BACKGROUND: Decannulation for people in a persistent vegetative state (PVS) is challenging and relevant predictors of successful decannulation have yet to be identified.
OBJECTIVE: This study aimed to explore the predictors of tracheostomy decannulation outcomes in individuals in PVS and to develop a nomogram.
METHODS: In 2022, 872 people with tracheostomy in PVS were retrospectively enrolled and their data was randomly divided into a training set and a validation set in a 7:3 ratio. Univariate and multivariate regression analyses were performed on the training set to explore the influencing factors for decannulation and nomogram development. Internal validation was performed using 5-fold cross-validation. External validation was performed using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) on both the training and validation sets.
RESULTS: Data from 610 to 262 individuals were used for the training and validation sets, respectively. The multivariate regression analysis found that duration of tracheostomy tube placement≥30 days (Odds Ratio [OR] 0.216, 95 % CI 0.151-0.310), pulmonary infection (OR 0.528, 95 %CI 0.366-0.761), hypoproteinemia (OR 0.669, 95 % CI 0.463-0.967), no passive standing training (OR 0.372, 95 % CI 0.253-0.547), abnormal swallowing reflex (OR 0.276, 95 % CI 0.116-0.656), mechanical ventilation (OR 0.658, 95 % CI 0.461-0.940), intensive care unit (ICU) duration>4 weeks (OR 0.517, 95 % CI 0.332-0.805), duration of endotracheal tube (OR 0.855, 95 % CI 0.803-0.907), older age (OR 0.981, 95 % CI 0.966-0.996) were risk factors for decannulation failure. Conversely, peroral feeding (OR 1.684, 95 % CI 1.178-2.406), passive standing training≥60 min (OR 1.687, 95 % CI 1.072-2.656), private caregiver (OR 1.944, 95 % CI 1.350-2.799) and ICU duration<2 weeks (OR 1.758, 95 % CI 1.173-2.634) were protective factors conducive to successful decannulation. The 5-fold cross-validation revealed a mean area under the curve of 0.744. The ROC curve C-indexes for the training and validation sets were 0.784 and 0.768, respectively, and the model exhibited good stability and accuracy. The DCA revealed a net benefit when the risk threshold was between 0 and 0.4.
CONCLUSIONS: The nomogram can help adjust the treatment and reduce decannulation failure.
BACKGROUND: Clinical registration is not mandatory for retrospective studies.

BACKGROUND: Research on disorders of consciousness in children is scarce and includes disparate and barely comparable participants and assessment instruments and therefore provides inconclusive information on the clinical progress and recovery in this population. This study retrospectively investigated the neurobehavioral progress and the signs of transition between states of consciousness in a group of children admitted to a rehabilitation program either with an unresponsive wakefulness syndrome (UWS) or in a minimally conscious state (MCS).
METHODS: Systematic weekly assessments were conducted with the Coma Recovery Scale-Revised (CRS-R) until emergence from MCS, discharge, or death.
RESULTS: Twenty-one children, nine admitted with a UWS and 12 admitted in an MCS, were included in the study. Four children with a UWS transitioned to an MCS with a CRS-R of 10 (9.2 to 12.2) by showing visual pursuit, visual fixation, or localization to noxious stimulation. Twelve children emerged from the MCS with a CRS-R of 20.5 (19 to 21.7). Children who emerged from the MCS had had a shorter time postinjury and higher CRS-R at admission, compared with those who did not emerge.
CONCLUSIONS: Almost half of the children who were admitted with a UWS transitioned to an MCS, and almost all who were admitted in an MCS emerged from this state. Children who emerged had shorter times since injury and higher scores on the CRS-R at admission, compared with those who did not emerge.

The assessment of consciousness states, especially distinguishing minimally conscious states (MCS) from unresponsive wakefulness states (UWS), constitutes a pivotal role in clinical therapies. Despite that numerous neural signatures of consciousness have been proposed, the effectiveness and reliability of such signatures for clinical consciousness assessment still remains an intense debate. Through a comprehensive review of the literature, inconsistent findings are observed about the effectiveness of diverse neural signatures. Notably, the majority of existing studies have evaluated neural signatures on a limited number of subjects (usually below 30), which may result in uncertain conclusions due to small data bias. This study presents a systematic evaluation of neural signatures with large-scale clinical resting-state electroencephalography (EEG) signals containing 99 UWS, 129 MCS, 36 emergence from the minimally conscious state, and 32 healthy subjects (296 total) collected over 3 years. A total of 380 EEG-based metrics for consciousness detection, including spectrum features, nonlinear measures, functional connectivity, and graph-based measures, are summarized and evaluated. To further mitigate the effect of data bias, the evaluation is performed with bootstrap sampling so that reliable measures can be obtained. The results of this study suggest that relative power in alpha and delta serve as dependable indicators of consciousness. With the MCS group, there is a notable increase in the phase lag index-related connectivity measures and enhanced functional connectivity between brain regions in comparison to the UWS group. A combination of features enables the development of an automatic detector of conscious states.

The minimally conscious state (MCS) is a prolonged disorder of consciousness (pDoC) and one of the most severe outcomes of acquired brain injury. Prevalence data are scarce. The aim of this study was to establish the nationwide point prevalence of institutionalized patients in MCS in the Netherlands.
This was a descriptive cross-sectional study in which all 86 Dutch hospitals, all 5 specialized pDoC rehabilitation facilities, and all 274 nursing homes were asked whether they were treating patients with a pDoC on the point prevalence date of September 15, 2021. Each patient\'s legal representative provided informed consent for their inclusion. Patient level of consciousness was verified using the Coma Recovery Scale-Revised (CRS-R) in a single assessment session performed in the facility of residence by an experienced physician. Data on patient demographics, etiology, level of consciousness, facility of residence, and clinical status were collected from a questionnaire by the treating physician. The prevalence of institutionalized patients in MCS of per 100,000 members of the Dutch population was calculated, based on actual census data.
Seventy patients were reported to have a pDoC, of whom 6 were excluded. The level of consciousness was verified for 49 patients while for 15, it could not be verified. Of the patients verified, 38 had a pDoC, of whom 32 were in MCS (mean age 44.8 years, 68.8% male). The prevalence of institutionalized patients in MCS is 0.2-0.3 per 100,000 Dutch inhabitants. Traumatic brain injury was present in 21 of 32 patients (65.6%). Specialized pDoC rehabilitation was received by 17 of 32 patients (53%), with the rest admitted to nursing homes. The most frequent signs of consciousness on the CRS-R were visual pursuit, reproducible movement to command, and automatic motor response.
This nationwide study revealed a low prevalence of institutionalized patients in MCS in the Netherlands. These findings are now being used to organize pDoC care in this country.

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.
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.
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.
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.

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.

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.
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.
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.
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.

Trigeminal nerve stimulation (TNS) has been proposed as a promising intervention for coma awakening. However, the effect of TNS on patients with prolonged disorders of consciousness (pDoC) is still unclear.
This study aimed to investigate the therapeutic effects of TNS in pDoC caused by stroke, trauma, and anoxia.
A total of 60 patients (male =25, female =35) aged over 18 who were in a vegetative state or minimally conscious state were randomly assigned to the TNS (N = 30) or sham TNS (N = 30) groups. 4 weeks of intervention and a followed up for 8 weeks were performed. The Glasgow Coma Scale (GCS) and Coma Recovery Scale-Revised (CRS-R) scores as primary outcomes were assessed at baseline and at 2, 4, 8, and 12 weeks.
The score changes in the TNS group over time for CRS-R (2-week: mean difference = 0.9, 95% CI = [0.3, 1.5], P = 0.006; 4-week: 1.6, 95% CI = [0.8, 2.5], P < 0.001; 8-week: mean difference = 2.4, 95% CI = [1.3, 3.5], P < 0.001; 12-week: mean difference = 2.3, 95% CI = [1.1, 3.4], P < 0.001) and GCS (4-week: mean difference = 0.7, 95% CI = [0.3, 1.2], P = 0.002; 8-week: mean difference = 1.1, 95% CI = [0.6, 1.7], P < 0.001; 12-week: 1.1, 95% CI = [0.5, 1.7], P = 0.003) were higher than those in the sham group. 18-Fluorodeoxyglucose Positron Emission Tomography (FDG-PET) revealed that the metabolism of the right parahippocampal cortex, right precuneus, and bilateral middle cingulate cortex was significantly increased in TNS group.
The results of this study indicate that TNS could increase local brain metabolism and may promote functional recovery in patients with prolonged disorders of consciousness.
Name of the registry: Chinese Clinical Trial Registry.
ChiCTR1900025573. The date that the study was submitted to a registry: 2019-09-01. The date when the first patient was enrolled was 2021-01-20.

UNASSIGNED: This paper presents a preliminary study on whether repetitive transcranial magnetic stimulation (rTMS) can modulate the nutritional status of persistent vegetative state (PVS) patients (the primary endpoint) by regulating the intestinal flora and the metabolites, with the correlation between them also investigated.
UNASSIGNED: Seventy-six patients with PVS were selected and divided into the observation group (n = 38) and the control group (n = 38) by random numerical grouping. All subjects\' stool samples were examined for metabolites and analyzed regarding the short-chain fatty acids (SCFAs) content. All subjects\' serum albumin, prealbumin, and hemoglobin levels were measured before and after the treatment. Nutrition risk screening 2002 was performed on all the subjects before and after the treatment and on the 30th and 90th days of the follow-up.
UNASSIGNED: (1) Intestinal flora structure: the Chao index, Ace index, and Shannon index of the observation group and the control group were significantly higher (p < 0.05), while the Simpson index was significantly lower (p < 0.05) following the treatment. (2) Metabolites of the intestinal flora: the observation group had significantly higher levels of acetic acid, butyric acid, and valeric acid (p < 0.05), as well as lower levels of propionic acid (p < 0.05) following the treatment. (3) Nutritional status (the primary endpoint): following the treatment, the above serum nutritional indices were significantly higher in both groups (p < 0.05), while the indices of the observation group were significantly higher than those of the control group (p < 0.05).
UNASSIGNED: The rTMS method may improve the nutritional status of patients with PVS by regulating the structure of the intestinal flora and affecting the level of SCFAs through the microbiota-gut-brain axis. The possible mechanism involves how high-frequency rTMS can cause increased excitation in the frontal lobe of the right side of the brain, thus regulating the 5-hydroxytryptamine and norepinephrine levels.

The difficulty of accurately identifying patients who would benefit from promising treatments makes it challenging to prove the efficacy of novel treatments for traumatic brain injury (TBI). Although machine learning is being increasingly applied to this task, existing binary outcome prediction models are insufficient for the effective stratification of TBI patients. The aim of this study was to develop an accurate 3-class outcome prediction model to enable appropriate patient stratification. To this end, retrospective balanced data of 1200 blunt TBI patients admitted to six Japanese hospitals from January 2018 onwards (200 consecutive cases at each institution) were used for model training and validation. We incorporated 21 predictors obtained in the emergency department, including age, sex, six clinical findings, four laboratory parameters, eight computed tomography findings, and an emergency craniotomy. We developed two machine learning models (XGBoost and dense neural network) and logistic regression models to predict 3-class outcomes based on the Glasgow Outcome Scale-Extended (GOSE) at discharge. The prediction models were developed using a training dataset with n = 1000, and their prediction performances were evaluated over two validation rounds on a validation dataset (n = 80) and a test dataset (n = 120) using the bootstrap method. Of the 1200 patients in aggregate, the median patient age was 71 years, 199 (16.7%) exhibited severe TBI, and emergency craniotomy was performed on 104 patients (8.7%). The median length of stay was 13.0 days. The 3-class outcomes were good recovery/moderate disability for 709 patients (59.1%), severe disability/vegetative state in 416 patients (34.7%), and death in 75 patients (6.2%). XGBoost model performed well with 69.5% sensitivity, 82.5% accuracy, and an area under the receiver operating characteristic curve of 0.901 in the final validation. In terms of the receiver operating characteristic curve analysis, the XGBoost outperformed the neural network-based and logistic regression models slightly. In particular, XGBoost outperformed the logistic regression model significantly in predicting severe disability/vegetative state. Although each model predicted favorable outcomes accurately, they tended to miss the mortality prediction. The proposed machine learning model was demonstrated to be capable of accurate prediction of in-hospital outcomes following TBI, even with the three GOSE-based categories. As a result, it is expected to be more impactful in the development of appropriate patient stratification methods in future TBI studies than conventional binary prognostic models. Further, outcomes were predicted based on only clinical data obtained from the emergency department. However, developing a robust model with consistent performance in diverse scenarios remains challenging, and further efforts are needed to improve generalization performance.