Epidemiology and outcome predictors in 450 patients with hanging-induced cardiac arrest: a retrospective study

BackgroundCardiac arrest is the most life-threatening complication of attempted suicide by hanging. However, data are scarce on its characteristics and outcome predictors.MethodsThis retrospective observational multicentre study in 31 hospitals included consecutive adults admitted after cardiac arrest induced by suicidal hanging. Factors associated with in-hospital mortality were identified by multivariate logistic regression with multiple imputations for missing data and adjusted to the temporal trends over the study period.ResultsOf 450 patients (350 men, median age, 43 [34–52] years), 305 (68%) had a psychiatric history, and 31 (6.9%) attempted hanging while hospitalized. The median time from unhanging to cardiopulmonary resuscitation was 0 [0–5] min, and the median time to return of spontaneous circulation (ROSC) was 20 [10–30] min. Seventy-nine (18%) patients survived to hospital discharge. Three variables were independently associated with higher in-hospital mortality: time from collapse or unhanging to ROSC>20 min (odds ratio [OR], 4.71; 95% confidence intervals [95%CIs], 2.02–10.96; p = 0.0004); glycaemia >1.4 g/L at admission (OR, 6.38; 95%CI, 2.60–15.66; p < 0.0001); and lactate >3.5 mmol/L at admission (OR, 6.08; 95%CI, 1.71–21.06; p = 0.005). A Glasgow Coma Scale (GCS) score of >5 at admission was associated with lower in-hospital mortality (OR, 0.009; 95%CI, 0.02–0.37; p = 0.0009).ConclusionIn patients with hanging-induced cardiac arrest, time from collapse or unhanging to return of spontaneous circulation, glycaemia, arterial lactate, and coma depth at admission were independently associated with survival to hospital discharge. Knowledge of these risk factors may help guide treatment decisions in these patients at high risk of hospital mortality

A covariate-constraint method to map brain feature space into lower dimensional manifolds

International audienceHuman brain connectome studies aim at both exploring healthy brains, and extracting and analyzing relevant features associated to pathologies of interest. Usually this consists in modeling the brain connectome as a graph and in using graph metrics as features. A fine brain description requires graph metrics computation at the node level. Given the relatively reduced number of patients in standard cohorts, such data analysis problems fall in the high-dimension low sample size framework. In this context, our goal is to provide a machine learning technique that exhibits flexibility, gives the investigator grip on the features and covariates, allows visualization and exploration, and yields insight into the data and the biological phenomena at stake. The retained approach is dimension reduction in a manifold learning methodology, the originality lying in that one (or several) reduced variables be chosen by the investigator. The proposed method is illustrated on two studies, the first one addressing comatose patients, the second one addressing young versus elderly population comparison. The method sheds light on the differences between brain connectivity graphs using graph metrics and potential clinical interpretations of theses differences

Resting-state networks distinguish locked-in from vegetative state patients

Purpose: Locked-in syndrome and vegetative state are distinct outcomes from coma. Despite their differences, they are clinically difficult to distinguish at the early stage and current diagnostic tools remain insufficient. Since some brain functions are preserved in locked-in syndrome, we postulated that networks of spontaneously co-activated brain areas might be present in locked-in patients, similar to healthy controls, but not in patients in a vegetative state. Methods: Five patients with locked-in syndrome, 12 patients in a vegetative state and 19 healthy controls underwent a resting-state fMRI scan. Individual spatial independent component analysis was used to separate spontaneous brain co-activations from noise. These co-activity maps were selected and then classified by two raters as either one of eight resting-state networks commonly shared across subjects or as specific to a subject. Results: The numbers of spontaneous co-activity maps, total resting-state networks, and resting-state networks underlying high-level cognitive activity were shown to differentiate controls and locked-in patients from patients in a vegetative state. Analyses of each common resting-state network revealed that the default mode network accurately distinguished locked-in from vegetative-state patients. The frontoparietal network also had maximum specificity but more limited sensitivity. Conclusions: This study reinforces previous reports on the preservation of the default mode network in locked-in syndrome in contrast to vegetative state but extends them by suggesting that other networks might be relevant to the diagnosis of locked-in syndrome. The aforementioned analysis of fMRI brain activity at rest might be a step in the development of a diagnostic biomarker to distinguish locked-in syndrome from vegetative state

Hubs of brain functional networks are radically reorganized in comatose patients

International audienceHuman brain networks have topological properties in common with many other complex systems, prompting the following question: what aspects of brain network organization are critical for distinctive functional properties of the brain, such as consciousness? To address this question, we used graph theoretical methods to explore brain network topology in resting state functional MRI data acquired from 17 patients with severely impaired consciousness and 20 healthy volunteers. We found that many global network properties were conserved in comatose patients. Specifically, there was no significant abnormality of global efficiency, clustering, small-worldness, modularity, or degree distribution in the patient group. However, in every patient, we found evidence for a radical reorganization of high degree or highly efficient "hub" nodes. Cortical regions that were hubs of healthy brain networks had typically become nonhubs of comatose brain networks and vice versa. These results indicate that global topological properties of complex brain networks may be homeostatically conserved under extremely different clinical conditions and that consciousness likely depends on the anatomical location of hub nodes in human brain networks

Out of the ICU shifting as a significant workload

IF 15.008Lettre à l'éditeur ("Intensive Care Medicine")https://link.springer.com/article/10.1007%2Fs00134-018-5240-

Immunocompromised patients with SARS-CoV-2 infection in intensive care units, outcome and mortality

Background: The new severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) outbreak severely hit Northeastern France from March to May 2020. The massive arrival of SARS-CoV-2 positive patients in the intensive care units (ICU) raised the question of how immunocompromised patients would be affected. Therefore, we analyzed the clinical, biological and radiological features of 24 immunocompromised ICU patients with severe SAR-CoV-2 infection. Results: The mortality rate was significantly higher for immunocompromised patients compared with other patients (41.7% versus 27.3%, respectively, p = 0.021). Mortality occurred in the first 2 weeks of intensive care, highlighting the possible interest in prolonged full-code managnement of these patients. Finally, patients with lymphoid malignancies appeared to be particularly affected, mostly with monoclonal gamma-pathology. Conclusion: Mortality rate of SARS-CoV-2 acute respiratory syndrome in immuno-compromised patient is high. No treatment was associated with survival improvement. Prolonged full-code management is required for these patients

Diffusion tensor imaging in human global cerebral anoxia: correlation with histology in a case with autopsy

International audiencepas de résum

Acute‐onset delirium in intensive care COVID patients: association of imperfect brain repair with foodborne micro‐pollutants

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