Coma

To assess the performance of a predictive model resulting from the analysis of sMRI/fMRI/contrast-enhanced MRI-derived personalized connectomic data, as compared with standard predictors (clinical examination, electrophysiology, serum biomarker, standard neuroimaging) collected ≥ 72h from sedation withdrawal and in normothermia condition, to predict anoxoischemic coma neurological outcome at 6 months.

The goal of this observational study is to learn whether functional near-infrared spectroscopy (fNIRS) can measure brain activity in healthy adults and in people with disorders of consciousness (DoC) in the neuro-intensive care unit (Neuro-ICU). DoC include conditions such as coma and minimally conscious state that occur after severe brain injury. These conditions make it difficult to assess a person's level of awareness because many clinical tests rely on observable behaviors such as speaking or moving, which are commonly impaired after brain injury. The main questions the study aims to answer are: * Can fNIRS detect changes in brain activity in healthy adults when they receive sensory stimulation or perform mental tasks? * Can the same fNIRS protocol be used in patients with disorders of consciousness in the Neuro-ICU to measure brain responses and determine whether the method is feasible in this clinical setting? The investigators will first study healthy adult volunteers to establish baseline brain responses and determine which tasks produce the most reliable signals. The protocol will then be applied to patients with disorders of consciousness admitted to the Neuro-ICU. Participants will take part in a single research session lasting about 30 to 45 minutes while wearing the lightweight fNIRS headband that measures brain oxygen levels using near-infrared light. During the session, participants will: * Wear a non-invasive fNIRS headband placed on the forehead * Receive gentle sensory stimulation (for example, compression devices on the legs or hands) * Listen to sounds or spoken sentences * Perform guided mental tasks such as imagining walking through their home or imagining moving a limb The study does not test a treatment and will not change medical care. The goal is to determine whether fNIRS can safely and reliably measure brain activity at the bedside and provide preliminary information that may help guide future research on improving the assessment of consciousness after brain injury.

This study aims to better understand recovery after coma caused by serious neurologic illness or injury. Patients who are unconscious (in a coma or disorder of consciousness) due to conditions such as stroke, cardiac arrest, traumatic brain injury, seizures, brain infection, or other neurologic emergencies may be enrolled during their hospitalization. The purpose of this observational research study is to learn which medical, neurologic, psychological, and social factors are associated with recovery over time. Researchers will collect information from the medical record during hospitalization, including details about the patient's illness, treatments received, brain imaging, and neurologic examinations. For patients who survive hospitalization, the study team will contact participants or their caregivers after discharge to assess recovery at scheduled time points using questionnaires and structured interviews about physical function, quality of life, emotional well-being, and daily activities. This study does not assign participants to any experimental treatment. Participation will not change the medical care patients receive. Information learned from this study may help improve future care for patients with coma and disorders of consciousness.

Improving Telephone Triage in Emergency Calls with AI The Coordinating Centre for Urgencies and Emergencies in Andalusia (CCUE) handles thousands of calls every day. Each call needs to be assessed based on the information given over the phone to determine how serious the case is. The reasons for calling range from minor health issues to life-threatening emergencies like cardiac arrest (CPA). This project focuses on improving telephone triage for four key emergency situations that often indicate severe or life-threatening conditions: Unconsciousness / Cardiac arrest Difficulty breathing Chest pain (non-traumatic, possible heart-related issues) Stroke symptoms Our goal is to make telephone triage more accurate and efficient by using advanced Artificial Intelligence (AI) techniques, including Machine Learning (ML) and Natural Language Processing (NLP). These tools will help CCUE operators make better and faster decisions, ensuring that patients receive the right care as quickly as possible. How it will be done: The investigators will analyze anonymized historical call data from the emergency coordination system (CCR) and digital clinical records (HCDM). This includes: Structured data: Predefined fields, such as answers to standard triage questions. Unstructured data: Free-text notes and other information recorded during the call. A hybrid AI approach will be used, combining: Traditional AI methods (supervised learning and deep learning) to classify cases. Generative AI techniques (advanced language models) to extract useful insights from free-text data. Building the Best Prediction Model To find the most effective AI model, we will test different machine learning techniques, including: Decision Trees Random Forests Support Vector Machines (SVM) XGBoost Ensemble methods Neural Networks We will also analyze which questions and variables are the most important in predicting the severity of a case. Based on this, we will suggest improvements to the current triage questions to enhance accuracy. Measuring Success We will evaluate the AI model using key performance metrics, including: Accuracy (overall correctness) Sensitivity (ability to detect real emergencies) Specificity (ability to avoid false alarms) False Positive \& False Negative Rates (how often the system makes mistakes) Likelihood Ratios (how well the system distinguishes between urgent and non-urgent cases) F1-Score \& ROC Curve (overall performance indicators) Why This Matters This project will assess how effective the current telephone triage system is and develop a new AI-powered model to improve it. The goal is to help emergency operators quickly identify the most serious cases, reducing response times and improving patient outcomes. In the future, the investigators aim to integrate this improved AI model into the CCUE system to enhance emergency response across Andalusia.

The purpose of this research study is to determine whether COMA-F is more efficacious at reducing emotional distress in caregivers of patients with severe acute brain injuries, compared to health education control.

Evaluating the prognosis of comatose patients after cardiac arrest (CA) in the intensive care unit (ICU) remains challenging. It requires a multimodal approach combining standardized clinical examination, serum biomarkers, imaging and classically electrophysiological examinations, (among them auditive evoked potentials or AEP) but none has a sufficient sensitivity/specificity. In a preliminary study, the investigators developed an algorithm from the signal collected with AEP, and generated a probability map to visually classify the participants after the algorithm processing. Participants could be classified either with a good neurological prognosis or with bad neurological prognosis or death. The investigators hypothesize that the "PRECOM" tool, applied blindly to a large prospective multicenter cohort of patients admitted to intensive care for coma in the aftermath of CA will predict neurological prognosis at 3 months with high sensitivity and specificity.

Standard predictors of outcome after cardiac arrest (CA) have substantial limitations in terms of reliability and generalizability. By providing brain structural connectivity maps, or connectomes, advanced MRI techniques, operating through high-strength magnetic field (HF; 1.5 to 3-T), have precisely revealed white and grey brain matter damages induced by CA, and have demonstrated the high sensitivity and specificity of these indicators for predicting neurological outcome after CA. However, HF MRI requires rigid safety precautions, highly trained technicians and patient transport to dedicated hospital imaging suites, hindering the implementation of these promising neuroimaging techniques in the setting of critical illness. Interestingly, a recent report demonstrates the capability of a proof-of-concept very low-field (VLF; 0.064-T) mobile MRI to obtain neuroimaging at the bedside in critically ill patients. Nevertheless, the spatial resolution of VLF-MRI seems low and there is no available evidence about the use of VLF-MRI to extract highly needed new predictors of neurological recovery based on critical brain structural connectomes. The CUBE project holds the promise of providing a radical paradigm shift in the field of neuroprognostication of anoxic coma patients. The current proposal is a "proof-of concept" study which aims to compare for the first time, HF, VLF and enhanced VLF (recon-VLF) structural connectomes from anoxic coma patients and healthy subjects across the time (3 paired HF and VLF brain scan across the first two weeks after CA). To obtain recon-VLF data, the Investigators will use an ensemble of ground-breaking methods to increase the native spatial resolution of VLF-MRI data. The whole brain imaging dataset will be used to prepare future neuroprognostication studies based on fully bedside assessment of brain structural integrity after CA.

The goal of this comparative effectiveness trial is to study electrographic status epilepticus (ESE) treatment in comatose patients after cardiac arrest. The main questions the trial aims to answer are: * Does ESE treatment improve outcome? * What is the impact of ESE treatment on healthcare costs? Participants in the the intervention group will receive standard care completed with anti-seizure treatment. The control group will receive standard care without anti-seizure treatment.

Median nerve stimulation (MNS), a non-invasive brain stimulation technique, has been widely adopted in clinical arousal therapies and multiple clinical investigations have attested to the efficacy of this technique; nevertheless, evidence concerning the application of MNS in improving the neurological prognosis of patients with return of spontaneous circulation (ROSC) following cardiac arrest (CA) remains scarce. The current study endeavors to assess the safety and efficacy of MNS treatment in enhancing the neurological prognosis of CA patients after ROSC and it is designed as a multicenter, prospective, randomized controlled trial with an estimated sample size of 400 patients. Eligible patients will be randomly allocated in a 1:1 ratio to either receive MNS treatment or sham stimulation treatment for 8 hours per day for 14 consecutive days and the primary outcome measure is the proportion of patients in each group with a Cerebral Performance Category (CPC) score ranging from 1 to 2, 6 months after randomization, which will help to determine the effectiveness of MNS in providing neuroprotection for patients with ROSC after CA.

Seizures are common in children (\~350 per 100,000 patients per year) and require immediate medical attention. If the seizure is prolonged (\> 5 minutes) it is called status epilepticus and delayed treatment leads to higher risk in drug resistance and brain injury. The current standard of care for children admitted to the ICU with established or suspected status epilepticus is to start a conventional continuous EEG study that helps diagnosing seizures by typical electro graphic patterns. It takes on average 4 hours to start and another two hours to obtain a reading by epileptologists. This is far beyond the time window of starting an EEG study (60 minutes) as recommended by the neurocritical care society. In adult ICUs, point of care "Rapid Response EEG" are becoming a new standard of care and our ICU adopted this practice in 2020. It can be easily placed by the ICU staff rather than a specifically trained EEG technician but has a lower resolution due to fewer leads (10 vs. 20). The purpose of this study is to determine wether and by how much time RR-EEG yields faster preliminary EEG reports that the previously available conventional EEG (cEEG) and wether the detection of electro graphic seizures is comparable. This is a retrospective cohort study following patients who are admitted to the PICU and are placed on either cEEG or RR-EEG for status epilepticus.

Disorders of consciousness (DOC) refers to the persistent loss of consciousness after 28 days in patients with brain injury caused by trauma, stroke, or hypoxia. It includes coma, vegetative state, and minimally conscious state. At present, there is no effective treatment for DOC. Only one RCT study of amantadine has proved that it may be effective for the treatment of DOC. In recent years, more evidence has shown that neuromodulation technology is beneficial to the recovery of DOC. Cervical spinal cord stimulation surgery is a new treatment method for patients with DOC. Electrodes are implanted in the high cervical spinal cord C2-C5. By adjusting different electrical stimulation parameters, it has a wake-promoting effect. In this study, patients were selected into the spinal cord stimulation group and the conventional treatment group according to the wishes of their families. The patients in the spinal cord stimulation group were given 21 days of cervical spinal cord stimulation treatment on the basis of conventional brain rehabilitation. Patients were followed up routinely and completed designated examinations at 12 months to determine the safety and efficacy of cervical spinal cord stimulation therapy.