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      Association of Intensive Care Unit Patient Load and Demand With Mortality Rates in US Department of Veterans Affairs Hospitals During the COVID-19 Pandemic

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          Abstract

          This cohort study examines the association of patient caseload and demand with mortality among patients with coronavirus disease 2019 (COVID-19) in US Veterans Affairs (VA) intensive care units.

          Key Points

          Question

          Is greater coronavirus disease 2019 (COVID-19) intensive care unit (ICU) strain associated with increased COVID-19 mortality?

          Findings

          In this cohort study of 8516 patients with COVID-19 admitted to 88 US Veterans Affairs hospitals, strains on critical care capacity were associated with increased COVID-19 mortality. Among patients with COVID-19, those treated in the ICU during periods of peak COVID-19 ICU demand had a nearly 2-fold increased risk of mortality compared with those treated during periods of low demand.

          Meaning

          These findings suggest that public health officials and hospital administrators should consider interventions that reduce COVID-19 ICU demand to improve survival among patients with COVID-19 in the ICU.

          Abstract

          Importance

          Although strain on hospital capacity has been associated with increased mortality in nonpandemic settings, studies are needed to examine the association between coronavirus disease 2019 (COVID-19) critical care capacity and mortality.

          Objective

          To examine whether COVID-19 mortality was associated with COVID-19 intensive care unit (ICU) strain.

          Design, Setting, and Participants

          This cohort study was conducted among veterans with COVID-19, as confirmed by polymerase chain reaction or antigen testing in the laboratory from March through August 2020, cared for at any Department of Veterans Affairs (VA) hospital with 10 or more patients with COVID-19 in the ICU. The follow-up period was through November 2020. Data were analyzed from March to November 2020.

          Exposures

          Receiving treatment for COVID-19 in the ICU during a period of increased COVID-19 ICU load, with load defined as mean number of patients with COVID-19 in the ICU during the patient’s hospital stay divided by the number of ICU beds at that facility, or increased COVID-19 ICU demand, with demand defined as mean number of patients with COVID-19 in the ICU during the patient’s stay divided by the maximum number of patients with COVID-19 in the ICU.

          Main Outcomes and Measures

          All-cause mortality was recorded through 30 days after discharge from the hospital.

          Results

          Among 8516 patients with COVID-19 admitted to 88 VA hospitals, 8014 (94.1%) were men and mean (SD) age was 67.9 (14.2) years. Mortality varied over time, with 218 of 954 patients (22.9%) dying in March, 399 of 1594 patients (25.0%) dying in April, 143 of 920 patients (15.5%) dying in May, 179 of 1314 patients (13.6%) dying in June, 297 of 2373 patients (12.5%) dying in July, and 174 of 1361 (12.8%) patients dying in August ( P < .001). Patients with COVID-19 who were treated in the ICU during periods of increased COVID-19 ICU demand had increased risk of mortality compared with patients treated during periods of low COVID-19 ICU demand (ie, demand of ≤25%); the adjusted hazard ratio for all-cause mortality was 0.99 (95% CI, 0.81-1.22; P = .93) for patients treated when COVID-19 ICU demand was more than 25% to 50%, 1.19 (95% CI, 0.95-1.48; P = .13) when COVID-19 ICU demand was more than 50% to 75%, and 1.94 (95% CI, 1.46-2.59; P < .001) when COVID-19 ICU demand was more than 75% to 100%. No association between COVID-19 ICU demand and mortality was observed for patients with COVID-19 not in the ICU. The association between COVID-19 ICU load and mortality was not consistent over time (ie, early vs late in the pandemic).

          Conclusions and Relevance

          This cohort study found that although facilities augmented ICU capacity during the pandemic, strains on critical care capacity were associated with increased COVID-19 ICU mortality. Tracking COVID-19 ICU demand may be useful to hospital administrators and health officials as they coordinate COVID-19 admissions across hospitals to optimize outcomes for patients with this illness.

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          Most cited references15

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          Clinical course and outcomes of critically ill patients with SARS-CoV-2 pneumonia in Wuhan, China: a single-centered, retrospective, observational study

          Summary Background An ongoing outbreak of pneumonia associated with the severe acute respiratory coronavirus 2 (SARS-CoV-2) started in December, 2019, in Wuhan, China. Information about critically ill patients with SARS-CoV-2 infection is scarce. We aimed to describe the clinical course and outcomes of critically ill patients with SARS-CoV-2 pneumonia. Methods In this single-centered, retrospective, observational study, we enrolled 52 critically ill adult patients with SARS-CoV-2 pneumonia who were admitted to the intensive care unit (ICU) of Wuhan Jin Yin-tan hospital (Wuhan, China) between late December, 2019, and Jan 26, 2020. Demographic data, symptoms, laboratory values, comorbidities, treatments, and clinical outcomes were all collected. Data were compared between survivors and non-survivors. The primary outcome was 28-day mortality, as of Feb 9, 2020. Secondary outcomes included incidence of SARS-CoV-2-related acute respiratory distress syndrome (ARDS) and the proportion of patients requiring mechanical ventilation. Findings Of 710 patients with SARS-CoV-2 pneumonia, 52 critically ill adult patients were included. The mean age of the 52 patients was 59·7 (SD 13·3) years, 35 (67%) were men, 21 (40%) had chronic illness, 51 (98%) had fever. 32 (61·5%) patients had died at 28 days, and the median duration from admission to the intensive care unit (ICU) to death was 7 (IQR 3–11) days for non-survivors. Compared with survivors, non-survivors were older (64·6 years [11·2] vs 51·9 years [12·9]), more likely to develop ARDS (26 [81%] patients vs 9 [45%] patients), and more likely to receive mechanical ventilation (30 [94%] patients vs 7 [35%] patients), either invasively or non-invasively. Most patients had organ function damage, including 35 (67%) with ARDS, 15 (29%) with acute kidney injury, 12 (23%) with cardiac injury, 15 (29%) with liver dysfunction, and one (2%) with pneumothorax. 37 (71%) patients required mechanical ventilation. Hospital-acquired infection occurred in seven (13·5%) patients. Interpretation The mortality of critically ill patients with SARS-CoV-2 pneumonia is considerable. The survival time of the non-survivors is likely to be within 1–2 weeks after ICU admission. Older patients (>65 years) with comorbidities and ARDS are at increased risk of death. The severity of SARS-CoV-2 pneumonia poses great strain on critical care resources in hospitals, especially if they are not adequately staffed or resourced. Funding None.
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            Dexamethasone in Hospitalized Patients with Covid-19 — Preliminary Report

            (2020)
            Abstract Background Coronavirus disease 2019 (Covid-19) is associated with diffuse lung damage. Glucocorticoids may modulate inflammation-mediated lung injury and thereby reduce progression to respiratory failure and death. Methods In this controlled, open-label trial comparing a range of possible treatments in patients who were hospitalized with Covid-19, we randomly assigned patients to receive oral or intravenous dexamethasone (at a dose of 6 mg once daily) for up to 10 days or to receive usual care alone. The primary outcome was 28-day mortality. Here, we report the preliminary results of this comparison. Results A total of 2104 patients were assigned to receive dexamethasone and 4321 to receive usual care. Overall, 482 patients (22.9%) in the dexamethasone group and 1110 patients (25.7%) in the usual care group died within 28 days after randomization (age-adjusted rate ratio, 0.83; 95% confidence interval [CI], 0.75 to 0.93; P<0.001). The proportional and absolute between-group differences in mortality varied considerably according to the level of respiratory support that the patients were receiving at the time of randomization. In the dexamethasone group, the incidence of death was lower than that in the usual care group among patients receiving invasive mechanical ventilation (29.3% vs. 41.4%; rate ratio, 0.64; 95% CI, 0.51 to 0.81) and among those receiving oxygen without invasive mechanical ventilation (23.3% vs. 26.2%; rate ratio, 0.82; 95% CI, 0.72 to 0.94) but not among those who were receiving no respiratory support at randomization (17.8% vs. 14.0%; rate ratio, 1.19; 95% CI, 0.91 to 1.55). Conclusions In patients hospitalized with Covid-19, the use of dexamethasone resulted in lower 28-day mortality among those who were receiving either invasive mechanical ventilation or oxygen alone at randomization but not among those receiving no respiratory support. (Funded by the Medical Research Council and National Institute for Health Research and others; RECOVERY ClinicalTrials.gov number, NCT04381936; ISRCTN number, 50189673.)
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              Association of Public Health Interventions With the Epidemiology of the COVID-19 Outbreak in Wuhan, China

              Was there an association of public health interventions with improved control of the COVID-19 outbreak in Wuhan, China? In this cohort study that included 32 583 patients with laboratory-confirmed COVID-19 in Wuhan from December 8, 2019, through March 8, 2020, the institution of interventions including cordons sanitaire , traffic restriction, social distancing, home quarantine, centralized quarantine, and universal symptom survey was temporally associated with reduced effective reproduction number of SARS-CoV-2 (secondary transmission) and the number of confirmed cases per day across age groups, sex, and geographic regions. A series of multifaceted public health interventions was temporally associated with improved control of the COVID-19 outbreak in Wuhan and may inform public health policy in other countries and regions. Coronavirus disease 2019 (COVID-19) has become a pandemic, and it is unknown whether a combination of public health interventions can improve control of the outbreak. To evaluate the association of public health interventions with the epidemiological features of the COVID-19 outbreak in Wuhan by 5 periods according to key events and interventions. In this cohort study, individual-level data on 32 583 laboratory-confirmed COVID-19 cases reported between December 8, 2019, and March 8, 2020, were extracted from the municipal Notifiable Disease Report System, including patients’ age, sex, residential location, occupation, and severity classification. Nonpharmaceutical public health interventions including cordons sanitaire , traffic restriction, social distancing, home confinement, centralized quarantine, and universal symptom survey. Rates of laboratory-confirmed COVID-19 infections (defined as the number of cases per day per million people), across age, sex, and geographic locations were calculated across 5 periods: December 8 to January 9 (no intervention), January 10 to 22 (massive human movement due to the Chinese New Year holiday), January 23 to February 1 ( cordons sanitaire , traffic restriction and home quarantine), February 2 to 16 (centralized quarantine and treatment), and February 17 to March 8 (universal symptom survey). The effective reproduction number of SARS-CoV-2 (an indicator of secondary transmission) was also calculated over the periods. Among 32 583 laboratory-confirmed COVID-19 cases, the median patient age was 56.7 years (range, 0-103; interquartile range, 43.4-66.8) and 16 817 (51.6%) were women. The daily confirmed case rate peaked in the third period and declined afterward across geographic regions and sex and age groups, except for children and adolescents, whose rate of confirmed cases continued to increase. The daily confirmed case rate over the whole period in local health care workers (130.5 per million people [95% CI, 123.9-137.2]) was higher than that in the general population (41.5 per million people [95% CI, 41.0-41.9]). The proportion of severe and critical cases decreased from 53.1% to 10.3% over the 5 periods. The severity risk increased with age: compared with those aged 20 to 39 years (proportion of severe and critical cases, 12.1%), elderly people (≥80 years) had a higher risk of having severe or critical disease (proportion, 41.3%; risk ratio, 3.61 [95% CI, 3.31-3.95]) while younger people (<20 years) had a lower risk (proportion, 4.1%; risk ratio, 0.47 [95% CI, 0.31-0.70]). The effective reproduction number fluctuated above 3.0 before January 26, decreased to below 1.0 after February 6, and decreased further to less than 0.3 after March 1. A series of multifaceted public health interventions was temporally associated with improved control of the COVID-19 outbreak in Wuhan, China. These findings may inform public health policy in other countries and regions. This population epidemiology study examines associations between phases of nonpharmaceutical public health interventions (social distancing, centralized quarantine, home confinement, and others) and rates of laboratory-confirmed COVID-19 infection in Wuhan, China, between December 2019 and early March 2020.
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                Author and article information

                Journal
                JAMA Netw Open
                JAMA Netw Open
                JAMA Netw Open
                JAMA Network Open
                American Medical Association
                2574-3805
                19 January 2021
                January 2021
                19 January 2021
                : 4
                : 1
                Affiliations
                [1 ]Precision Monitoring to Transform Care Quality Enhancement Research Initiative, Health Services Research and Development, Department of Veterans Affairs, Indianapolis, Indiana
                [2 ]Health Services Research and Development Center for Health Information and Communication, Richard L. Roudebush VA Medical Center, Department of Veterans Affairs, Indianapolis, Indiana
                [3 ]Medicine Service, Richard L. Roudebush VA Medical Center, Indianapolis, Indiana
                [4 ]Department of Medicine, Indiana University School of Medicine, Indianapolis
                [5 ]Department of Neurology, Indiana University School of Medicine, Indianapolis
                [6 ]William M. Tierney Center for Health Services Research, Regenstrief Institute, Indianapolis, Indiana
                [7 ]Department of Biostatistics, Indiana University School of Medicine, Indianapolis
                [8 ]School of Nursing, Purdue University, West Lafayette, Indiana
                [9 ]Department of Biostatistics, College of Public Health, University of Nebraska Medical Center, Omaha
                [10 ]Department of Pharmacy Practice, College of Pharmacy, Purdue University, West Lafayette, Indiana
                [11 ]Northern California Institute for Research and Education, San Francisco
                [12 ]San Francisco VA Medical Center, San Francisco, California
                [13 ]Division of General Internal Medicine, Department of Medicine, University of California, San Francisco
                Author notes
                Article Information
                Accepted for Publication: November 28, 2020.
                Published: January 19, 2021. doi:10.1001/jamanetworkopen.2020.34266
                Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2021 Bravata DM et al. JAMA Network Open.
                Corresponding Author: Dawn M. Bravata, MD, Health Services Research and Development Center for Health Information and Communication, Richard L. Roudebush VA Medical Center, 1481 W 10th St, HSR&D Mail Code 11H, Indianapolis, IN 46202 ( Dawn.Bravata2@ 123456va.gov ).
                Author Contributions: Dr Bravata and Mr Perkins had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.
                Concept and design: Bravata, Arling, Zhang, Zillich, Keyhani.
                Acquisition, analysis, or interpretation of data: Bravata, Perkins, L. Myers, Arling, Zhang, Zillich, Reese, Dysangco, Agarwal, J. Myers, Austin, Sexson, Leonard, Dev.
                Drafting of the manuscript: Bravata, Arling.
                Critical revision of the manuscript for important intellectual content: Bravata, Perkins, L. Myers, Zhang, Zillich, Reese, Dysangco, Agarwal, J. Myers, Austin, Sexson, Leonard, Dev, Keyhani.
                Statistical analysis: Bravata, Perkins, Arling, Zhang.
                Obtained funding: Bravata.
                Administrative, technical, or material support: L. Myers, Reese, J. Myers, Austin, Sexson, Keyhani.
                Supervision: Bravata, Zhang.
                Conflict of Interest Disclosures: Drs Bravata and Perkins reported receiving grants from the Department of Veterans Affairs during the conduct of the study. Dr Agarwal reported receiving personal fees and travel support from Bayer, Relypsa, Reata, Sanofi, Boehringer, and Merck; personal fees from Janssen, DiaMedica, Lexicon, Akebia, Eli Lilly, and Astra Zeneca; and travel support from Akebia outside the submitted work. No other disclosures were reported.
                Funding/Support: This work was supported by the US Department of Veterans Affairs Health Services Research and Development Service Precision Monitoring to Transform Care Quality Enhancement Research Initiative (QUE 15-280).
                Role of the Funder/Sponsor: The funder had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
                Disclaimer: The contents of this study do not represent the views of the US Department of Veterans Affairs or the US government.
                Article
                zoi201043
                10.1001/jamanetworkopen.2020.34266
                7816100
                33464319
                a90c4621-aa23-4dc2-8900-4ca200ed2041
                Copyright 2021 Bravata DM et al. JAMA Network Open.

                This is an open access article distributed under the terms of the CC-BY License.

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