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Background Despite numerous advances in the understanding of the pathophysiology, progression, and management of acute respiratory failure (ARF) and acute respiratory distress syndrome (ARDS), there is limited contemporary data on the mortality burden of ARF and ARDS in the United States (US). Research Question What are the contemporary trends and geographic variation in ARF and ARDS-related mortality in the US? Study Design and Methods A retrospective analysis of the National Center for Health ”Statistics’ nationwide mortality data was conducted to assess the ARF and ARDS-related mortality trends from 2014-2018 and the geographic distribution of ARF and ARDS-related deaths in 2018 for all American residents. Piecewise linear regression was used to evaluate the trends in age-adjusted mortality rates (AAMR) in the overall population and various demographic subgroups of age, sex, race, urbanization, and region. Results Among 1,434,349 ARF-related deaths and 52,958 ARDS-related deaths during the study period, the AAMR was highest in older individuals (≥65 years), non-Hispanic Blacks, those living in the non-metropolitan region. The AAMR for ARF-related deaths (per 100,000 persons) increased from 74.9 (95%CI:74.6-75.2) in 2014 to 85.6 (95%CI:85.3-85.9) in 2018 (Annual Percentage Change [APC]: 3.4 [95%CI:2.2-4.6];Ptrend=0.003). The AAMR (per 100,000) for ARDS-related deaths was 3.2 (95%CI:3.2-3.3) in 2014 and 3.0 (95%CI:3.0-3.1) (APC: -0.9 [95%CI:-5.4-3.8]; Ptrend=0.56). The observed increase in rates for ARF mortality was consistent across the subgroups of age, sex, race/ethnicity, urbanization status, and geographical region (Ptrend<0.05 for all). The AAMR (per 100,000 persons) for ARF (91.3 [95%CI: 90.8-91.8]) and ARDS-related mortality (3.3 [95%CI:32.34]) in 2018 were highest in the South. Interpretation The ARF-related mortality increased at ∼3.4% annually, and ARDS-related mortality showed a lack of decline in the last five years. These data contextualize important health information to guide priorities for research, clinical care, and policy, especially during the coronavirus disease-19 pandemic in the US.

Background In December 2019, a novel coronavirus-associated pneumonia, now known as coronavirus disease 2019 (COVID-19), was first detected in Wuhan, China. To prevent the rapid spread of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and treat patients with mild symptoms, sports stadiums and convention centers were reconstructed into mobile hospitals. Research Question It is unknown whether a mobile cabin hospital can provide a safe treatment site for patients with mild COVID-19 symptoms. Study Design and Methods This study retrospectively reviewed the medical records of 421 patients with COVID-19 admitted to a mobile cabin hospital in Wuhan from February 9, 2020, to March 5, 2020. Clinical data comprised patient age, sex, clinical presentation, chest imaging, nucleic acid testing, length of hospitalization, and outcomes. Results Of the patients who were discharged from the cabin hospital, 362 (86.0%) were categorized as recovered; 14.0% developed severe symptoms and were transferred to a designated hospital. The most common presenting symptoms were fever (60.6%) and cough (52.0%); 5.2% exhibited no obvious symptoms. High fever (> 39.0°C) was more common in severe cases than in recovered cases (18.6% vs 6.6%). The distribution of lung lesions was peripheral in 85.0% of patients, multifocal in 69.4%, and bilateral in 68.2%. The most common pattern was ground-glass opacity (67.7%), followed by patchy shadowing (49.2%). The incidence of patchy shadowing was higher in patients with severe disease (66.1%) than in those who recovered (31.8%, P Interpretation Mobile cabin hospitals provide a safe treatment site for patients with mild COVID-19 symptoms and offer an effective isolation area to prevent the spread of severe acute respiratory syndrome coronavirus 2.

Before coronavirus disease 2019 (COVID-19), telehealth evaluation and management (E/M) services were not widely used in the United States and often were restricted to rural areas or locations with poor access to care. Most Medicare beneficiaries could not receive telehealth services in their homes. In response to the COVID-19 pandemic, Medicare, Medicaid, and commercial insurers relaxed restrictions on both coverage and reimbursement of telehealth services. These changes, together with the need for social distancing, transformed the delivery of outpatient E/M services through an increase in telehealth use. In some cases, the transition from in-person outpatient care to telehealth occurred overnight. Billing and claim submission for telehealth services is complicated; has changed over the course of the pandemic; and varies with each insurance carrier, making telehealth adoption burdensome. Despite these challenges, telehealth is beneficial for health-care providers and patients. Without additional legislation at the federal and state levels, it is likely that telehealth use will continue to decline after the COVID-19 public health emergency.

International audience; Abstract Study Objectives The COVID-19 pandemic has had dramatic effects on society and people’s daily habits. In this observational study, we recorded objective data on sleep macro- and microarchitecture repeatedly over several nights before and during the COVID-19 government-imposed lockdown. The main objective was to evaluate changes in patterns of sleep duration and architecture during home confinement using the pre-confinement period as a control. Methods Participants were regular users of a sleep-monitoring headband that records, stores, and automatically analyzes physiological data in real time, equivalent to polysomnography. We measured sleep onset duration, total sleep time, duration of sleep stages (N2, N3, and rapid eye movement [REM]), and sleep continuity. Via the user’s smartphone application, participants filled in questionnaires on how lockdown changed working hours, eating behavior, and daily life at home. They also filled in the Insomnia Severity Index, reduced Morningness–Eveningness Questionnaire, and Hospital Anxiety and Depression Scale questionnaires, allowing us to create selected subgroups. Results The 599 participants were mainly men (71%) of median age 47 (interquartile range: 36–59). Compared to before lockdown, during lockdown individuals slept more overall (mean +3·83 min; SD: ±1.3), had less deep sleep (N3), more light sleep (N2), and longer REM sleep (mean +3·74 min; SD: ±0.8). They exhibited less weekend-specific changes, suggesting less sleep restriction during the week. Changes were most pronounced in individuals reporting eveningness preferences, suggesting relative sleep deprivation in this population and exacerbated sensitivity to societal changes. Conclusion This unique dataset should help us understand the effects of lockdown on sleep architecture and on our health.

International audience; No abstract available

BACKGROUND: The COVID-19 pandemic has led to unprecedented demand for ICUs, with the need to triage admissions along with the development of ICU triage criteria. However, how these criteria relate to outcomes in patients already admitted to the ICU is unknown, as is the incremental ICU capacity that triage of these patients might create given existing admission practices. RESEARCH QUESTION: What is the short- and long-term survival of low- vs high-priority patients for ICU admission according to current pandemic triage criteria? STUDY DESIGN AND METHODS: This study analyzed prospectively collected registry data (2007-2018) in 23 ICUs in Victoria, Australia, with probabilistic linkage with death registries. After excluding elective surgery, admissions were stratified according to existing ICU triage protocol prioritization as low (age ≥ 85 years, or severe chronic illness, or Sequential Organ Failure Assessment [SOFA] score = 0 or ≥ 12), medium (SOFA score = 8-11) or high (SOFA score = 1-7) priority. The primary outcome was long-term survival. Secondary outcomes were in-hospital mortality, ICU length of stay (LOS) and bed-day usage. RESULTS: This study examined 126,687 ICU admissions. After 5 years of follow-up, 1,093 of 3,296 (33%; 95% CI, 32-34) of "low-priority" patients aged ≥ 85 years or with severe chronic illness and 86 of 332 (26%; 95% CI, 24-28) with a SOFA score ≥ 12 were still alive. Sixty-three of 290 (22%; 95% CI, 17-27) of patients in these groups followed up for 10 years were still alive. Together, low-priority patients accounted for 27% of all ICU bed-days and had lower in-hospital mortality (22%) than the high-priority patients (28%). Among nonsurvivors, low-priority admissions had shorter ICU LOS than medium- or high-priority admissions. INTERPRETATION: Current SOFA score or age or severe comorbidity-based ICU pandemic triage protocols exclude patients with a close to 80% hospital survival, a > 30% five-year survival, and 27% of ICU bed-day use. These findings imply the need for stronger evidence-based ICU triage protocols.

The coronavirus disease 2019 pandemic will be remembered for the rapidity with which it spread, the morbidity and mortality associated with it, and the paucity of evidence-based management guidelines. One of the major concerns of hospitals was to limit spread of infection to health-care workers. Because the virus is spread mainly by respiratory droplets and aerosolized particles, procedures that may potentially disperse viral particles, the so-called "aerosol-generating procedures" were avoided whenever possible. Included in this category were noninvasive ventilation (NIV), high-flow nasal cannula (HFNC), and awake (nonintubated) proning. Accordingly, at many health-care facilities, patients who had increasing oxygen requirements were emergently intubated and mechanically ventilated to avoid exposure to aerosol-generating procedures. With experience, physicians realized that mortality of invasively ventilated patients was high and it was not easy to extubate many of these patients. This raised the concern that HFNC and NIV were being underutilized to avoid intubation and to facilitate extubation. In this article, we attempt to separate fact from fiction and perception from reality pertaining to the aerosol dispersion with NIV, HFNC, and awake proning. We describe precautions that hospitals and health-care providers must take to mitigate risks with these devices. Finally, we take a practical approach in describing how we use the three techniques, including the common indications, contraindications, and practical aspects of application.

Dyspnea is an uncomfortable sensation with the potential to cause psychological trauma. Patients presenting with acute respiratory failure, particularly when tidal volume is restricted during mechanical ventilation, may experience the most distressing form of dyspnea known as air hunger. Air hunger activates brain pathways known to be involved in posttraumatic stress disorder (PTSD), anxiety, and depression. These conditions are considered part of the post-intensive care syndrome. These sequelae may be even more prevalent among patients with ARDS. Low tidal volume, a mainstay of modern therapy for ARDS, is difficult to avoid and is likely to cause air hunger despite sedation. Adjunctive neuromuscular blockade does not prevent or relieve air hunger, but it does prevent the patient from communicating discomfort to caregivers. Consequently, paralysis may also contribute to the development of PTSD. Although research has identified post-ARDS PTSD as a cause for concern, and investigators have taken steps to quantify the burden of disease, there is little information to guide mechanical ventilation strategies designed to reduce its occurrence. We suggest such efforts will be more successful if they are directed at the known mechanisms of air hunger. Investigation of the antidyspnea effects of sedative and analgesic drugs commonly used in the ICU and their impact on post-ARDS PTSD symptoms is a logical next step. Although in practice we often accept negative consequences of life-saving therapies as unavoidable, we must understand the negative sequelae of our therapies and work to minimize them under our primary directive to "first, do no harm" to patients.

Background Working in the ICU during the first COVID-19 wave was associated with high levels of mental health disorders. Research Question What are the mental health symptoms in health care providers (HCPs) facing the second wave? Study Design and Methods A cross-sectional study (October 30-December 1, 2020) was conducted in 16 ICUs during the second wave in France. HCPs completed the Hospital Anxiety and Depression Scale, the Impact of Event Scale-Revised (for post-traumatic stress disorder), and the Maslach Burnout Inventory. Results Of 1,203 HCPs, 845 responded (70%) (66% nursing staff, 32% medical staff, 2% other professionals); 487 (57.6%) had treated more than 10 new patients with COVID-19 in the previous week. Insomnia affected 320 (37.9%), and 7.7% were taking a psychotropic drug daily. Symptoms of anxiety, depression, post-traumatic stress disorder, and burnout were reported in 60.0% (95% CI, 56.6%-63.3%), 36.1% (95% CI, 32.9%-39.5%), 28.4% (95% CI, 25.4%-31.6%), and 45.1% (95% CI, 41.7%-48.5%) of respondents, respectively. Independent predictors of such symptoms included respondent characteristics (sex, profession, experience, personality traits), work organization (ability to rest and to care for family), and self-perceptions (fear of becoming infected or of infecting family and friends, feeling pressure related to the surge, intention to leave the ICU, lassitude, working conditions, feeling they had a high-risk profession, and “missing the clapping”). The number of patients with COVID-19 treated in the first wave or over the last week was not associated with symptoms of mental health disorders. Interpretation The prevalence of symptoms of mental health disorders is high in ICU HCPs managing the second COVID-19 surge. The highest tiers of hospital management urgently need to provide psychological support, peer-support groups, and a communication structure that ensure the well-being of HCPs.

BACKGROUND: Since coronavirus disease 2019 (COVID-19) was identified, its clinical and biological heterogeneity has been recognized. Identifying COVID-19 phenotypes might help guide basic, clinical, and translational research efforts. RESEARCH QUESTION: Does the clinical spectrum of patients with COVID-19 contain distinct phenotypes and subphenotypes? STUDY DESIGN AND METHODS: We included adult patients (≥ 18 years) positive for laboratory-confirmed severe acute respiratory syndrome-coronavirus 2 infection from a prospective COVID-19 registry database in the Cleveland Clinic Health System in Ohio and Florida. The patients were split into training and testing sets. Using latent class analysis (LCA), we first identified phenotypic clusters of patients with COVID-19 based on demographics, comorbidities, and presenting symptoms. We then identified subphenotypes of hospitalized patients with additional blood biomarker data measured on hospital admission. The associations of phenotypes/subphenotypes and clinical outcomes were investigated. Multivariable prediction models were established to predict assignment to the LCA-defined phenotypes and subphenotypes and then evaluated on an independent testing set. RESULTS: We analyzed data for 20,572 patients. Seven phenotypes were identified on the basis of different profiles of presenting COVID-19 symptoms and existing comorbidities, including the following groups: young, no symptoms; young, symptoms; middle-aged, no symptoms; middle-aged, symptoms; middle-aged, comorbidities; old, no symptoms; and old, symptoms. The rates of inpatient hospitalization for the phenotypes were significantly different (P < .001). Five subphenotypes were identified for the subgroup of hospitalized patients, including the following subgroups: young, elevated WBC and platelet counts; middle-aged, lymphopenic with elevated C-reactive protein; middle-aged, hyperinflammatory; old, leukopenic with comorbidities; and old, hyperinflammatory with kidney dysfunction. The hospital mortality and the times from hospitalization to ICU transfer or death were significantly different (P < .001). The models for predicting the LCA-defined phenotypes and subphenotypes showed high discrimination (concordance index, 0.92 and 0.91). INTERPRETATION: Hypothesis-free LCA-defined phenotypes and subphenotypes of patients with COVID-19 can be identified. These may help clinical investigators conduct stratified analyses in clinical trials and assist basic science researchers in characterizing the pathobiology of the spectrum of COVID-19 presentations.