Endotracheal intubation rate is lower with additional face-mask noninvasive ventilation for critically-ill SARS-CoV-2 patients requiring high-flow nasal oxygen: a retrospective bicentric cohort with propensity score analysis.

BACKGROUND: SARS-CoV-2 pneumonia is responsible for unprecedented numbers of acute respiratory failure requiring invasive mechanical ventilation (IMV). This work aimed to assess whether adding face-mask noninvasive ventilation (NIV) to high-flow nasal oxygen (HFNO) was associated with a reduced need for endotracheal intubation. METHODS: This retrospective cohort study was conducted from July 2020 to January 2021 in two tertiary care intensive care units (ICUs) in Paris, France. Patients admitted for laboratory confirmed SARS-CoV-2 infection with acute hypoxemic respiratory failure requiring HFNO with or without NIV were included. The primary outcome was the rate of endotracheal intubation. Secondary outcomes included day-28 mortality, day-28 respiratory support and IMV free days, ICU and hospital length-of-stay. Sensitivity analyses with both propensity score matching and overlap weighting were used. RESULTS: One hundred twenty-eight patients were included, 88 (69%) received HFNO alone and 40 (31%) received additional NIV. Additional NIV was associated with a reduced rate of endotracheal intubation in multivariate analysis (53 [60%] vs. 15 [38%], HR=0.46 [95% CI: 0.23-0.95], P=0.04). Sensitivity analyses by propensity score matching (HR=0.45 [95% CI: 0.24-0.84], P=0.01) and overlap weighting (HR=0.52 [95% CI: 0.28-0.94], P=0.03) were consistent. Day-28 mortality was 25 (28%) in the HFNO group and 8 (20%) in the NIV group (HR=0.75 [95% CI: 0.15-3.82], P=0.72). NIV was associated with higher IMV free days (20 [0-28] vs. 28 [14-28], P=0.015). All sensitivity analyses were consistent regarding secondary outcomes. CONCLUSIONS: Need for endotracheal intubation was lower in critically-ill SARS-CoV-2 patients receiving face-mask noninvasive mechanical ventilation in addition to high-flow oxygen therapy.

Imbalance of von Willebrand factor and ADAMTS13 axis is rather a biomarker of strong inflammation and endothelial damage than a cause of thrombotic process in critically ill COVID-19 patients.

BACKGROUND: Critically ill patients with coronavirus disease 2019 (COVID-19) are prone to developing macrothrombosis and microthrombosis. COVID-19 has been reported to be rarely associated with thrombotic microangiopathies. A disintegrin and metalloprotease with thrombospondin type I repeats, member 13 (ADAMTS13) severe deficiency, the hallmark of thrombotic thrombocytopenic purpura (TTP), induces the formation of platelet, unusually large von Willebrand factor (VWF) multimer microthrombi. In immune-mediated TTP, ADAMTS13 adopts specifically an open conformation. The VWF/ADAMTS13 couple may contribute to the microthrombi formation in pulmonary alveolar capillaries in COVID-19. OBJECTIVE: To investigate clinical features, hemostatic laboratory parameters, VWF/ADAMTS13 axis, and ADAMTS13 conformation in critically ill COVID-19 patients at admission. METHODS: Fifty three critically ill COVID-19 patients were enrolled between March 18 and May 9 2020 in a monocentric hospital. RESULTS: The median age was 59 years and the male-to-female ratio was 2.8/1. We reported seven pulmonary embolisms and 15 deaths. Biological investigations showed increased fibrinogen and factor V levels, and strongly increased D-dimers correlated with mortality. No patient presented severe thrombocytopenia nor microangiopathic hemolytic anemia. An imbalance between high VWF antigen levels and normal or slightly decreased ADAMTS13 activity levels (strongly elevated VWF/ADAMTS13 ratio) was correlated with mortality. Three patients had a partial quantitative deficiency in ADAMTS13. We also reported a closed conformation of ADAMTS13 in all patients, reinforcing the specificity of an open conformation of ADAMTS13 as a hallmark of TTP. CONCLUSION: We suggest that slightly decreased or normal ADAMTS13 activity and highly elevated VWF are rather biomarkers reflecting both the strong inflammation and the endothelial damage rather than drivers of the thrombotic process of COVID-19.

Infectious events in patients with severe COVID-19: results of a cohort of patients with high prevalence of underlying immune defect.

BACKGROUND: Empirical antibiotic has been considered in severe COVID-19 although little data are available regarding concomitant infections. This study aims to assess the frequency of infections, community and hospital-acquired infections, and risk factors for infections and mortality during severe COVID-19. METHODS: Retrospective single-center study including consecutive patients admitted to the intensive care unit (ICU) for severe COVID-19. Competing-risk analyses were used to assess cumulative risk of infections. Time-dependent Cox and fine and gray models were used to assess risk factors for infections and mortality. Propensity score matching was performed to estimate the effect of dexamethasone. RESULTS: We included 100 patients including 34 patients with underlying malignancies or organ transplantation. First infectious event was bacterial for 35 patients, and fungal for one. Cumulative incidence of infectious events was 27% [18-35] at 10 ICU-days. Prevalence of community-acquired infections was 7% [2.8-13.9]. Incidence density of hospital-acquired infections was 125 [91-200] events per 1000 ICU-days. Risk factors independently associated with hospital-acquired infections included MV. Patient's severity and underlying malignancy were associated with mortality. Dexamethasone was associated with increased infections (36% [20-53] vs. 12% [4-20] cumulative incidence at day-10; p = 0.01). After matching, dexamethasone was associated with hospital-acquired infections (35% [18-52] vs. 13% [1-25] at 10 days, respectively, p = 0.03), except in the subset of patients requiring MV, and had no influence on mortality. CONCLUSIONS: In this population of COVID-19 patients with high prevalence of underlying immune defect, a high risk of infections was noted. MV and use of steroids were independently associated with infection rate.

Identification of Distinct Immunophenotypes in Critically Ill Coronavirus Disease 2019 Patients.

BACKGROUND: Severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) infection causes direct lung damage, overwhelming endothelial activation, and inflammatory reaction, leading to acute respiratory failure and multi-organ dysfunction. Ongoing clinical trials are evaluating targeted therapies to hinder this exaggerated inflammatory response. Critically ill coronavirus disease 2019 (COVID-19) patients have shown heterogeneous severity trajectories, suggesting that response to therapies is likely to vary across patients. RESEARCH QUESTION: Are critically ill COVID-19 patients biologically and immunologically dissociable based on profiling of currently evaluated therapeutic targets? STUDY DESIGN AND METHODS: We did a single-center, prospective study in an ICU department in France. Ninety-six critically ill adult patients admitted with a documented SARS-CoV-2 infection were enrolled. We conducted principal components analysis and hierarchical clustering on a vast array of immunologic variables measured on the day of ICU admission. RESULTS: We found that patients were distributed in three clusters bearing distinct immunologic features and associated with different ICU outcomes. Cluster 1 had a "humoral immunodeficiency" phenotype with predominant B-lymphocyte defect, relative hypogammaglobulinemia, and moderate inflammation. Cluster 2 had a "hyperinflammatory" phenotype, with high cytokine levels (IL-6, IL-1ß, IL-8, tumor necrosis factor-alpha [TNF⍺]) associated with CD4+ and CD8+ T-lymphocyte defects. Cluster 3 had a "complement-dependent" phenotype with terminal complement activation markers (elevated C3 and sC5b-9). INTERPRETATION: Patients with severe COVID-19 exhibiting cytokine release marks, complement activation, or B-lymphocyte defects are distinct from each other. Such immunologic variability argues in favor of targeting different mediators in different groups of patients and could serve as a basis for patient identification and clinical trial eligibility.

Elevated plasma IL-6 and CRP levels are associated with adverse clinical outcomes and death in critically ill SARS-CoV-2 patients: inflammatory response of SARS-CoV-2 patients.

BACKGROUND: SARS coronavirus 2 (SARS-CoV-2) is responsible for high morbidity and mortality worldwide, mostly due to the exacerbated inflammatory response observed in critically ill patients. However, little is known about the kinetics of the systemic immune response and its association with survival in SARS-CoV-2+ patients admitted in ICU. We aimed to compare the immuno-inflammatory features according to organ failure severity and in-ICU mortality. METHODS: Six-week multicentre study (N = 3) including SARS-CoV-2+ patients admitted in ICU. Analysis of plasma biomarkers at days 0 and 3-4 according to organ failure worsening (increase in SOFA score) and 60-day mortality. RESULTS: 101 patients were included. Patients had severe respiratory diseases with PaO2/FiO2 of 155 [111-251] mmHg), SAPS II of 37 [31-45] and SOFA score of 4 [3-7]. Eighty-three patients (83%) required endotracheal intubation/mechanical ventilation and among them, 64% were treated with prone position. IL-1ß was barely detectable. Baseline IL-6 levels positively correlated with organ failure severity. Baseline IL-6 and CRP levels were significantly higher in patients in the worsening group than in the non-worsening group (278 [70-622] vs. 71 [29-153] pg/mL, P < 0.01; and 178 [100-295] vs. 100 [37-213] mg/L, P < 0.05, respectively). Baseline IL-6 and CRP levels were significantly higher in non-survivors compared to survivors but fibrinogen levels and lymphocyte counts were not different between groups. After adjustment on SOFA score and time from symptom onset to first dosage, IL-6 and CRP remained significantly associated with mortality. IL-6 changes between Day 0 and Day 3-4 were not different according to the outcome. A contrario, kinetics of CRP and lymphocyte count were different between survivors and non-survivors. CONCLUSIONS: In SARS-CoV-2+ patients admitted in ICU, a systemic pro-inflammatory signature was associated with clinical worsening and 60-day mortality.

Les trois cultures de la psychiatrie computationnelle

Résumé Le champ de la psychiatrie computationnelle prend de l’ampleur depuis quelques années. Dans cet article, nous proposons de distinguer trois champs de la psychiatrie computationnelle. Le premier champ correspond à la Digital Psychiatry, ou psychiatrie utilisant les outils numériques, qui peut être définie comme un champ de la psychiatrie qui utilise des outils connectés pour recueillir des données numériques. Le deuxième champ correspond à la Big Psychiatry, ou psychiatrie fondée sur les données massives, qui traite de grandes quantités de données, en particulier par des méthodes d’apprentissage automatique, une des branches de l’intelligence artificielle. Le troisième champ correspond à la Psychiatry Modeling, ou modélisation en psychiatrie utilisant notamment les neurosciences computationnelles, avec le développement et l’utilisation de modèles formels (mathématiques) du fonctionnement (et dysfonctionnement) cérébral et cognitif, afin de caractériser les mécanismes à l’origine des symptômes observés en clinique. Ces trois champs se complètent et sont fortement dépendants les uns des autres. Ainsi, l’accès aux données est fourni par la Digital Psychiatry ;le traitement des données est opéré par la Big Psychiatry ;et la formalisation des hypothèses est offerte par la Psychiatry Modeling. Cette triple organisation de la psychiatrie computationnelle offre un cadre de réflexion robuste pour appréhender la psychiatrie personnalisée et de précision, articulée autour de méthodologies statistiques et mathématiques, axée sur la prédiction et l’explication et utilisant des données qualitativement et quantitativement variées – tout en s’adressant nécessairement à un sujet commun, le patient de la clinique psychiatrique. Introduction Whether on the social, economic or scientific level, the digital sciences tend to change the conception of health. Computational Psychiatry, in the sense of a psychiatry based on “numbers” and information flow, has evolved rapidly. Methods In this article, we propose the distinction between three fields of Computational Psychiatry. A first field corresponds to “Digital Psychiatry”, i.e. a field using digital, connected, tools in the main goal to collect digital data (especially important in this period of COVID-19). A second field corresponds to “Big Psychiatry”, or Big Data Psychiatry, which deals with large amounts of data, e.g. through recent methodologies in Machine learning or artificial intelligence. A third field corresponds to “Psychiatry Modeling”, which corresponds to the utilization of formal hypothesis (i.e. mathematical models) about brain and behavior (and their dysfunctions) in line with computational neurosciences. Results The collection of digital data fits into methodologies of assessments and interventions in daily life, named Ecological Momentary Assessment. Of course, these digital data, which differ quantitatively and qualitatively from what psychiatry has been able to collect in its history, raise numerous epistemological and ethical questions. In the field of Big Psychiatry, most Machine learning techniques provide predictions rather than pathophysiological mechanisms, and these Machine learning techniques makes it possible to propose new delineations of disorders in a logic of stratified medicine. Lastly, resulting from studies in computational neurosciences, explanatory modeling of the brain (often called “Generative modeling”) proposes a number of theories to understand the functioning of the brain in psychiatric disorder (e.g. predictive coding, reinforcement learning, decision making theories, but also dynamical systems theories and graph and network theory). Discussion and conclusion This field could offer a framework to characterize the origin of the psychiatric symptoms. Obviously, these three fields are highly mutually dependent, with for instance a data access provided by Digital Psychiatry (with Digital Tools), a data processing operated by Big Psychiatry (with Machine learning) and a formalization of hypotheses offered by Generative modeling of the brain from Psychiatry Modeling. This triple organization of Computational Psychiatry offers a robust framework for personalized and precision psychiatry, articulated around statistical and mathematical methodologies, focused on prediction and explanation and using qualitatively and quantitatively varied data. However, such a framework is necessarily geared to a common subject: the patient of the psychiatric clinic.

Acute kidney injury in critically ill patients with COVID-19.

Acute kidney injury (AKI) has been reported in up to 25% of critically-ill patients with SARS-CoV-2 infection, especially in those with underlying comorbidities. AKI is associated with high mortality rates in this setting, especially when renal replacement therapy is required. Several studies have highlighted changes in urinary sediment, including proteinuria and hematuria, and evidence of urinary SARS-CoV-2 excretion, suggesting the presence of a renal reservoir for the virus. The pathophysiology of COVID-19 associated AKI could be related to unspecific mechanisms but also to COVID-specific mechanisms such as direct cellular injury resulting from viral entry through the receptor (ACE2) which is highly expressed in the kidney, an imbalanced renin-angotensin-aldosteron system, pro-inflammatory cytokines elicited by the viral infection and thrombotic events. Non-specific mechanisms include haemodynamic alterations, right heart failure, high levels of PEEP in patients requiring mechanical ventilation, hypovolemia, administration of nephrotoxic drugs and nosocomial sepsis. To date, there is no specific treatment for COVID-19 induced AKI. A number of investigational agents are being explored for antiviral/immunomodulatory treatment of COVID-19 and their impact on AKI is still unknown. Indications, timing and modalities of renal replacement therapy currently rely on non-specific data focusing on patients with sepsis. Further studies focusing on AKI in COVID-19 patients are urgently warranted in order to predict the risk of AKI, to identify the exact mechanisms of renal injury and to suggest targeted interventions.