage and the turning away of family caregivers from performing religious rites and rituals. Conclusion: Considering the undeniable role of spirituality in dealing with the stressful situation of caring for a patient in a vegetative state, the spiritual needs of family caregivers and the implementation of nursing interventions to enhance the spiritual health of caregivers should be given more attention.

Conventional structural magnetic resonance imaging in differentiating chronic disorders of consciousness

BACKGROUND: Differential diagnosis of chronic disorders of consciousness remains one of the most difficult problems even for experienced clinicians. AIM: To evaluate the inter-expert consistency and capacity of the researcher-developed structural scale based on magnetic resonance imaging to differentiate chronic disorders of consciousness, named, DOC-MRIDS, on a larger sample of patients. MATERIALS AND METHODS: Sixty patients with a clinically stable status diagnosed with consciousness disorders (vegetative state, n=32; minimally conscious state, n=28) were enrolled. The revised coma recovery scale (CRS-R) was included in the clinical assessment. All patients underwent structural magnetic resonance imaging with 3.0-T Siemens scanners including T2 and T1 sequences. Structural changes were assessed using the DOC-MRIDS scale and included the following features: diffuse cortical atrophy, ventricular enlargement, gyri dilatation, leukoaraiosis, brainstem and/or thalamic degeneration, corpus callosum degeneration, and focal corpus callosum lesions. A total score was calculated. Magnetic resonance imaging data were analyzed by three neuroradiologists, and inter-observer agreement (Krippendorf’s alpha) was assessed. RESULTS: A high inter-examiner agreement of the DOC-MRIDS scale score was found, with α=0.806 (95% confidence interval 0.757–0.849). The vegetative state group had a higher DOC-MRIDS score than the minimally conscious state group (p 0.005). A negative correlation was obtained between CRS-R and DOC-MRIDS scale scores (ρ=–0.457, p 0.0001), individual clinical scale domains, and magnetic resonance imaging features. CONCLUSION: When assessing structural changes in patients with chronic consciousness disorders, the use of the DOC-MRIDS scale helps differentiate the type of such disorders with sufficient specificity, sensitivity, and inter-rater agreement. This scale can be used in clinical practice as an additional differential diagnostic tool.

The effect of cranioplasty on outcomes and complications of unresponsive wakefulness syndrome and minimally responsive state.

Background Studies that have shown neurological improvement following cranioplasty (CP) after decompressive craniectomy (DC) in patients with unresponsive wakefulness syndrome (UWS) and minimally conscious state (MCS) did not include control groups. The aim of this study was to assess the justification of CP for these patients. Methods Data were collected from medical records of inpatients with UWS and MCS admitted between 2002 and 2018. Results Of the 144 participants (mean age 40 years, 76% males, 75% in UWS), 37% had CP following DC. The Loewenstein Communication Scale (LCS) gain was 12±17 and 16±17 for the control and study patients, respectively. The corresponding consciousness recovery rate (based on Coma Recovery Scale-Revised scores) was 51% and 53%, respectively. One-year survival rates were 0.80 and 0.93, and 5-year survival rates were 0.67 and 0.73, respectively. Mean outcome values were higher for the study group, but the differences between the groups did not reach statistical significance. Conclusions The study did not demonstrate that CP increases brain recovery or survival. Nevertheless, it showed that CP did not decrease them either, and it did not increase complications rate. The findings, therefore, support offering CP to patients with UWS and MCS as CP does not increase risks and can achieve additional goals for these patients.

Rasch Analysis of the Chinese Version of the Nociception Coma Scale-Revised in Patients with Prolonged Disorders of Consciousness.

The aim of this study was to analyze the Chinese version of the Nociception Coma Scale-Revised in patients with prolonged disorders of consciousness within the framework of Rasch modeling, including investigating the invariance of total scores across different etiologies of disorders of consciousness. Prospective psychometric study. Patients with prolonged disorders of consciousness from the Rehabilitation and Neurology units in hospital. None. The Nociception Coma Scale-Revised was undertaken by trained raters and the Coma Recovery Scale-Revised was used to assess patients' consciousness. The psychometric properties within the Rasch model including item-person targeting, reliability and separation, item fit, unidimensionality, and differential item functioning were assessed. 84 patients with prolonged disorders of consciousness (mean age 53 years; mean injury 5 months; 42 with Minimally Conscious State and 42 with Unresponsive Wakefulness Syndrome) of 252 observations were enrolled in the study. Through the procedure of repeated assessment and differential item function, a lower item bias Rasch set was purified. The Rasch model assumptions were examined and met, with item reliability and validity meeting the recommended threshold. The Chinese version of the Nociception Coma Scale-Revised demonstrated unidimensionality, good reliability and separation, and good item fit, but dissatisfied person fit and item-person targeting. The verbal subscale showed a notable discrepancy between person responses and the difficulty of the items, suggesting limited clinical significance.

Mortality Predictors and Neurological Outcomes Following Extracorporeal Cardiopulmonary Resuscitation (eCPR): A Single-Center Retrospective Study.

Extracorporeal cardiopulmonary resuscitation (eCPR) offers cardiorespiratory support to patients experiencing cardiac arrest. However, this technology is not yet considered a standard treatment, and the evidence on eCPR criteria and its association with survival and good neurological outcomes remains scarce. Therefore, we aimed to investigate the overall mortality and risk factors for mortality. Moreover, we provide a comparison of demographic, clinical, and laboratory characteristics of patients, including neurological outcomes and adverse events during support. This retrospective analysis included in-hospital and out-of-hospital cardiac arrest patients who received eCPR and were admitted between January 2008 and June 2022 at a tertiary and trauma one-level university hospital in Austria. In total, 90 patients fulfilled inclusion criteria, 41 (46%) patients survived until intensive care unit discharge, and 39 (43%) survived until hospital discharge. The most common cause of cardiac arrest was myocardial infarction (42, 47%), and non-shockable initial rhythm was reported in 50 patients (56%). Of 33 survivors with documented outcomes, 30 had a good recovery as measured with Cerebral Performance Category score, 2 suffered severe disability, and 1 remained in a persistent vegetative state. Finally, multivariate analysis identified asystole as initial rhythm (HR 2.88, p = 0.049), prolonged CPR (HR 1.02, p = 0.043), and CPR on the weekend (HR 2.57, p = 0.032) as factors with a higher risk of mortality. eCPR-related decision-making could be additionally supported by the comprehension of the reported risk factors for mortality and severe disability. Further studies are needed to elucidate the impact of peri-arrest variables on outcomes, aiming to improve patient selection.

Assessment of the Potential of Modern Diagnostic Tools in Differentiation of Minimum Conscious State from the Vegetative State

Background The problem of treatment of patients in coma and persistent disturbances of consciousness is difficult and often neglected. Purpose The study validated the effectiveness of functional magnetic resonance imaging, electroencephalography and isotopic brain perfusion in differentiating vegetative state from minimal conscious state. Methods The study was performed in a group of 61 patients with persistent disturbances of consciousness. All patients underwent CRS-R differentiation of consciousness and functional magnetic resonance imaging, EEG, and SPECT/CT studies according to a uniform diagnostic protocol. Results Analysis has shown that functional magnetic resonance imaging is an effective tool for detecting of consciousness. Analysis of the EEG showed that the occurrence of primary alpha rhythm in patients with impaired consciousness coincides with the state of minimal conscious and may indicate a favourable prognosis. Sector analysis of isotopic cerebral perfusion allowed to put forward the thesis that the key role in the maintenance of consciousness plays a medial part of the left parietal lobe. Conclusion On the basis of these results, diagnostic protocol was developed for patients with disturbances of consciousness.

Cognitive Motor Dissociation in Disorders of Consciousness

BackgroundPatients with brain injury who are unresponsive to commands may perform cognitive tasks that are detected on functional magnetic resonance imaging (fMRI) and electroencephalography (EEG). This phenomenon, known as cognitive motor dissociation, has not been systematically studied in a large cohort of persons with disorders of consciousness.MethodsIn this prospective cohort study conducted at six international centers, we collected clinical, behavioral, and task-based fMRI and EEG data from a convenience sample of 353 adults with disorders of consciousness. We assessed the response to commands on task-based fMRI or EEG in participants without an observable response to verbal commands (i.e., those with a behavioral diagnosis of coma, vegetative state, or minimally conscious state–minus) and in participants with an observable response to verbal commands. The presence or absence of an observable response to commands was assessed with the use of the Coma Recovery Scale–Revised (CRS-R).ResultsData from fMRI only or EEG only were available for 65% of the participants, and data from both fMRI and EEG were available for 35%. The median age of the participants was 37.9 years, the median time between brain injury and assessment with the CRS-R was 7.9 months (25% of the participants were assessed with the CRS-R within 28 days after injury), and brain trauma was an etiologic factor in 50%. We detected cognitive motor dissociation in 60 of the 241 participants (25%) without an observable response to commands, of whom 11 had been assessed with the use of fMRI only, 13 with the use of EEG only, and 36 with the use of both techniques. Cognitive motor dissociation was associated with younger age, longer time since injury, and brain trauma as an etiologic factor. In contrast, responses on task-based fMRI or EEG occurred in 43 of 112 participants (38%) with an observable response to verbal commands.ConclusionsApproximately one in four participants without an observable response to commands performed a cognitive task on fMRI or EEG as compared with one in three participants with an observable response to commands. (Funded by the James S. McDonnell Foundation and others.)

Perspectives of healthcare providers on withdrawal of life-sustaining treatment and advanced directives for unresponsive wakefulness syndrome in China.

We performed the current research to describe healthcare providers' perspectives toward withdrawal of life-sustaining treatment (WLST) and advanced directive (AD) of patients with unresponsive wakefulness syndrome (UWS) and to identify influencing factors of their perspectives. Healthcare providers were recruited during a professional conference on disorders of consciousness (DoC). Participants completed self-administered questionnaires which included demographics, personal perspectives regarding WLST and the perception of ADs. A total of 230 Chinese healthcare providers (female: 69.7%) were included. Only a small proportion reported positive attitudes toward withdrawing artificial nutrition and hydration (35.2%), antibiotics (30.9%), and do-not-resuscitation orders (23.5%) in UWS patients. As for predictors' identification, religion was significantly associated with the positive attitude toward DNR order (p = 0.004). Moreover, although 47.4% of the participants had never heard of ADs before of conference, almost all of them would consider ADs (95.7%) thereafter, especially for non-neurologists (p = 0.033). The propensity to WLST for UWS in China is low and perspective on WLST is significantly associated with individual characteristics. The attitudes of healthcare providers toward integrating ADs in the decisional process are positive. Future research regarding ADs and their predictors should be carried out to improve the quality of end-of-life care of UWS in China.

Phenolic Compounds in Different Stages of Ontogenesis in Chrysanthemum—A Potential for Thrips-Resistance Characterisation

A number of studies have indicated the potential role of secondary metabolites, referred to as ‘resistance factors’, in plant defence against insect pests. Nevertheless, it remains unclear which metabolites serve as predictors of resistance in chrysanthemum cultivars against thrips. In the present study, the phenolic compounds of chrysanthemum leaves at different ontogenetic stages were analysed using high-performance liquid chromatography (HPLC). Furthermore, the relative epidermal flavonol contents in the leaves were quantified using the Dualex® Scientific 4 sensor, and the suitability of this non-destructive method for the rapid discrimination of resistance levels was evaluated. The results demonstrated that the most notable discrepancies in phenolic metabolite profiles were observed in the older leaves and the vegetative state of the chrysanthemum plants. Multiple discriminant analysis was conducted using HPLC-analysed metabolites to predict the importance of metabolites in resistant, susceptible, or highly susceptible plants in the vegetative stage. The results demonstrated that multiple metabolites, rather than a single metabolite, are responsible for thrips resistance in chrysanthemum. However, the relative flavonol content did not reflect the HPLC-analysed flavonoid glycosides or hydroxycinnamic acid derivatives, indicating that the Dualex® sensor is not a suitable device for determining resistance levels in chrysanthemums. Testing is required to extend and analyse the results in greater depth.

Using contact network dynamics to implement efficient interventions against pathogen spread in hospital settings: A modelling study.

Long-term care facilities (LTCFs) are hotspots for pathogen transmission. Infection control interventions are essential, but the high density and heterogeneity of interindividual contacts within LTCF may hinder their efficacy. Here, we explore how the patient-staff contact structure may inform effective intervention implementation. Using an individual-based model (IBM), we reproduced methicillin-resistant Staphylococcus aureus colonisation transmission dynamics over a detailed contact network recorded within a French LTCF of 327 patients and 263 staff over 3 months. Simulated baseline cumulative colonisation incidence was 21 patients (prediction interval: 11, 31) and 35 staff (prediction interval: 19, 54). We examined the potential impact of 3 types of interventions against transmission (reallocation reducing the number of unique contacts per staff, reinforced contact precautions, and hypothetical vaccination protecting against acquisition), targeted towards specific populations. All 3 interventions were effective when applied to all nurses or healthcare assistants (median reduction in MRSA colonisation incidence up to 35%), but the benefit did not exceed 8% when targeting any other single staff category. We identified "supercontactor" individuals with most contacts ("frequency-based," overrepresented among nurses, porters, and rehabilitation staff) or with the longest cumulative time spent in contact ("duration-based," overrepresented among healthcare assistants and patients in elderly care or persistent vegetative state (PVS)). Targeting supercontactors enhanced interventions against pathogen spread in the LTCF. With contact precautions, targeting frequency-based staff supercontactors led to the highest incidence reduction (20%, 95% CI: 19, 21). Vaccinating a mix of frequency- and duration-based staff supercontactors led to a higher reduction (23%, 95% CI: 22, 24) than all other approaches. Although based on data from a single LTCF, when varying epidemiological parameters to extend to other pathogens, our results suggest that targeting supercontactors is always the most effective strategy, indicating this approach could be applied to prevent transmission of other nosocomial pathogens. By characterising the contact structure in hospital settings and identifying the categories of staff and patients more likely to be supercontactors, with either more or longer contacts than others, interventions against nosocomial spread could be more effective. We find that the most efficient implementation strategy depends on the intervention (reallocation, contact precautions, vaccination) and target population (staff, patients, supercontactors). Importantly, both staff and patients may be supercontactors, highlighting the importance of including patients in measures to prevent pathogen transmission in LTCF.