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Taylor YJ, Kowalkowski M, Palakshappa J. Social Disparities and Critical Illness during the Coronavirus Disease 2019 Pandemic: A Narrative Review. Crit Care Clin 2024; 40:805-825. [PMID: 39218487 DOI: 10.1016/j.ccc.2024.05.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/04/2024]
Abstract
The coronavirus disease 2019 (COVID-19) pandemic raised new considerations for social disparities in critical illness including hospital capacity and access to personal protective equipment, access to evolving therapies, vaccinations, virtual care, and restrictions on family visitation. This narrative review aims to explore evidence about racial/ethnic and socioeconomic differences in critical illness during the COVID-19 pandemic, factors driving those differences and promising solutions for mitigating inequities in the future. We apply a patient journey framework to identify social disparities at various stages before, during, and after patient interactions with critical care services and discuss recommendations for policy and practice.
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Affiliation(s)
- Yhenneko J Taylor
- Center for Health System Sciences, Atrium Health, 1300 Scott Avenue, Charlotte, NC 28204, USA.
| | - Marc Kowalkowski
- Department of Internal Medicine, Center for Health System Sciences, Wake Forest University School of Medicine, 1300 Scott Avenue, Charlotte, NC 28204, USA
| | - Jessica Palakshappa
- Department of Internal Medicine, Wake Forest University School of Medicine, 2 Watlington Hall, 1 Medical Center Boulevard, Winston-Salem, NC 27157, USA
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Zhao H, Yan X, Guo Z, Li K, Wang Z, Wang J, Lv D, Zhu J, Chen Y. Comparison of outcomes and characteristics of patients admitted to the ICU with COVID-19 and other community-acquired pneumonia based on propensity score matching. BMC Infect Dis 2024; 24:419. [PMID: 38644489 PMCID: PMC11034039 DOI: 10.1186/s12879-024-09306-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Accepted: 04/09/2024] [Indexed: 04/23/2024] Open
Abstract
OBJECTIVE To compare the similarities and differences between patients with Coronavirus Disease 2019 (COVID-19) and those with other community-acquired pneumonia (CAP) admitted to the intensive care unit (ICU), utilizing propensity score matching (PSM), regarding hospitalization expenses, treatment options, and prognostic outcomes, aiming to inform the diagnosis and treatment of COVID-19. METHODS Patients admitted to the ICU of the Third People's Hospital of Datong City, diagnosed with COVID-19 from December 2022 to February 2023, constituted the observation group, while those with other CAP admitted from January to November 2022 formed the control group. Basic information, clinical data at admission, and time from symptom onset to admission were matched using PSM. RESULTS A total of 70 patients were included in the COVID-19 group and 119 in the CAP group. The patients were matched by the propensity matching method, and 37 patients were included in each of the last two groups. After matching, COVID-19 had a higher failure rate than CAP, but the difference was not statistically significant (73% vs. 51%, p = 0.055). The utilization rate of antiviral drugs (40% vs. 11%, p = 0.003), γ-globulin (19% vs. 0%, p = 0.011) and prone position ventilation (PPV) (27% vs. 0%, p < 0.001) in patients with COVID-19 were higher than those in the CAP, and the differences were statistically significant. The total hospitalization cost of COVID-19 patients was lower than that of CAP patients, and the difference was statistically significant (27889.5 vs. 50175.9, p = 0.007). The hospital stay for COVID-19 patients was shorter than for CAP patients, but the difference was not statistically significant (10.9 vs. 16.6, p = 0.071). CONCLUSION Our findings suggest that limited medical resources influenced patient outcomes during the COVID-19 pandemic. Addressing substantial demands for ICU capacity and medications during this period could have potentially reduced the mortality rate among COVID-19 patients.
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Affiliation(s)
- Hongli Zhao
- Department of Critical Care Medicine, Datong Third People's Hospital, Datong, Shanxi, China
| | - Xiulin Yan
- Department of Critical Care Medicine, Datong Third People's Hospital, Datong, Shanxi, China.
| | - Ziru Guo
- Science and Education Section, Datong Third People's Hospital, Datong, Shanxi, China
| | - Kaiyu Li
- Department of Critical Care Medicine, Datong Third People's Hospital, Datong, Shanxi, China
| | - Zhaopeng Wang
- Department of Critical Care Medicine, Datong Third People's Hospital, Datong, Shanxi, China
| | - Jun Wang
- Department of Critical Care Medicine, Datong Third People's Hospital, Datong, Shanxi, China
| | - Dong Lv
- Department of Critical Care Medicine, Datong Third People's Hospital, Datong, Shanxi, China
| | - Jianling Zhu
- Department of Critical Care Medicine, Datong Third People's Hospital, Datong, Shanxi, China
| | - Ye Chen
- Department of Critical Care Medicine, Datong Third People's Hospital, Datong, Shanxi, China
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Bastos LSL, Hamacher S, Kurtz P, Ranzani OT, Zampieri FG, Soares M, Bozza FA, Salluh JIF. The Association Between Prepandemic ICU Performance and Mortality Variation in COVID-19: A Multicenter Cohort Study of 35,619 Critically Ill Patients. Chest 2024; 165:870-880. [PMID: 37838338 DOI: 10.1016/j.chest.2023.10.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Revised: 09/20/2023] [Accepted: 10/05/2023] [Indexed: 10/16/2023] Open
Abstract
BACKGROUND During the COVID-19 pandemic, ICUs remained under stress and observed elevated mortality rates and high variations of outcomes. A knowledge gap exists regarding whether an ICU performing best during nonpandemic times would still perform better when under high pressure compared with the least performing ICUs. RESEARCH QUESTION Does prepandemic ICU performance explain the risk-adjusted mortality variability for critically ill patients with COVID-19? STUDY DESIGN AND METHODS This study examined a cohort of adults with real-time polymerase chain reaction-confirmed COVID-19 admitted to 156 ICUs in 35 hospitals from February 16, 2020, through December 31, 2021, in Brazil. We evaluated crude and adjusted in-hospital mortality variability of patients with COVID-19 in the ICU during the pandemic. Association of baseline (prepandemic) ICU performance and in-hospital mortality was examined using a variable life-adjusted display (VLAD) during the pandemic and a multivariable mixed regression model adjusted by clinical characteristics, interaction of performance with the year of admission, and mechanical ventilation at admission. RESULTS Thirty-five thousand six hundred nineteen patients with confirmed COVID-19 were evaluated. The median age was 52 years, median Simplified Acute Physiology Score 3 was 42, and 18% underwent invasive mechanical ventilation. In-hospital mortality was 13% and 54% for those receiving invasive mechanical ventilation. Adjusted in-hospital mortality ranged from 3.6% to 63.2%. VLAD in the most efficient ICUs was higher than the overall median in 18% of weeks, whereas VLAD was 62% and 84% in the underachieving and least efficient groups, respectively. The least efficient baseline ICU performance group was associated independently with increased mortality (OR, 2.30; 95% CI, 1.45-3.62) after adjusting for patient characteristics, disease severity, and pandemic surge. INTERPRETATION ICUs caring for patients with COVID-19 presented substantial variation in risk-adjusted mortality. ICUs with better baseline (prepandemic) performance showed reduced mortality and less variability. Our findings suggest that achieving ICU efficiency by targeting improvement in organizational aspects of ICUs may impact outcomes, and therefore should be a part of the preparedness for future pandemics.
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Affiliation(s)
- Leonardo S L Bastos
- Department of Industrial Engineering, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, Brazil.
| | - Silvio Hamacher
- Department of Industrial Engineering, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Pedro Kurtz
- Hospital Copa Star, Rio de Janeiro, Brazil; Paulo Niemeyer State Brain Institute, Rio de Janeiro, Brazil; D'Or Institute for Research and Education, Rio de Janeiro, Brazil
| | - Otavio T Ranzani
- Pulmonary Division, Heart Institute, Hospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil; Barcelona Institute for Global Health, ISGlobal, Universitat Pompeu Fabra, CIBER Epidemiología y Salud Pública, Barcelona, Spain
| | - Fernando G Zampieri
- D'Or Institute for Research and Education, Rio de Janeiro, Brazil; Department of Critical Care Medicine, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, AB, Canada
| | - Marcio Soares
- D'Or Institute for Research and Education, Rio de Janeiro, Brazil
| | - Fernando A Bozza
- D'Or Institute for Research and Education, Rio de Janeiro, Brazil; National Institute of Infectious Disease Evandro Chagas, Oswaldo Cruz Foundation, Rio de Janeiro, Brazil
| | - Jorge I F Salluh
- D'Or Institute for Research and Education, Rio de Janeiro, Brazil; Postgraduate Program of Internal Medicine, Federal University of Rio de Janeiro, (UFRJ), Rio de Janeiro, Brazil
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França ARM, Rocha E, Bastos LSL, Bozza FA, Kurtz P, Maccariello E, Lapa E Silva JR, Salluh JIF. Development and validation of a machine learning model to predict the use of renal replacement therapy in 14,374 patients with COVID-19. J Crit Care 2024; 80:154480. [PMID: 38016226 DOI: 10.1016/j.jcrc.2023.154480] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2023] [Revised: 11/11/2023] [Accepted: 11/15/2023] [Indexed: 11/30/2023]
Abstract
PURPOSE To develop a model to predict the use of renal replacement therapy (RRT) in COVID-19 patients. MATERIALS AND METHODS Retrospective analysis of multicenter cohort of intensive care unit (ICU) admissions of Brazil involving COVID-19 critically adult patients, requiring ventilatory support, admitted to 126 Brazilian ICUs, from February 2020 to December 2021 (development) and January to May 2022 (validation). No interventions were performed. RESULTS Eight machine learning models' classifications were evaluated. Models were developed using an 80/20 testing/train split ratio and cross-validation. Thirteen candidate predictors were selected using the Recursive Feature Elimination (RFE) algorithm. Discrimination and calibration were assessed. Temporal validation was performed using data from 2022. Of 14,374 COVID-19 patients with initial respiratory support, 1924 (13%) required RRT. RRT patients were older (65 [53-75] vs. 55 [42-68]), had more comorbidities (Charlson's Comorbidity Index 1.0 [0.00-2.00] vs 0.0 [0.00-1.00]), had higher severity (SAPS-3 median: 61 [51-74] vs 48 [41-58]), and had higher in-hospital mortality (71% vs 22%) compared to non-RRT. Risk factors for RRT, such as Creatinine, Glasgow Coma Scale, Urea, Invasive Mechanical Ventilation, Age, Chronic Kidney Disease, Platelets count, Vasopressors, Noninvasive Ventilation, Hypertension, Diabetes, modified frailty index (mFI) and Gender, were identified. The best discrimination and calibration were found in the Random Forest (AUC [95%CI]: 0.78 [0.75-0.81] and Brier's Score: 0.09 [95%CI: 0.08-0.10]). The final model (Random Forest) showed comparable performance in the temporal validation (AUC [95%CI]: 0.79 [0.75-0.84] and Brier's Score, 0.08 [95%CI: 0.08-0.1]). CONCLUSIONS An early ML model using easily available clinical and laboratory data accurately predicted the use of RRT in critically ill patients with COVID-19. Our study demonstrates that using ML techniques is feasible to provide early prediction of use of RRT in COVID-19 patients.
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Affiliation(s)
- Allan R M França
- Postgraduate Program of Internal Medicine, Federal University of Rio de Janeiro, (UFRJ), Rio de Janeiro, Brazil.
| | - Eduardo Rocha
- Postgraduate Program of Internal Medicine, Federal University of Rio de Janeiro, (UFRJ), Rio de Janeiro, Brazil; Postgraduate Program, D'Or Institute for Research and Education (IDOR), Rio de Janeiro, RJ, Brazil
| | - Leonardo S L Bastos
- Department of Industrial Engineering (DEI), Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Rio de Janeiro, RJ, Brazil
| | - Fernando A Bozza
- Postgraduate Program, D'Or Institute for Research and Education (IDOR), Rio de Janeiro, RJ, Brazil; National Institute of Infectious Disease Evandro Chagas (INI), Oswaldo Cruz Foundation (FIOCRUZ), Rio de Janeiro, RJ, Brazil
| | - Pedro Kurtz
- Postgraduate Program, D'Or Institute for Research and Education (IDOR), Rio de Janeiro, RJ, Brazil; Hospital Copa Star, Rio de Janeiro, RJ, Brazil
| | - Elizabeth Maccariello
- Postgraduate Program, D'Or Institute for Research and Education (IDOR), Rio de Janeiro, RJ, Brazil
| | - José Roberto Lapa E Silva
- Postgraduate Program of Internal Medicine, Federal University of Rio de Janeiro, (UFRJ), Rio de Janeiro, Brazil
| | - Jorge I F Salluh
- Postgraduate Program of Internal Medicine, Federal University of Rio de Janeiro, (UFRJ), Rio de Janeiro, Brazil; Postgraduate Program, D'Or Institute for Research and Education (IDOR), Rio de Janeiro, RJ, Brazil
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Peter D, Li SX, Wang Y, Zhang J, Grady J, McDowell K, Norton E, Lin Z, Bernheim S, Venkatesh AK, Fleisher LA, Schreiber M, Suter LG, Triche EW. Pre-COVID-19 hospital quality and hospital response to COVID-19: examining associations between risk-adjusted mortality for patients hospitalised with COVID-19 and pre-COVID-19 hospital quality. BMJ Open 2024; 14:e077394. [PMID: 38553067 PMCID: PMC10982775 DOI: 10.1136/bmjopen-2023-077394] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Accepted: 02/25/2024] [Indexed: 04/02/2024] Open
Abstract
OBJECTIVES The extent to which care quality influenced outcomes for patients hospitalised with COVID-19 is unknown. Our objective was to determine if prepandemic hospital quality is associated with mortality among Medicare patients hospitalised with COVID-19. DESIGN This is a retrospective observational study. We calculated hospital-level risk-standardised in-hospital and 30-day mortality rates (risk-standardised mortality rates, RSMRs) for patients hospitalised with COVID-19, and correlation coefficients between RSMRs and pre-COVID-19 hospital quality, overall and stratified by hospital characteristics. SETTING Short-term acute care hospitals and critical access hospitals in the USA. PARTICIPANTS Hospitalised Medicare beneficiaries (Fee-For-Service and Medicare Advantage) age 65 and older hospitalised with COVID-19, discharged between 1 April 2020 and 30 September 2021. INTERVENTION/EXPOSURE Pre-COVID-19 hospital quality. OUTCOMES Risk-standardised COVID-19 in-hospital and 30-day mortality rates (RSMRs). RESULTS In-hospital (n=4256) RSMRs for Medicare patients hospitalised with COVID-19 (April 2020-September 2021) ranged from 4.5% to 59.9% (median 18.2%; IQR 14.7%-23.7%); 30-day RSMRs ranged from 12.9% to 56.2% (IQR 24.6%-30.6%). COVID-19 RSMRs were negatively correlated with star rating summary scores (in-hospital correlation coefficient -0.41, p<0.0001; 30 days -0.38, p<0.0001). Correlations with in-hospital RSMRs were strongest for patient experience (-0.39, p<0.0001) and timely and effective care (-0.30, p<0.0001) group scores; 30-day RSMRs were strongest for patient experience (-0.34, p<0.0001) and mortality (-0.33, p<0.0001) groups. Patients admitted to 1-star hospitals had higher odds of mortality (in-hospital OR 1.87, 95% CI 1.83 to 1.91; 30-day OR 1.46, 95% CI 1.43 to 1.48) compared with 5-star hospitals. If all hospitals performed like an average 5-star hospital, we estimate 38 000 fewer COVID-19-related deaths would have occurred between April 2020 and September 2021. CONCLUSIONS Hospitals with better prepandemic quality may have care structures and processes that allowed for better care delivery and outcomes during the COVID-19 pandemic. Understanding the relationship between pre-COVID-19 hospital quality and COVID-19 outcomes will allow policy-makers and hospitals better prepare for future public health emergencies.
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Affiliation(s)
- Doris Peter
- Center for Outcomes Research and Evaluation, Yale New Haven Health System, New Haven, Connecticut, USA
| | - Shu-Xia Li
- Center for Outcomes Research and Evaluation, Yale New Haven Health System, New Haven, Connecticut, USA
- Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut, USA
| | - Yongfei Wang
- Center for Outcomes Research and Evaluation, Yale New Haven Health System, New Haven, Connecticut, USA
- Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut, USA
| | - Jing Zhang
- Center for Outcomes Research and Evaluation, Yale New Haven Health System, New Haven, Connecticut, USA
- Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut, USA
| | - Jacqueline Grady
- Center for Outcomes Research and Evaluation, Yale New Haven Health System, New Haven, Connecticut, USA
| | - Kerry McDowell
- Center for Outcomes Research and Evaluation, Yale New Haven Health System, New Haven, Connecticut, USA
| | - Erica Norton
- Center for Outcomes Research and Evaluation, Yale New Haven Health System, New Haven, Connecticut, USA
| | - Zhenqiu Lin
- Center for Outcomes Research and Evaluation, Yale New Haven Health System, New Haven, Connecticut, USA
- Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut, USA
| | - Susannah Bernheim
- The Center for Medicare and Medicaid Innovation, Centers for Medicare and Medicaid Services, Baltimore, Maryland, USA
| | - Arjun K Venkatesh
- Center for Outcomes Research and Evaluation, Yale New Haven Health System, New Haven, Connecticut, USA
- Department of Emergency Medicine, Yale School of Medicine, New Haven, Connecticut, USA
| | - Lee A Fleisher
- Department of Anesthesiology and Critical Care, Perelman School of Medicine, Philadelphia, PA, Philadelphia, PA, USA
| | - Michelle Schreiber
- The Center for Clinical Standards and Quality, Centers for Medicare and Medicaid Services, Baltimore, Maryland, USA
| | - Lisa G Suter
- Center for Outcomes Research and Evaluation, Yale New Haven Health System, New Haven, Connecticut, USA
- Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut, USA
| | - Elizabeth W Triche
- Center for Outcomes Research and Evaluation, Yale New Haven Health System, New Haven, Connecticut, USA
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Pilcher DV, Hensman T, Bihari S, Bailey M, McClure J, Nicholls M, Chavan S, Secombe P, Rosenow M, Huckson S, Litton E. Measuring the Impact of ICU Strain on Mortality, After-Hours Discharge, Discharge Delay, Interhospital Transfer, and Readmission in Australia With the Activity Index. Crit Care Med 2023; 51:1623-1637. [PMID: 37486188 PMCID: PMC10645102 DOI: 10.1097/ccm.0000000000005985] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/25/2023]
Abstract
OBJECTIVES ICU resource strain leads to adverse patient outcomes. Simple, well-validated measures of ICU strain are lacking. Our objective was to assess whether the "Activity index," an indicator developed during the COVID-19 pandemic, was a valid measure of ICU strain. DESIGN Retrospective national registry-based cohort study. SETTING One hundred seventy-five public and private hospitals in Australia (June 2020 through March 2022). SUBJECTS Two hundred seventy-seven thousand seven hundred thirty-seven adult ICU patients. INTERVENTIONS None. MEASUREMENTS AND MAIN RESULTS Data from the Australian and New Zealand Intensive Care Society Adult Patient Database were matched to the Critical Health Resources Information System. The mean daily Activity index of each ICU (census total of "patients with 1:1 nursing" + "invasive ventilation" + "renal replacement" + "extracorporeal membrane oxygenation" + "active COVID-19," divided by total staffed ICU beds) during the patient's stay in the ICU was calculated. Patients were categorized as being in the ICU during very quiet (Activity index < 0.1), quiet (0.1 to < 0.6), intermediate (0.6 to < 1.1), busy (1.1 to < 1.6), or very busy time-periods (≥ 1.6). The primary outcome was in-hospital mortality. Secondary outcomes included after-hours discharge from the ICU, readmission to the ICU, interhospital transfer to another ICU, and delay in discharge from the ICU. Median Activity index was 0.87 (interquartile range, 0.40-1.24). Nineteen thousand one hundred seventy-seven patients died (6.9%). In-hospital mortality ranged from 2.4% during very quiet to 10.9% during very busy time-periods. After adjusting for confounders, being in an ICU during time-periods with higher Activity indices, was associated with an increased risk of in-hospital mortality (odds ratio [OR], 1.49; 99% CI, 1.38-1.60), after-hours discharge (OR, 1.27; 99% CI, 1.21-1.34), readmission (OR, 1.18; 99% CI, 1.09-1.28), interhospital transfer (OR, 1.92; 99% CI, 1.72-2.15), and less delay in ICU discharge (OR, 0.58; 99% CI, 0.55-0.62): findings consistent with ICU strain. CONCLUSIONS The Activity index is a simple and valid measure that identifies ICUs in which increasing strain leads to progressively worse patient outcomes.
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Affiliation(s)
- David V Pilcher
- The Australian and New Zealand Intensive Care Society (ANZICS) Centre for Outcome and Resource Evaluation, Prahran, VIC, Australia
- Department of Intensive Care, Alfred Health, Commercial Road, Prahran, VIC, Australia
- Australian and New Zealand Intensive Care Research Centre, Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, VIC, Australia
- Department of Intensive Care, Guy's and St. Thomas' NHS Foundation Trust, London, United Kingdom
- College of Medicine and Public Health, Flinders University, Adelaide, SA, Australia
- Adult Retrieval Victoria, Ambulance Victoria, South Melbourne, VIC, Australia
- Department of Intensive Care, St. Vincent's Hospital, Darlinghurst, NSW, Australia
- Department of Intensive Care, Alice Springs Hospital, Alice Springs, NT, Australia
- Department of Intensive Care, Fiona Stanley Hospital, Murdoch, WA, Australia
| | - Tamishta Hensman
- The Australian and New Zealand Intensive Care Society (ANZICS) Centre for Outcome and Resource Evaluation, Prahran, VIC, Australia
- Department of Intensive Care, Guy's and St. Thomas' NHS Foundation Trust, London, United Kingdom
| | - Shailesh Bihari
- College of Medicine and Public Health, Flinders University, Adelaide, SA, Australia
| | - Michael Bailey
- Australian and New Zealand Intensive Care Research Centre, Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, VIC, Australia
| | - Jason McClure
- Department of Intensive Care, Alfred Health, Commercial Road, Prahran, VIC, Australia
- Australian and New Zealand Intensive Care Research Centre, Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, VIC, Australia
- Adult Retrieval Victoria, Ambulance Victoria, South Melbourne, VIC, Australia
| | - Mark Nicholls
- The Australian and New Zealand Intensive Care Society (ANZICS) Centre for Outcome and Resource Evaluation, Prahran, VIC, Australia
- Department of Intensive Care, St. Vincent's Hospital, Darlinghurst, NSW, Australia
| | - Shaila Chavan
- The Australian and New Zealand Intensive Care Society (ANZICS) Centre for Outcome and Resource Evaluation, Prahran, VIC, Australia
| | - Paul Secombe
- The Australian and New Zealand Intensive Care Society (ANZICS) Centre for Outcome and Resource Evaluation, Prahran, VIC, Australia
- Australian and New Zealand Intensive Care Research Centre, Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, VIC, Australia
- Department of Intensive Care, Alice Springs Hospital, Alice Springs, NT, Australia
| | - Melissa Rosenow
- Adult Retrieval Victoria, Ambulance Victoria, South Melbourne, VIC, Australia
| | - Sue Huckson
- The Australian and New Zealand Intensive Care Society (ANZICS) Centre for Outcome and Resource Evaluation, Prahran, VIC, Australia
| | - Edward Litton
- The Australian and New Zealand Intensive Care Society (ANZICS) Centre for Outcome and Resource Evaluation, Prahran, VIC, Australia
- Department of Intensive Care, Fiona Stanley Hospital, Murdoch, WA, Australia
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Taxbro K, Hammarskjöld F, Nilsson M, Persson M, Chew MS, Sunnergren O. Factors related to COVID-19 mortality among three Swedish intensive care units-A retrospective study. Acta Anaesthesiol Scand 2023; 67:788-796. [PMID: 36915957 DOI: 10.1111/aas.14232] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2022] [Revised: 02/15/2023] [Accepted: 03/07/2023] [Indexed: 03/16/2023]
Abstract
BACKGROUND Mortality due to acute hypoxemic respiratory failure (AHRF) in patients with coronavirus disease-19 (COVID-19) differs across units, regions, and countries. These variations may be attributed to several factors, including comorbidities, acute physiological derangement, disease severity, treatment, ethnicity, healthcare system strain, and socioeconomic status. This study aimed to explore the features of patient characteristics, clinical management, and staffing that may be related to mortality among three intensive care units (ICUs) within the same hospital system in South Sweden. METHODS We retrospectively analyzed ICU patients with COVID-19 and AHRF in Region Jönköping County, Sweden. The primary outcome was the 90-day mortality rate. We used univariate and multivariable logistic regression analyses to investigate the relationship of predictors with outcomes. RESULTS Between March 15, 2020, and May 31, 2021, 331 patients with AHRF and COVID-19 were admitted to the three ICUs. There were differences in disease severity, treatments, process-related factors, and socioeconomic factors between the units. These factors were related to 90-day mortality. After multivariable adjustment, age, severity of acute respiratory distress syndrome, and the number of nurses per ICU-bed independently predicted 90-day mortality. CONCLUSION Age, disease severity, and nurse staffing, but not treatment or socioeconomic status, were independently associated with 90-day mortality among critically ill patients with AHRF due to COVID-19. We also identified variations in care related processes, which may be a modifiable risk factor and warrants future investigation.
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Affiliation(s)
- Knut Taxbro
- Department of Anaesthesia and Intensive Care Medicine, Ryhov County Hospital, Jönköping, Sweden
- Department of Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden
| | - Fredrik Hammarskjöld
- Department of Anaesthesia and Intensive Care Medicine, Ryhov County Hospital, Jönköping, Sweden
- Department of Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden
| | - Mats Nilsson
- Department of Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden
- Futurum, Academy of Health and Care, Region Jönköping County, Jönköping, Sweden
| | - Magnus Persson
- Department of Anaesthesia and Intensive Care Medicine, Värnamo Hospital, Värnamo, Sweden
| | - Michelle S Chew
- Department of Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden
- Department of Anaesthesia and Intensive Care Medicine, Linköping University Hospital, Linköping, Sweden
| | - Ola Sunnergren
- Department of Otorhinolaryngology, Region Jönköping County, Jönköping, Sweden
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Anesi GL, Savarimuthu SM, Invernizzi J, Hyman R, Ramkillawan A, Eddey C, Wise RD, Smith MTD. ICU Mortality Across Prepandemic and Pandemic Cohorts in a Resource-Limited Setting: A Critical Care Resiliency Analysis From South Africa. CHEST CRITICAL CARE 2023; 1:100005. [PMID: 39211576 PMCID: PMC11360720 DOI: 10.1016/j.chstcc.2023.100005] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 09/04/2024]
Abstract
BACKGROUND Hospital adaptation and resiliency, required during public health emergencies to optimize outcomes, are understudied especially in resource-limited settings. RESEARCH QUESTION What are the prepandemic and pandemic critical illness outcomes in a resource-limited setting and in the context of capacity strain? STUDY DESIGN AND METHODS We performed a retrospective cohort study among patients admitted to ICUs at two public hospitals in the KwaZulu-Natal Department of Health in South Africa preceding and during the COVID-19 pandemic (2017-2022). We used multivariate logistic regression to analyze the association between three patient cohorts (prepandemic non-COVID-19, pandemic non-COVID-19, and pandemic COVID-19) and ICU capacity strain and the primary outcome of ICU mortality. RESULTS Three thousand two hundred twenty-one patients were admitted to the ICU during the prepandemic period and 2,539 patients were admitted to the ICU during the pandemic period (n = 375 [14.8%] with COVID-19 and n = 2,164 [85.2%] without COVID-19). The prepandemic and pandemic non-COVID-19 cohorts were similar. Compared with the non-COVID-19 cohorts, the pandemic COVID-19 cohort showed older age, higher rates of chronic cardiovascular disease and diabetes, less extrapulmonary organ dysfunction, and longer ICU length of stay. Compared with the prepandemic non-COVID-19 cohort, the pandemic non-COVID-19 cohort showed similar odds of ICU mortality (OR, 1.06; 95% CI, 0.90-1.25; P = .50) whereas the pandemic COVID-19 cohort showed significantly increased odds of ICU mortality (OR, 3.91; 95% CI, 3.03-5.05 P < .0005). ICU occupancy was not associated with ICU mortality in either the COVID-19 cohort (OR, 1.05 per 10% change in ICU occupancy; 95% CI, 0.96-1.14; P = .27) or the pooled non-COVID-19 cohort (OR, 1.01 per 10% change in ICU occupancy; 95% CI, 0.98-1.03; P = .52). INTERPRETATION Patients admitted to the ICU before and during the pandemic without COVID-19 were broadly similar in clinical characteristics and outcomes, suggesting critical care resiliency, whereas patients admitted to the ICU with COVID-19 showed important clinical differences and significantly higher mortality.
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Affiliation(s)
- George L Anesi
- Division of Pulmonary, Allergy, and Critical Care, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
| | - Stella M Savarimuthu
- Section of Pulmonary, Critical Care, and Sleep Medicine, Yale School of Medicine, New Haven, CT
| | - Jonathan Invernizzi
- Department of Anaesthesia and Critical Care, Harry Gwala Regional Hospital, Greys Hospital, KwaZulu-Natal Department of Health, Pietermaritzburg
| | - Robyn Hyman
- Department of Anaesthesia and Critical Care, Harry Gwala Regional Hospital, Greys Hospital, KwaZulu-Natal Department of Health, Pietermaritzburg
| | - Arisha Ramkillawan
- Department of Anaesthesia and Critical Care, Greys Hospital, KwaZulu-Natal Department of Health, Pietermaritzburg
| | - Creaghan Eddey
- Department of Anaesthesia and Critical Care, Greys Hospital, KwaZulu-Natal Department of Health, Pietermaritzburg
| | - Robert D Wise
- Department of Anaesthesia and Critical Care, School of Clinical Medicine, University of KwaZulu-Natal, Durban, South Africa
- Faculty Medicine and Pharmacy, Vrije Universiteit Brussel, Brussels, Belgium
- Intensive Care Department, John Radcliffe Hospital, Oxford University Trust Hospitals, Oxford, England
| | - Michelle T D Smith
- Department of Anaesthesia and Critical Care, Greys Hospital, KwaZulu-Natal Department of Health, Pietermaritzburg
- Department of Anaesthesia and Critical Care, School of Clinical Medicine, University of KwaZulu-Natal, Durban, South Africa
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Andrist E. Fairly Distributing the Distributive Justice Argument Permits Stopping ECMO. THE AMERICAN JOURNAL OF BIOETHICS : AJOB 2023; 23:65-67. [PMID: 37220363 DOI: 10.1080/15265161.2023.2201226] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/25/2023]
Affiliation(s)
- Erica Andrist
- Center for Bioethics, Harvard Medical School
- University of Michigan
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10
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Zec S, Zorko Garbajs N, Dong Y, Gajic O, Kordik C, Harmon L, Bogojevic M, Singh R, Sun Y, Bansal V, Vu L, Cawcutt K, Litell JM, Redmond S, Fitzpatrick E, Kooda KJ, Biehl M, Dangayach NS, Kaul V, Chae JM, Leppin A, Siuba M, Kashyap R, Walkey AJ, Niven AS. Implementation of a Virtual Interprofessional ICU Learning Collaborative: Successes, Challenges, and Initial Reactions From the Structured Team-Based Optimal Patient-Centered Care for Virus COVID-19 Collaborators. Crit Care Explor 2023; 5:e0922. [PMID: 37637353 PMCID: PMC10456981 DOI: 10.1097/cce.0000000000000922] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/29/2023] Open
Abstract
Initial Society of Critical Care Medicine Discovery Viral Infection and Respiratory illness Universal Study (VIRUS) Registry analysis suggested that improvements in critical care processes offered the greatest modifiable opportunity to improve critically ill COVID-19 patient outcomes. OBJECTIVES The Structured Team-based Optimal Patient-Centered Care for Virus COVID-19 ICU Collaborative was created to identify and speed implementation of best evidence based COVID-19 practices. DESIGN SETTING AND PARTICIPANTS This 6-month project included volunteer interprofessional teams from VIRUS Registry sites, who received online training on the Checklist for Early Recognition and Treatment of Acute Illness and iNjury approach, a structured and systematic method for delivering evidence based critical care. Collaborators participated in weekly 1-hour videoconference sessions on high impact topics, monthly quality improvement (QI) coaching sessions, and received extensive additional resources for asynchronous learning. MAIN OUTCOMES AND MEASURES Outcomes included learner engagement, satisfaction, and number of QI projects initiated by participating teams. RESULTS Eleven of 13 initial sites participated in the Collaborative from March 2, 2021, to September 29, 2021. A total of 67 learners participated in the Collaborative, including 23 nurses, 22 physicians, 10 pharmacists, nine respiratory therapists, and three nonclinicians. Site attendance among the 11 sites in the 25 videoconference sessions ranged between 82% and 100%, with three sites providing at least one team member for 100% of sessions. The majority reported that topics matched their scope of practice (69%) and would highly recommend the program to colleagues (77%). A total of nine QI projects were initiated across three clinical domains and focused on improving adherence to established critical care practice bundles, reducing nosocomial complications, and strengthening patient- and family-centered care in the ICU. Major factors impacting successful Collaborative engagement included an engaged interprofessional team; an established culture of engagement; opportunities to benchmark performance and accelerate institutional innovation, networking, and acclaim; and ready access to data that could be leveraged for QI purposes. CONCLUSIONS AND RELEVANCE Use of a virtual platform to establish a learning collaborative to accelerate the identification, dissemination, and implementation of critical care best practices for COVID-19 is feasible. Our experience offers important lessons for future collaborative efforts focused on improving ICU processes of care.
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Affiliation(s)
- Simon Zec
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, MN
- Department of Anesthesia, Pain Medicine and Critical Care, Beth Israel Deaconess Medical Center, Boston, MA
| | - Nika Zorko Garbajs
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, MN
- Department of Vascular Neurology and Intensive Therapy, University Medical Centre Ljubljana, Ljubljana, Slovenia
| | - Yue Dong
- Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, MN
| | - Ognjen Gajic
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, MN
| | | | - Lori Harmon
- Society of Critical Care Medicine, Mount Prospect, IL
| | - Marija Bogojevic
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, MN
- Department of Medicine, Montefiore New Rochelle Hospital, New Rochelle, NY
| | - Romil Singh
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, MN
- Department of Neurology, Allegheny Network, Pittsburgh, PA
| | - Yuqiang Sun
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, MN
| | - Vikas Bansal
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, MN
| | - Linh Vu
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, MN
| | - Kelly Cawcutt
- Department of Internal Medicine, Division of Infectious Diseases, University of Nebraska Medical Center, Omaha, NE
| | - John M Litell
- Department of Emergency Medicine, Hennepin Healthcare, Minneapolis, MN
- Department of Emergency Medicine, University of Minnesota, Minneapolis, MN
| | - Sarah Redmond
- Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN
| | - Eleanor Fitzpatrick
- Surgical Intensive Care Unit, Thomas Jefferson University Hospital, Philadelphia, PA
| | | | - Michelle Biehl
- Department of Critical Care Medicine and Pulmonary Medicine, Respiratory Institute, Cleveland Clinic, Cleveland, OH
| | - Neha S Dangayach
- Neurocritical Care Division, Mount Sinai Health System, New York, NY
| | - Viren Kaul
- Department of Pulmonary and Critical Care Medicine, Crouse Health/State University of New York Upstate Medical University, Syracuse, NY
| | - June M Chae
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, MN
- Division of Pulmonary and Critical Care Medicine, Mayo Clinic Health System Eau Claire, Eau Claire, WI
| | - Aaron Leppin
- Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN
| | - Mathew Siuba
- Department of Critical Care Medicine and Pulmonary Medicine, Respiratory Institute, Cleveland Clinic, Cleveland, OH
| | - Rahul Kashyap
- Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, MN
| | - Allan J Walkey
- Pulmonary Center, Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, Department of Medicine, Evans Center of Implementation and Improvement Sciences, Boston University School of Medicine, Boston, MA
| | - Alexander S Niven
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, MN
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11
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Hua MJ, Feinglass J. Variations in COVID-19 Hospital Mortality by Patient Race/Ethnicity and Hospital Type in Illinois. J Racial Ethn Health Disparities 2023; 10:911-919. [PMID: 35257313 PMCID: PMC8900642 DOI: 10.1007/s40615-022-01279-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Revised: 02/25/2022] [Accepted: 02/28/2022] [Indexed: 11/25/2022]
Abstract
BACKGROUND AND OBJECTIVES It is controversial whether hospital care mitigated or exacerbated population level racial and ethnic disparities in COVID-19 mortality. To begin answering that question, this study analyzed variations in COVID-19 hospital mortality in Illinois by patient race and ethnicity and by hospital characteristics, while providing an estimate of hospital-level variation in COVID-19 mortality. METHOD This is a retrospective cohort study based on hospital administrative data for adult patients with COVID-19 discharged from acute care, non-federal Illinois hospitals from April 1, 2020 through June 30, 2021. The association of patient and hospital characteristics with the likelihood of death was analyzed using multilevel logistic regression. RESULTS There were 158,569 COVID-19-coded admissions to 181 general hospitals in Illinois; 14.5% resulted in death or discharge to hospice. Hospital deaths accounted for nearly 90% of all COVID-19-associated deaths over 15 months in Illinois. After adjusting for patient- and hospital-level characteristics, Hispanic patients had higher mortality risk (aOR 1.26, 95% CI: 1.20-1.33) as compared with non-Hispanic White patients, while non-Hispanic Black patients had lower mortality risk (aOR 0.75, 95% CI: 0.71-0.79). Safety net hospitals receiving disproportionate share hospital (DSH) funds had higher mortality risk (aOR 1.81, 95% CI: 1.43-2.30) compared with other hospitals. CONCLUSION Risk-adjusted COVID-19 hospital mortality was highest among patients of Hispanic ethnicity, while non-Hispanic Black patients had lower risk than non-Hispanic White patients. There was significant variation in hospital mortality rates, with particularly high safety net hospital mortality.
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Affiliation(s)
- Miao Jenny Hua
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine & Cook County Health, Chicago, IL, USA.
| | - Joe Feinglass
- Division of General Internal Medicine and Geriatrics, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
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Admon AJ, Iwashyna TJ, Kamphuis LA, Gundel SJ, Sahetya SK, Peltan ID, Chang SY, Han JH, Vranas KC, Mayer KP, Hope AA, Jolley SE, Caldwell E, Monahan ML, Hauschildt K, Brown SM, Aggarwal NR, Thompson BT, Hough CL. Assessment of Symptom, Disability, and Financial Trajectories in Patients Hospitalized for COVID-19 at 6 Months. JAMA Netw Open 2023; 6:e2255795. [PMID: 36787143 PMCID: PMC9929698 DOI: 10.1001/jamanetworkopen.2022.55795] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Accepted: 12/25/2022] [Indexed: 02/15/2023] Open
Abstract
Importance Individuals who survived COVID-19 often report persistent symptoms, disabilities, and financial consequences. However, national longitudinal estimates of symptom burden remain limited. Objective To measure the incidence and changes over time in symptoms, disability, and financial status after COVID-19-related hospitalization. Design, Setting, and Participants A national US multicenter prospective cohort study with 1-, 3-, and 6-month postdischarge visits was conducted at 44 sites participating in the National Heart, Lung, and Blood Institute Prevention and Early Treatment of Acute Lung Injury Network's Biology and Longitudinal Epidemiology: COVID-19 Observational (BLUE CORAL) study. Participants included hospitalized English- or Spanish-speaking adults without severe prehospitalization disabilities or cognitive impairment. Participants were enrolled between August 24, 2020, and July 20, 2021, with follow-up occurring through March 30, 2022. Exposure Hospitalization for COVID-19 as identified with a positive SARS-CoV-2 molecular test. Main Outcomes and Measures New or worsened cardiopulmonary symptoms, financial problems, functional impairments, perceived return to baseline health, and quality of life. Logistic regression was used to identify factors associated with new cardiopulmonary symptoms or financial problems at 6 months. Results A total of 825 adults (444 [54.0%] were male, and 379 [46.0%] were female) met eligibility criteria and completed at least 1 follow-up survey. Median age was 56 (IQR, 43-66) years; 253 (30.7%) participants were Hispanic, 145 (17.6%) were non-Hispanic Black, and 360 (43.6%) were non-Hispanic White. Symptoms, disabilities, and financial problems remained highly prevalent among hospitalization survivors at month 6. Rates increased between months 1 and 6 for cardiopulmonary symptoms (from 67.3% to 75.4%; P = .001) and fatigue (from 40.7% to 50.8%; P < .001). Decreases were noted over the same interval for prevalent financial problems (from 66.1% to 56.4%; P < .001) and functional limitations (from 55.3% to 47.3%; P = .004). Participants not reporting problems at month 1 often reported new symptoms (60.0%), financial problems (23.7%), disabilities (23.8%), or fatigue (41.4%) at month 6. Conclusions and Relevance The findings of this cohort study of people discharged after COVID-19 hospitalization suggest that recovery in symptoms, functional status, and fatigue was limited at 6 months, and some participants reported new problems 6 months after hospital discharge.
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Affiliation(s)
- Andrew J. Admon
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, University of Michigan Medical School, Ann Arbor
- Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor
- VA Center for Clinical Management Research, LTC Charles Kettles VA Medical Center, Ann Arbor, Michigan
- University of Michigan Institute for Healthcare Policy and Innovation, Ann Arbor
| | - Theodore J. Iwashyna
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, University of Michigan Medical School, Ann Arbor
- VA Center for Clinical Management Research, LTC Charles Kettles VA Medical Center, Ann Arbor, Michigan
- University of Michigan Institute for Healthcare Policy and Innovation, Ann Arbor
- Health Policy and Management, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland
- Pulmonary and Critical Care Medicine, Department of Medicine, Johns Hopkins University, Baltimore, Maryland
| | - Lee A. Kamphuis
- VA Center for Clinical Management Research, LTC Charles Kettles VA Medical Center, Ann Arbor, Michigan
| | - Stephanie J. Gundel
- Department of Emergency Medicine, Harborview Medical Center, Seattle, Washington
| | - Sarina K. Sahetya
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Ithan D. Peltan
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Intermountain Medical Center, Murray, Utah
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City
| | - Steven Y. Chang
- Ronald Reagan-UCLA Medical Center, Pulmonary & Critical Care Medicine, David Geffen School of Medicine at University of California, Los Angeles
| | - Jin H. Han
- Department of Emergency Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
- Geriatric Research, Education, and Clinical Center, VA Tennessee Valley Healthcare System, Nashville
| | - Kelly C. Vranas
- Division of Pulmonary and Critical Care Medicine, Oregon Health & Science University, Portland
- Center to Improve Veteran Involvement in Care, VA Portland Health Care System, Oregon Health and Science University, Portland, Oregon
| | - Kirby P. Mayer
- Department of Physical Therapy, College of Health Sciences, University of Kentucky, Lexington
| | - Aluko A. Hope
- Division of Pulmonary and Critical Care Medicine, Department of Mundeledicine, Oregon Health and Science University School of Medicine, Portland
| | - Sarah E. Jolley
- Division of Pulmonary Sciences and Critical Care Medicine, University of Colorado, Denver
| | - Ellen Caldwell
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, University of Washington, Seattle
| | - Max L. Monahan
- Pulmonary and Critical Care Medicine, Department of Medicine, Johns Hopkins University, Baltimore, Maryland
- Center for Clinical Management Research, VA Ann Arbor Healthcare System, Ann Arbor, Michigan
| | - Katrina Hauschildt
- Pulmonary and Critical Care Medicine, Department of Medicine, Johns Hopkins University, Baltimore, Maryland
- Division of Pulmonary and Critical Medicine, Massachusetts General Hospital, Boston, Massachusetts
| | - Samuel M. Brown
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Intermountain Medical Center, Murray, Utah
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City
| | - Neil R. Aggarwal
- Division of Pulmonary Sciences and Critical Care Medicine, University of Colorado, Denver
| | - B. Taylor Thompson
- Division of Pulmonary and Critical Medicine, Massachusetts General Hospital, Boston, Massachusetts
| | - Catherine L. Hough
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Oregon Health and Science University School of Medicine, Portland
- The National Heart, Lung, and Blood Institute Prevention and Early Treatment of Acute Lung Injury Network
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Ramos FJDS, Atallah FC, de Souza MA, Ferreira EM, Machado FR, Freitas FGR. Determinants of death in critically ill COVID-19 patients during the first wave of COVID-19: a multicenter study in Brazil. J Bras Pneumol 2023; 48:e20220083. [PMID: 36629631 PMCID: PMC9747148 DOI: 10.36416/1806-3756/e20220083] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Accepted: 06/07/2022] [Indexed: 01/14/2023] Open
Abstract
OBJECTIVE To evaluate clinical outcomes and factors associated with mortality, focusing on secondary infections, in critically ill patients with COVID-19 in three Brazilian hospitals during the first pandemic wave. METHODS This was a retrospective observational study involving adult patients with COVID-19 admitted to one of the participating ICUs between March and August of 2020. We analyzed clinical features, comorbidities, source of SARS-CoV-2 infection, laboratory data, microbiology data, complications, and causes of death. We assessed factors associated with in-hospital mortality using logistic regression models. RESULTS We included 645 patients with a mean age of 61.4 years. Of those, 387 (60.0%) were male, 12.9% (83/643) had undergone solid organ transplant, and almost 10% (59/641) had nosocomial COVID-19 infection. During ICU stay, 359/644 patients (55.7%) required invasive mechanical ventilation, 225 (34.9%) needed renal replacement therapy, 337 (52.2%) received vasopressors, and 216 (33.5%) had hospital-acquired infections (HAIs), mainly caused by multidrug-resistant gram-negative bacteria. HAIs were independently associated with a higher risk of death. The major causes of death were refractory shock and multiple organ dysfunction syndrome but not ARDS, as previously reported in the literature. CONCLUSIONS In this study, most of our cohort required invasive mechanical ventilation and almost one third had HAIs, which were independently associated with a higher risk of death. Other factors related to death were Charlson Comorbidity Index, SOFA score at admission, and clinical complications during ICU stay. Nosocomial COVID-19 infection was not associated with death. The main immediate causes of death were refractory shock and multiple organ dysfunction syndrome.
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Affiliation(s)
- Fernando Jose da Silva Ramos
- . Serviço de Anestesiologia, Dor e Medicina Intensiva, Hospital São Paulo, Escola Paulista de Medicina, Universidade Federal de São Paulo - UNIFESP - São Paulo (SP) Brasil.,. Hospital BP Mirante, São Paulo (SP) Brasil
| | - Fernanda Chohfi Atallah
- . Serviço de Anestesiologia, Dor e Medicina Intensiva, Hospital São Paulo, Escola Paulista de Medicina, Universidade Federal de São Paulo - UNIFESP - São Paulo (SP) Brasil.,. Hospital BP Mirante, São Paulo (SP) Brasil
| | - Maria Aparecida de Souza
- . Serviço de Anestesiologia, Dor e Medicina Intensiva, Hospital São Paulo, Escola Paulista de Medicina, Universidade Federal de São Paulo - UNIFESP - São Paulo (SP) Brasil
| | - Elaine Maria Ferreira
- . Serviço de Anestesiologia, Dor e Medicina Intensiva, Hospital São Paulo, Escola Paulista de Medicina, Universidade Federal de São Paulo - UNIFESP - São Paulo (SP) Brasil
| | - Flavia Ribeiro Machado
- . Serviço de Anestesiologia, Dor e Medicina Intensiva, Hospital São Paulo, Escola Paulista de Medicina, Universidade Federal de São Paulo - UNIFESP - São Paulo (SP) Brasil
| | - Flavio Geraldo Resende Freitas
- . Serviço de Anestesiologia, Dor e Medicina Intensiva, Hospital São Paulo, Escola Paulista de Medicina, Universidade Federal de São Paulo - UNIFESP - São Paulo (SP) Brasil.,. Hospital SEPACO, São Paulo (SP) Brasil
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14
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Clinical characteristics and factors associated with ICU mortality during the first year of the SARS-Cov-2 pandemic in Romania: A prospective, cohort, multicentre study of 9000 patients. Eur J Anaesthesiol 2023; 40:4-12. [PMID: 36385096 DOI: 10.1097/eja.0000000000001776] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
BACKGROUND The epidemiology of critically ill patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) may be different worldwide. Despite similarities in medicine quality and formation, there are also significant differences concerning healthcare and ICU organisation, staffing, financial resources and population compliance and adherence. Large cohort data of critically ill patients from Central and Eastern Europe are also lacking. OBJECTIVES The study objectives were to describe the clinical characteristics of patients admitted to Romanian ICUs with SARS-CoV-2 infection and to identify the factors associated with ICU mortality. DESIGN Prospective, cohort, observational study. SETTING National recruitment, multicentre study, between March 2020 to March 2021. PATIENTS All patients with SARS-CoV-2 infection admitted to Romanian ICUs were eligible. There were no exclusion criteria. INTERVENTION None. MAIN OUTCOME MEASURE ICU mortality. RESULTS The statistical analysis included 9058 patients with definitive ICU outcome. The multivariable mixed effects logistic regression model found that age [odds ratio (OR) 1.27; 95% confidence interval (CI), 1.23 to 1.31], male gender (OR 1.21; 95% CI 1.05 to 1.4), medical history of neoplasia (OR 1.74; 95% CI, 1.36 to 2.22), chronic kidney disease (OR 1.54; 95% CI, 1.27 to 1.88), type II diabetes (OR 1.23; 95% CI, 1.06 to 1.43), chronic heart failure (OR 1.24; 95% CI, 1.03 to 1.49), dyspnoea (OR 1.3; 95% CI, 1.1 to 1.5), SpO2 less than 90% (OR 3; 95% CI, 2.5 to 3.5), admission SOFA score (OR 1.07; 95% CI, 1.05 to 1.09), acute respiratory distress syndrome (ARDS) on ICU admission (OR 1.35; 95% CI, 1.1 to 1.63) and the need for noninvasive (OR 1.8, 95% CI, 1.5 to 1.22) or invasive ventilation (OR 28; 95% CI, 22 to 35) and neuromuscular blockade (OR 3.5; 95% CI, 2.6 to 4.8), were associated with larger ICU mortality.Higher GCS on admission (OR 0.81; 95% CI, 0.79 to 0.83), treatment with hydroxychloroquine (OR 0.78; 95% CI, 0.64 to 0.95) and tocilizumab (OR 0.58; 95% CI, 0.48 to 0.71) were inversely associated with ICU mortality. CONCLUSION The SARS-CoV-2 critically ill Romanian patients share common personal and clinical characteristics with published European cohorts. Public health measures and vaccination campaign should focus on patients at risk.
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López-Martínez C, Martín-Vicente P, Gómez de Oña J, López-Alonso I, Gil-Peña H, Cuesta-Llavona E, Fernández-Rodríguez M, Crespo I, Salgado Del Riego E, Rodríguez-García R, Parra D, Fernández J, Rodríguez-Carrio J, Jimeno-Demuth FJ, Dávalos A, Chapado LA, Coto E, Albaiceta GM, Amado-Rodríguez L. Transcriptomic clustering of critically ill COVID-19 patients. Eur Respir J 2023; 61:13993003.00592-2022. [PMID: 36104291 PMCID: PMC9478362 DOI: 10.1183/13993003.00592-2022] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2022] [Accepted: 08/19/2022] [Indexed: 02/01/2023]
Abstract
BACKGROUND Infections caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) may cause a severe disease, termed coronavirus disease 2019 (COVID-19), with significant mortality. Host responses to this infection, mainly in terms of systemic inflammation, have emerged as key pathogenetic mechanisms and their modulation has shown a mortality benefit. METHODS In a cohort of 56 critically ill COVID-19 patients, peripheral blood transcriptomes were obtained at admission to an intensive care unit (ICU) and clustered using an unsupervised algorithm. Differences in gene expression, circulating microRNAs (c-miRNAs) and clinical data between clusters were assessed, and circulating cell populations estimated from sequencing data. A transcriptomic signature was defined and applied to an external cohort to validate the findings. RESULTS We identified two transcriptomic clusters characterised by expression of either interferon-related or immune checkpoint genes, respectively. Steroids have cluster-specific effects, decreasing lymphocyte activation in the former but promoting B-cell activation in the latter. These profiles have different ICU outcomes, despite no major clinical differences at ICU admission. A transcriptomic signature was used to identify these clusters in two external validation cohorts (with 50 and 60 patients), yielding similar results. CONCLUSIONS These results reveal different underlying pathogenetic mechanisms and illustrate the potential of transcriptomics to identify patient endotypes in severe COVID-19 with the aim to ultimately personalise their therapies.
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Affiliation(s)
- Cecilia López-Martínez
- Instituto de Investigación Sanitaria del Principado de Asturias, Oviedo, Spain
- Centro de Investigación Biomédica en Red (CIBER)-Enfermedades Respiratorias, Madrid, Spain
- Instituto Universitario de Oncología del Principado de Asturias, Oviedo, Spain
| | - Paula Martín-Vicente
- Instituto de Investigación Sanitaria del Principado de Asturias, Oviedo, Spain
- Centro de Investigación Biomédica en Red (CIBER)-Enfermedades Respiratorias, Madrid, Spain
- Instituto Universitario de Oncología del Principado de Asturias, Oviedo, Spain
| | - Juan Gómez de Oña
- Instituto de Investigación Sanitaria del Principado de Asturias, Oviedo, Spain
- Servicio de Genética Molecular, Hospital Universitario Central de Asturias, Oviedo, Spain
- Red de Investigación Renal (REDINREN), Madrid, Spain
| | - Inés López-Alonso
- Instituto de Investigación Sanitaria del Principado de Asturias, Oviedo, Spain
- Centro de Investigación Biomédica en Red (CIBER)-Enfermedades Respiratorias, Madrid, Spain
- Instituto Universitario de Oncología del Principado de Asturias, Oviedo, Spain
- Departamento de Morfología y Biología Celular, Universidad de Oviedo, Oviedo, Spain
| | - Helena Gil-Peña
- Instituto de Investigación Sanitaria del Principado de Asturias, Oviedo, Spain
- Servicio de Pediatría, Hospital Universitario Central de Asturias, Oviedo, Spain
| | - Elías Cuesta-Llavona
- Instituto de Investigación Sanitaria del Principado de Asturias, Oviedo, Spain
- Servicio de Genética Molecular, Hospital Universitario Central de Asturias, Oviedo, Spain
| | - Margarita Fernández-Rodríguez
- Instituto de Investigación Sanitaria del Principado de Asturias, Oviedo, Spain
- Instituto Universitario de Oncología del Principado de Asturias, Oviedo, Spain
| | - Irene Crespo
- Instituto de Investigación Sanitaria del Principado de Asturias, Oviedo, Spain
- Centro de Investigación Biomédica en Red (CIBER)-Enfermedades Respiratorias, Madrid, Spain
- Departamento de Biología Funcional, Universidad de Oviedo, Oviedo, Spain
| | - Estefanía Salgado Del Riego
- Instituto de Investigación Sanitaria del Principado de Asturias, Oviedo, Spain
- Unidad de Cuidados Intensivos Polivalente, Hospital Universitario Central de Asturias, Oviedo, Spain
| | - Raquel Rodríguez-García
- Instituto de Investigación Sanitaria del Principado de Asturias, Oviedo, Spain
- Centro de Investigación Biomédica en Red (CIBER)-Enfermedades Respiratorias, Madrid, Spain
- Unidad de Cuidados Intensivos Cardiológicos, Hospital Universitario Central de Asturias, Oviedo, Spain
| | - Diego Parra
- Instituto de Investigación Sanitaria del Principado de Asturias, Oviedo, Spain
- Unidad de Cuidados Intensivos Cardiológicos, Hospital Universitario Central de Asturias, Oviedo, Spain
| | - Javier Fernández
- Instituto de Investigación Sanitaria del Principado de Asturias, Oviedo, Spain
- Servicio de Microbiología, Hospital Universitario Central de Asturias, Oviedo, Spain
| | - Javier Rodríguez-Carrio
- Instituto de Investigación Sanitaria del Principado de Asturias, Oviedo, Spain
- Departamento de Biología Funcional, Universidad de Oviedo, Oviedo, Spain
| | | | - Alberto Dávalos
- Instituto Madrileño de Estudios Avanzados (IMDEA) Alimentación, CEI UAM+CSIC, Madrid, Spain
| | - Luis A Chapado
- Instituto Madrileño de Estudios Avanzados (IMDEA) Alimentación, CEI UAM+CSIC, Madrid, Spain
| | - Eliecer Coto
- Instituto de Investigación Sanitaria del Principado de Asturias, Oviedo, Spain
- Servicio de Genética Molecular, Hospital Universitario Central de Asturias, Oviedo, Spain
- Red de Investigación Renal (REDINREN), Madrid, Spain
- Departamento de Medicina, Universidad de Oviedo, Oviedo, Spain
| | - Guillermo M Albaiceta
- Instituto de Investigación Sanitaria del Principado de Asturias, Oviedo, Spain
- Centro de Investigación Biomédica en Red (CIBER)-Enfermedades Respiratorias, Madrid, Spain
- Instituto Universitario de Oncología del Principado de Asturias, Oviedo, Spain
- Departamento de Biología Funcional, Universidad de Oviedo, Oviedo, Spain
- Unidad de Cuidados Intensivos Cardiológicos, Hospital Universitario Central de Asturias, Oviedo, Spain
- G.M. Albaiceta and L. Amado-Rodríguez share last authorship
| | - Laura Amado-Rodríguez
- Instituto de Investigación Sanitaria del Principado de Asturias, Oviedo, Spain
- Centro de Investigación Biomédica en Red (CIBER)-Enfermedades Respiratorias, Madrid, Spain
- Instituto Universitario de Oncología del Principado de Asturias, Oviedo, Spain
- Unidad de Cuidados Intensivos Cardiológicos, Hospital Universitario Central de Asturias, Oviedo, Spain
- Departamento de Medicina, Universidad de Oviedo, Oviedo, Spain
- G.M. Albaiceta and L. Amado-Rodríguez share last authorship
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16
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Leatherman JW, Prekker ME, Kummer RL, Maurer JL, Beacom EJ, Ahiskali AS, Shapiro RS. Ventilatory Parameters Measured After One Week of Mechanical Ventilation and Survival in COVID-19-Related ARDS. Respir Care 2023; 68:44-51. [PMID: 36318980 PMCID: PMC9993523 DOI: 10.4187/respcare.10029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
BACKGROUND Ventilatory parameters measured soon after initiation of mechanical ventilation have limited ability to predict outcome of COVID-19-related ARDS. We hypothesized that ventilatory parameters measured after one week of mechanical ventilation might differ between survivors and non-survivors. METHODS One hundred twenty-seven subjects with COVID-related ARDS had gas exchange and lung mechanics assessed on the day of intubation and one week later. The main parameters of interest were PaO2 /FIO2 , ventilatory ratio (VR), respiratory system compliance (CRS), and a composite score that was calculated as (PaO2 /FIO2 /100) × CRS/VR. The primary outcome was death in the ICU. RESULTS Of the 127 subjects, 42 (33%) died in the ICU and 85 (67%) were successfully extubated. On the day of intubation, PaO2 /FIO2 , CRS, and composite score of survivors and non-survivors were similar, but survivors had a lower VR. At one week, as compared to survivors, non-survivors had a significantly higher VR (2.04 ± 0.76 vs 1.60 ± 0.43, P < .001), lower CRS (27.4 ± 6.4 mL/cm H2O vs 32.4 ± 9.3 mL/cm H2O, P = .002), and lower composite score (20.6 ± 11.9 vs 34.5 ± 18.6, P < .001), with no statistically significant difference in PaO2 /FIO2 (137 ± 49 vs 155 ± 48, P = .08). CONCLUSIONS In subjects with COVID ARDS, parameters that reflect dead space (VR), lung mechanics (CRS), and a combined score that included PaO2 /FIO2 , VR, and CRS differed between survivors and non-survivors after one week of mechanical ventilation but with considerable overlap of values between survivors and non-survivors.
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Affiliation(s)
- James W Leatherman
- Division of Pulmonary and Critical Care Medicine, Hennepin County Medical Center, Minneapolis, Minnesota; and the University of Minnesota, Minneapolis, Minnesota.
| | - Matthew E Prekker
- Division of Pulmonary and Critical Care Medicine, Hennepin County Medical Center, Minneapolis, Minnesota; and the University of Minnesota, Minneapolis, Minnesota
| | - Rebecca L Kummer
- Internal Medicine Residency Program, Hennepin County Medical Center, Minneapolis, Minnesota
| | - John L Maurer
- Internal Medicine Residency Program, Hennepin County Medical Center, Minneapolis, Minnesota
| | - Evan J Beacom
- Internal Medicine Residency Program, Hennepin County Medical Center, Minneapolis, Minnesota
| | - Aileen S Ahiskali
- Department of Pharmacy, Hennepin County Medical Center, Minneapolis, Minnesota
| | - Robert S Shapiro
- Division of Pulmonary and Critical Care Medicine, Hennepin County Medical Center, Minneapolis, Minnesota; and the University of Minnesota, Minneapolis, Minnesota
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17
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Impact of ICU strain on outcomes. Curr Opin Crit Care 2022; 28:667-673. [PMID: 36226707 DOI: 10.1097/mcc.0000000000000993] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
PURPOSE OF REVIEW Acute surge events result in health capacity strain, which can result in deviations from normal care, activation of contingencies and decisions related to resource allocation. This review discusses the impact of health capacity strain on patient centered outcomes. RECENT FINDINGS This manuscript discusses the lack of validated metrics for ICU strain capacity and a need for understanding the complex interrelationships of strain with patient outcomes. Recent work through the coronavirus disease 2019 pandemic has shown that acute surge events are associated with significant increase in hospital mortality. Though causal data on the differential impact of surge actions and resource availability on patient outcomes remains limited the overall signal consistently highlights the link between ICU strain and critical care outcomes in both normal and surge conditions. SUMMARY An understanding of ICU strain is fundamental to the appropriate clinical care for critically ill patients. Accounting for stain on outcomes in critically ill patients allows for minimization of variation in care and an ability of a given healthcare system to provide equitable, and quality care even in surge scenarios.
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18
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Leither LM, Buckel W, Brown SM. Care of the Seriously Ill Patient with SARS-CoV-2. Med Clin North Am 2022; 106:949-960. [PMID: 36280338 PMCID: PMC9364720 DOI: 10.1016/j.mcna.2022.08.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
In late 2019, SARS-CoV-2 caused the greatest global health crisis in a century, impacting all aspects of society. As the COVID-19 pandemic evolved throughout 2020 and 2021, multiple variants emerged, contributing to multiple surges in cases of COVID-19 worldwide. In 2021, highly effective vaccines became available, although the pandemic continues into 2022. There has been tremendous expansion of basic, translational, and clinical knowledge about SARS-CoV-2 and COVID-19 since the pandemic's onset. Treatment options have been rapidly explored, attempting to repurpose preexisting medications in tandem with development and evaluation of novel agents. Care of the seriously ill patient is examined.
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Affiliation(s)
- Lindsay M Leither
- Division of Pulmonary & Critical Care Medicine, Department of Medicine, Intermountain Medical Center, 5121 South Cottonwood Street, Salt Lake City, UT 84107, USA; Division of Pulmonary & Critical Care Medicine, Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA.
| | - Whitney Buckel
- Pharmacy Services, Intermountain Healthcare, 4393 S Riverboat Road, Taylorsville, UT 84123, USA
| | - Samuel M Brown
- Division of Pulmonary & Critical Care Medicine, Department of Medicine, Intermountain Medical Center, 5121 South Cottonwood Street, Salt Lake City, UT 84107, USA; Division of Pulmonary & Critical Care Medicine, Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA
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19
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Souza-Silva MVR, Ziegelmann PK, Nobre V, Gomes VMR, Etges APBDS, Schwarzbold AV, Nunes AGS, Maurílio ADO, Scotton ALBA, Costa ASDM, Glaeser AB, Farace BL, Ribeiro BN, Ramos CM, Cimini CCR, de Carvalho CA, Rempel C, Silveira DV, Carazai DDR, Ponce D, Pereira EC, Kroger EMS, Manenti ERF, Cenci EPDA, Lucas FB, dos Santos FC, Anschau F, Botoni FA, Aranha FG, de Aguiar FC, Bartolazzi F, Crestani GP, Vietta GG, Nascimento GF, Noal HC, Duani H, Vianna HR, Guimarães HC, de Alvarenga JC, Chatkin JM, de Morais JDP, Carvalho JDSN, Rugolo JM, Ruschel KB, Gomes LDBW, de Oliveira LS, Zandoná LB, Pinheiro LS, Pacheco LS, Menezes LDSM, Sousa LDD, de Moura LCS, Santos LEA, Nasi LA, Cabral MADS, Floriani MA, Souza MD, Carneiro M, de Godoy MF, Cardoso MMDA, Nogueira MCA, Lima MOSDS, de Figueiredo MP, Guimarães-Júnior MH, Sampaio NDCS, de Oliveira NR, Andrade PGS, Assaf PL, Martelli PJDL, Martins RC, Valacio RA, Pozza R, Menezes RM, Mourato RLS, de Abreu RM, Silva RDF, Francisco SC, Guimarães SMM, Araújo SF, Oliveira TF, Kurtz T, Fereguetti TO, de Oliveira TC, Ribeiro YCNMB, Ramires YC, Polanczyk CA, Marcolino MS. Hospital characteristics associated with COVID-19 mortality: data from the multicenter cohort Brazilian Registry. Intern Emerg Med 2022; 17:2299-2313. [PMID: 36153772 PMCID: PMC9510333 DOI: 10.1007/s11739-022-03092-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Accepted: 08/30/2022] [Indexed: 11/27/2022]
Abstract
The COVID-19 pandemic caused unprecedented pressure over health care systems worldwide. Hospital-level data that may influence the prognosis in COVID-19 patients still needs to be better investigated. Therefore, this study analyzed regional socioeconomic, hospital, and intensive care units (ICU) characteristics associated with in-hospital mortality in COVID-19 patients admitted to Brazilian institutions. This multicenter retrospective cohort study is part of the Brazilian COVID-19 Registry. We enrolled patients ≥ 18 years old with laboratory-confirmed COVID-19 admitted to the participating hospitals from March to September 2020. Patients' data were obtained through hospital records. Hospitals' data were collected through forms filled in loco and through open national databases. Generalized linear mixed models with logit link function were used for pooling mortality and to assess the association between hospital characteristics and mortality estimates. We built two models, one tested general hospital characteristics while the other tested ICU characteristics. All analyses were adjusted for the proportion of high-risk patients at admission. Thirty-one hospitals were included. The mean number of beds was 320.4 ± 186.6. These hospitals had eligible 6556 COVID-19 admissions during the study period. Estimated in-hospital mortality ranged from 9.0 to 48.0%. The first model included all 31 hospitals and showed that a private source of funding (β = - 0.37; 95% CI - 0.71 to - 0.04; p = 0.029) and location in areas with a high gross domestic product (GDP) per capita (β = - 0.40; 95% CI - 0.72 to - 0.08; p = 0.014) were independently associated with a lower mortality. The second model included 23 hospitals and showed that hospitals with an ICU work shift composed of more than 50% of intensivists (β = - 0.59; 95% CI - 0.98 to - 0.20; p = 0.003) had lower mortality while hospitals with a higher proportion of less experienced medical professionals had higher mortality (β = 0.40; 95% CI 0.11-0.68; p = 0.006). The impact of those association increased according to the proportion of high-risk patients at admission. In-hospital mortality varied significantly among Brazilian hospitals. Private-funded hospitals and those located in municipalities with a high GDP had a lower mortality. When analyzing ICU-specific characteristics, hospitals with more experienced ICU teams had a reduced mortality.
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Affiliation(s)
- Maira Viana Rego Souza-Silva
- grid.8430.f0000 0001 2181 4888Medical School and University Hospital, Universidade Federal de Minas Gerais, Avenida Professor Alfredo Balena, 190, sala 246, Belo Horizonte, Minas Gerais Brazil
| | - Patricia Klarmann Ziegelmann
- grid.8532.c0000 0001 2200 7498Departament of Statistics, Universidade Federal Do Rio Grande Do Sul, Porto Alegre, Rio Grande do Sul Brazil
| | - Vandack Nobre
- grid.8430.f0000 0001 2181 4888Medical School and University Hospital, Universidade Federal de Minas Gerais, Avenida Professor Alfredo Balena, 190, sala 246, Belo Horizonte, Minas Gerais Brazil
| | - Virginia Mara Reis Gomes
- grid.411452.70000 0000 9898 6728Centro Universitário de Belo Horizonte (UniBH), Belo Horizonte, Minas Gerais Brazil
| | | | | | | | | | | | | | - Andressa Barreto Glaeser
- grid.414856.a0000 0004 0398 2134Hospital Moinhos de Vento, Porto Alegre, Rio Grande do Sul Brazil
| | - Bárbara Lopes Farace
- grid.490178.3Hospital Risoleta Tolentino Neves, Belo Horizonte, Minas Gerais Brazil
| | | | | | | | | | - Claudete Rempel
- grid.441846.b0000 0000 9020 9633Universidade Do Vale Do Taquari, Lajeado, Rio Grande do Sul Brazil
| | | | | | - Daniela Ponce
- grid.410543.70000 0001 2188 478XMedical School, Universidade Estadual Paulista “Júlio de Mesquita Filho”, Botucatu, São Paulo Brazil
| | | | | | | | | | | | | | - Fernando Anschau
- grid.414914.dHospital Nossa Senhora da Conceição, Porto Alegre, Rio Grande do Sul Brazil
| | | | | | - Filipe Carrilho de Aguiar
- grid.411227.30000 0001 0670 7996University Hospital, Universidade Federal de Pernambuco, Recife, Pernambuco Brazil
| | | | - Gabriela Petry Crestani
- grid.414871.f0000 0004 0491 7596Hospital Mãe de Deus, Porto Alegre, Rio Grande do Sul Brazil
| | | | | | - Helena Carolina Noal
- grid.488599.10000 0004 0481 6891Hospital Universitário de Santa Maria, Santa Maria, Rio Grande do Sul Brazil
| | - Helena Duani
- grid.8430.f0000 0001 2181 4888Medical School and University Hospital, Universidade Federal de Minas Gerais, Avenida Professor Alfredo Balena, 190, sala 246, Belo Horizonte, Minas Gerais Brazil
| | - Heloisa Reniers Vianna
- grid.419130.e0000 0004 0413 0953Faculdade de Ciências Médicas de Minas Gerais, University Hospital, Belo Horizonte, Minas Gerais Brazil
| | | | | | - José Miguel Chatkin
- grid.411379.90000 0001 2198 7041Hospital São Lucas da Pontifícia Universidade Católica do Rio Grande do Sul (PUC-RS), Porto Alegre, Rio Grande do Sul Brazil
| | - Júlia Drumond Parreiras de Morais
- grid.419130.e0000 0004 0413 0953Faculdade de Ciências Médicas de Minas Gerais, University Hospital, Belo Horizonte, Minas Gerais Brazil
| | | | - Juliana Machado Rugolo
- grid.410543.70000 0001 2188 478XHospital das Clínicas da Faculdade de Medicina de Botucatu, Botucatu, São Paulo Brazil
| | - Karen Brasil Ruschel
- grid.414871.f0000 0004 0491 7596Hospital Mãe de Deus, Porto Alegre, Rio Grande do Sul Brazil
| | | | | | - Liege Barella Zandoná
- grid.441846.b0000 0000 9020 9633Universidade Do Vale Do Taquari, Lajeado, Rio Grande do Sul Brazil
| | - Lílian Santos Pinheiro
- grid.411287.90000 0004 0643 9823Universidade Federal dos Vales do Jequitinhonha e Mucuri (UFVJM), Teófilo Otoni, Minas Gerais Brazil
| | - Liliane Souto Pacheco
- grid.488599.10000 0004 0481 6891Hospital Universitário de Santa Maria, Santa Maria, Rio Grande do Sul Brazil
| | - Luanna da Silva Monteiro Menezes
- grid.8430.f0000 0001 2181 4888Medical School and University Hospital, Universidade Federal de Minas Gerais, Avenida Professor Alfredo Balena, 190, sala 246, Belo Horizonte, Minas Gerais Brazil
| | | | | | - Luisa Elem Almeida Santos
- grid.441942.e0000 0004 0490 8155Centro Universitário de Patos de Minas, Patos de Minas, Minas Gerais Brazil
| | - Luiz Antonio Nasi
- grid.414856.a0000 0004 0398 2134Hospital Moinhos de Vento, Porto Alegre, Rio Grande do Sul Brazil
| | - Máderson Alvares de Souza Cabral
- grid.8430.f0000 0001 2181 4888Medical School and University Hospital, Universidade Federal de Minas Gerais, Avenida Professor Alfredo Balena, 190, sala 246, Belo Horizonte, Minas Gerais Brazil
| | - Maiara Anschau Floriani
- grid.414856.a0000 0004 0398 2134Hospital Moinhos de Vento, Porto Alegre, Rio Grande do Sul Brazil
| | - Maíra Dias Souza
- Hospital Metropolitano Odilon Behrens, Belo Horizonte, Minas Gerais Brazil
| | - Marcelo Carneiro
- Hospital Santa Cruz, Santa Cruz do Sul, Rio Grande do Sul Brazil
| | - Mariana Frizzo de Godoy
- grid.411379.90000 0001 2198 7041Hospital São Lucas da Pontifícia Universidade Católica do Rio Grande do Sul (PUC-RS), Porto Alegre, Rio Grande do Sul Brazil
| | | | | | | | | | | | | | - Neimy Ramos de Oliveira
- grid.452464.50000 0000 9270 1314Hospital Eduardo de Menezes, Belo Horizonte, Minas Gerais Brazil
| | | | - Pedro Ledic Assaf
- Hospital Metropolitano Doutor Célio de Castro, Belo Horizonte, Minas Gerais Brazil
| | | | | | | | - Roberta Pozza
- Hospital Tacchini, Bento Gonçalves, Rio Grande do Sul Brazil
| | | | | | | | | | | | | | | | | | - Tatiana Kurtz
- Hospital Santa Cruz, Santa Cruz do Sul, Rio Grande do Sul Brazil
| | | | | | | | | | - Carísi Anne Polanczyk
- Institute for Health Technology Assessment (IATS/ CNPq), Porto Alegre, Rio Grande do Sul Brazil
- grid.8532.c0000 0001 2200 7498Universidade Federal do Rio Grande do Sul, Porto Alegre, Rio Grande do Sul Brazil
| | - Milena Soriano Marcolino
- grid.8430.f0000 0001 2181 4888Medical School and University Hospital, Universidade Federal de Minas Gerais, Avenida Professor Alfredo Balena, 190, sala 246, Belo Horizonte, Minas Gerais Brazil
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20
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Lyons PG, Bhavani SV, Mody A, Bewley A, Dittman K, Doyle A, Windham SL, Patel TM, Raju BN, Keller M, Churpek MM, Calfee CS, Michelson AP, Kannampallil T, Geng EH, Sinha P. Hospital trajectories and early predictors of clinical outcomes differ between SARS-CoV-2 and influenza pneumonia. EBioMedicine 2022; 85:104295. [PMID: 36202054 PMCID: PMC9527494 DOI: 10.1016/j.ebiom.2022.104295] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Revised: 09/15/2022] [Accepted: 09/16/2022] [Indexed: 11/07/2022] Open
Abstract
BACKGROUND A comparison of pneumonias due to SARS-CoV-2 and influenza, in terms of clinical course and predictors of outcomes, might inform prognosis and resource management. We aimed to compare clinical course and outcome predictors in SARS-CoV-2 and influenza pneumonia using multi-state modelling and supervised machine learning on clinical data among hospitalised patients. METHODS This multicenter retrospective cohort study of patients hospitalised with SARS-CoV-2 (March-December 2020) or influenza (Jan 2015-March 2020) pneumonia had the composite of hospital mortality and hospice discharge as the primary outcome. Multi-state models compared differences in oxygenation/ventilatory utilisation between pneumonias longitudinally throughout hospitalisation. Differences in predictors of outcome were modelled using supervised machine learning classifiers. FINDINGS Among 2,529 hospitalisations with SARS-CoV-2 and 2,256 with influenza pneumonia, the primary outcome occurred in 21% and 9%, respectively. Multi-state models differentiated oxygen requirement progression between viruses, with SARS-CoV-2 manifesting rapidly-escalating early hypoxemia. Highly contributory classifier variables for the primary outcome differed substantially between viruses. INTERPRETATION SARS-CoV-2 and influenza pneumonia differ in presentation, hospital course, and outcome predictors. These pathogen-specific differential responses in viral pneumonias suggest distinct management approaches should be investigated. FUNDING This project was supported by NIH/NCATS UL1 TR002345, NIH/NCATS KL2 TR002346 (PGL), the Doris Duke Charitable Foundation grant 2015215 (PGL), NIH/NHLBI R35 HL140026 (CSC), and a Big Ideas Award from the BJC HealthCare and Washington University School of Medicine Healthcare Innovation Lab and NIH/NIGMS R35 GM142992 (PS).
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Affiliation(s)
- Patrick G. Lyons
- Department of Medicine, Washington University School of Medicine, St. Louis, MO, United States,Healthcare Innovation Lab, BJC HealthCare, St. Louis, MO, United States,Corresponding author at: Washington University School of Medicine, 660 South Euclid Avenue, MSC 8052-43-14, St. Louis, MO 63110, United States.
| | | | - Aaloke Mody
- Department of Medicine, Washington University School of Medicine, St. Louis, MO, United States
| | - Alice Bewley
- Department of Medicine, Washington University School of Medicine, St. Louis, MO, United States
| | - Katherine Dittman
- Department of Medicine, Washington University School of Medicine, St. Louis, MO, United States
| | - Aisling Doyle
- Department of Medicine, Washington University School of Medicine, St. Louis, MO, United States
| | - Samuel L. Windham
- Department of Medicine, Washington University School of Medicine, St. Louis, MO, United States
| | - Tej M. Patel
- Department of Medicine, Washington University School of Medicine, St. Louis, MO, United States
| | - Bharat Neelam Raju
- Department of Medicine, Washington University School of Medicine, St. Louis, MO, United States
| | - Matthew Keller
- Department of Medicine, Washington University School of Medicine, St. Louis, MO, United States
| | - Matthew M. Churpek
- Department of Medicine, University of Wisconsin School of Medicine, Madison, WI, United States
| | - Carolyn S. Calfee
- Department of Medicine, Division of Pulmonary, Critical Care, Allergy, and Sleep Medicine, University of California, San Francisco, CA, United States
| | - Andrew P. Michelson
- Department of Medicine, Washington University School of Medicine, St. Louis, MO, United States,Institute for Informatics, Washington University School of Medicine, St. Louis, MO, United States
| | - Thomas Kannampallil
- Institute for Informatics, Washington University School of Medicine, St. Louis, MO, United States,Department of Anesthesiology, Washington University School of Medicine, St. Louis, MO, United States
| | - Elvin H. Geng
- Department of Medicine, Washington University School of Medicine, St. Louis, MO, United States
| | - Pratik Sinha
- Department of Anesthesiology, Washington University School of Medicine, St. Louis, MO, United States
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21
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Quinn KL, Abdel‐Qadir H, Barrett K, Bartsch E, Beaman A, Biering‐Sørensen T, Colacci M, Cressman A, Detsky A, Gosset A, Lassen MH, Kandel C, Khaykin Y, Lapointe‐Shaw L, Lovblom E, MacFadden DR, Perkins B, Rothman KJ, Skaarup KG, Stall N, Tang T, Yarnell C, Zipursky J, Warkentin MT, Fralick M. Variation in the risk of death due to COVID-19: An international multicenter cohort study of hospitalized adults. J Hosp Med 2022; 17:793-802. [PMID: 36040111 PMCID: PMC9539016 DOI: 10.1002/jhm.12946] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Revised: 06/28/2022] [Accepted: 07/06/2022] [Indexed: 01/08/2023]
Abstract
BACKGROUND There is wide variation in mortality among patients hospitalized with COVID-19. Whether this is related to patient or hospital factors is unknown. OBJECTIVE To compare the risk of mortality for patients hospitalized with COVID-19 and to determine whether the majority of that variation was explained by differences in patient characteristics across sites. DESIGN, SETTING, AND PARTICIPANTS An international multicenter cohort study of hospitalized adults with laboratory-confirmed COVID-19 enrolled from 10 hospitals in Ontario, Canada and 8 hospitals in Copenhagen, Denmark between January 1, 2020 and November 11, 2020. MAIN OUTCOMES AND MEASURES Inpatient mortality. We used a multivariable multilevel regression model to compare the in-hospital mortality risk across hospitals and quantify the variation attributable to patient-level factors. RESULTS There were 1364 adults hospitalized with COVID-19 in Ontario (n = 1149) and in Denmark (n = 215). In Ontario, the absolute risk of in-hospital mortality ranged from 12.0% to 39.8% across hospitals. Ninety-eight percent of the variation in mortality in Ontario was explained by differences in the characteristics of the patients. In Denmark, the absolute risk of inpatients ranged from 13.8% to 20.6%. One hundred percent of the variation in mortality in Denmark was explained by differences in the characteristics of the inpatients. CONCLUSION There was wide variation in inpatient COVID-19 mortality across hospitals, which was largely explained by patient-level factors, such as age and severity of presenting illness. However, hospital-level factors that could have affected care, including resource availability and capacity, were not taken into account. These findings highlight potential limitations in comparing crude mortality rates across hospitals for the purposes of reporting on the quality of care.
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Affiliation(s)
- Kieran L. Quinn
- Department of Medicine, Sinai Health SystemUniversity of TorontoTorontoOntarioCanada
- Division of Internal Medicine, Temerty Faculty of MedicineUniversity of TorontoTorontoOntarioCanada
- Interdepartmental Division of Palliative Care, Sinai Health SystemUniversity of TorontoTorontoOntarioCanada
| | - Husam Abdel‐Qadir
- Department of Medicine, Division of CardiologyWomen's College HospitalTorontoOntarioCanada
- Department of MedicineUniversity Health NetworkTorontoOntarioCanada
| | - Kali Barrett
- Department of MedicineUniversity Health NetworkTorontoOntarioCanada
- Department of Medicine, Interdepartmental Division of Critical Care MedicineUniversity of TorontoTorontoOntarioCanada
| | - Emily Bartsch
- Department of Medicine, Sinai Health SystemUniversity of TorontoTorontoOntarioCanada
| | - Andrea Beaman
- Department of PharmacyTrillium Health PartnersMississaugaOntarioCanada
| | | | - Michael Colacci
- Department of MedicineUniversity of TorontoTorontoOntarioCanada
| | - Alex Cressman
- Division of Internal Medicine, Temerty Faculty of MedicineUniversity of TorontoTorontoOntarioCanada
| | - Allan Detsky
- Department of Medicine, Sinai Health SystemUniversity of TorontoTorontoOntarioCanada
| | - Alexi Gosset
- Department of MedicineUniversity of TorontoTorontoOntarioCanada
| | - Mats H. Lassen
- Department of MedicineUniversity of TorontoTorontoOntarioCanada
| | - Chris Kandel
- Department of MedicineMichael Garron HospitalTorontoOntarioCanada
| | - Yaariv Khaykin
- Department of MedicineSouthlake Regional Health CentreNewmarketOntarioCanada
| | | | - Erik Lovblom
- Department of Medicine, Lunenfeld‐Tanenbaum Research InstituteMount Sinai HospitalTorontoOntarioCanada
| | - Derek R. MacFadden
- Department of MedicineThe Ottawa Hospital Research InstituteOttawaOntarioCanada
| | - Bruce Perkins
- Department of MedicineUniversity Health NetworkTorontoOntarioCanada
| | - Kenneth J Rothman
- Department of Epidemiology, School of Public HealthBoston UniversityMassachusettsBostonUSA
| | | | - Nathan Stall
- Department of Medicine, Division of General Internal Medicine and GeriatricsSinai Health and the University Health NetworkTorontoOntarioCanada
| | - Terence Tang
- Department of Medicine, Interdepartmental Division of Critical Care MedicineUniversity of TorontoTorontoOntarioCanada
| | - Chris Yarnell
- Department of Medicine, Interdepartmental Division of Critical Care MedicineUniversity of TorontoTorontoOntarioCanada
| | - Jonathan Zipursky
- Department of MedicineSunnybrook Health Sciences CentreTorontoOntarioCanada
| | - Matthew T. Warkentin
- Department of Medicine, Lunenfeld‐Tanenbaum Research InstituteMount Sinai HospitalTorontoOntarioCanada
| | - Mike Fralick
- Department of Medicine, Sinai Health SystemUniversity of TorontoTorontoOntarioCanada
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22
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Aries P, Huet O, Balicchi J, Mathais Q, Estagnasie C, Martin-Lecamp G, Simon O, Morvan AC, Puech B, Subiros M, Blonde R, Boue Y. Characteristics and outcomes of SARS-COV 2 critically ill patients after emergence of the variant of concern 20H/501Y.V2: A comparative cohort study. Medicine (Baltimore) 2022; 101:e30816. [PMID: 36181037 PMCID: PMC9524525 DOI: 10.1097/md.0000000000030816] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/05/2023] Open
Abstract
There are currently no data regarding characteristics of critically ill patients with Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) variant of concern (VOC) 20H/501Y.V2. We therefore aimed to describe changes of characteristics in critically ill patients with Covid-19 between the first and the second wave when viral genome sequencing indicated that VOC was largely dominant in Mayotte Island (Indian Ocean). Consecutive patients with Covid-19 and over 18 years admitted in the unique intensive care unit (ICU) of Mayotte during wave 2 were compared with an historical cohort of patients admitted during wave 1. We performed a LR comparing wave 1 and wave 2 as outcomes. To complete analysis, we built a Random Forest model (RF), that is, a machine learning classification tool- using the same variable set as that of the LR. We included 156 patients, 41 (26.3%) and 115 (73.7%) belonging to the first and second waves respectively. Univariate analysis did not find difference in demographic data or in mortality. Our multivariate LR found that patients in wave 2 had less fever (absence of fever aOR 5.23, 95% confidence interval (CI) 1.89-14.48, p = .001) and a lower simplified acute physiology score (SAPS II) (aOR 0.95, 95% CI 0.91-0.99, p = .007) at admission; at 24 hours, the need of invasive mechanical ventilation was higher (aOR 3.49, 95% CI 0.98-12.51, p = .055) and pO2/FiO2 ratio was lower (aOR 0.99, 95 % CI 0.98-0.99, p = .03). Patients in wave 2 had also an increased risk of ventilator-associated pneumonia (VAP) (aOR 4.64, 95% CI 1.54-13.93, p = .006). Occurrence of VAP was also a key variable to classify patients between wave 1 and wave 2 in the variable importance plot of the RF model. Our data suggested that VOC 20H/501Y.V2 could be associated with a higher severity of respiratory failure at admission and a higher risk for developing VAP. We hypothesized that the expected gain in survival brought by recent improvements in critical care management could have been mitigated by increased transmissibility of the new lineage leading to admission of more severe patients. The immunological role of VOC 20H/501Y.V2 in the propensity for VAP requires further investigations.
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Affiliation(s)
- Philippe Aries
- Clermont-Tonnerre Military Teaching Hospital, Brest, France
- Department of Anesthesia and Surgical Intensive Care, Brest Teaching Hospital, Brest, France
- UFR of Medicine, University of Western Brittany, Brest, France
- *Correspondence: Philippe Aries, Clermont-Tonnerre Military Teaching Hospital, Brest, France (e-mail: )
| | - Olivier Huet
- Department of Anesthesia and Surgical Intensive Care, Brest Teaching Hospital, Brest, France
- UFR of Medicine, University of Western Brittany, Brest, France
| | - Julien Balicchi
- Regional Health Agency, Centre Kinga, Mamoudzou, Mayotte, France
| | - Quentin Mathais
- Department of Anesthesiology and Intensive Care, Military Hospital Sainte Anne, Toulon, France
| | | | | | - Olivier Simon
- Intensive Care Unit, Hospital of Southern Réunion, University Teaching Hospital of La Réunion, Saint-Pierre, Reunion Island, France
| | - Anne-Cécile Morvan
- Intensive Care Unit, Hospital of Western Réunion, Saint-Paul, Reunion Island, France
| | - Bérénice Puech
- Intensive Care Unit, Félix Guyon Hospital, University Teaching Hospital of La Réunion, Saint Denis, Reunion Island, France
| | - Marion Subiros
- French Public Health Agency in the Indian Ocean Region, Mamoudzou, Mayotte, France
| | - Renaud Blonde
- Intensive Care Unit, Mayotte Hospital, Mamoudzou, Mayotte, France
| | - Yvonnick Boue
- Intensive Care Unit, Mayotte Hospital, Mamoudzou, Mayotte, France
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Ramaswamy R, Ramaswamy V, Holly M, Bartels S, Barach P. Building local decision-making competencies during COVID-19: Accelerating the transition from learning healthcare systems to learning health communities. Learn Health Syst 2022; 7:e10337. [PMID: 36247203 PMCID: PMC9538137 DOI: 10.1002/lrh2.10337] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 08/01/2022] [Accepted: 08/08/2022] [Indexed: 12/15/2022] Open
Abstract
Introduction The persisting and evolving COVID-19 pandemic has made apparent that no singular policy of mitigation at a regional, national or global level has achieved satisfactory and universally acceptable results. In the United States, carefully planned and executed pandemic policies have been neither effective nor popular and COVID-19 risk management decisions have been relegated to individual citizens and communities. In this paper, we argue that a more effective approach is to equip and strengthen community coalitions to become local learning health communities (LLHCs) that use data over time to make adaptive decisions that can optimize the equity and well-being in their communities. Methods We used data from the North Carolina (NC) county and zip code levels from May to August 2020 to demonstrate how a LLHC could use statistical process control (SPC) charts and simple statistical analysis to make local decisions about how to respond to COVID-19. Results We found many patterns of COVID-19 progression at the local (county and zip code) levels during the same time period within the state that were completely different from the aggregate NC state level data used for policy making. Conclusions Systematic approaches to learning from local data to support effective decisions have promise well beyond the current pandemic. These tools can help address other complex public health issues, and advance outcomes and equity. Building this capacity requires investment in data infrastructure and the strengthening of data competencies in community coalitions to better interpret data with limited need for advanced statistical expertise. Additional incentives that build trust, support data transparency, encourage truth-telling and promote meaningful teamwork are also critical. These must be carefully designed, contextually appropriate and multifaceted to motivate citizens to create and sustain an effective learning system that works for their communities.
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Affiliation(s)
- Rohit Ramaswamy
- Cincinnati Children's Hospital Medical CenterJames M Anderson Center for Health Systems ExcellenceCincinnatiOhioUSA
| | | | - Margaret Holly
- Department of Health Policy and ManagementUniversity of North Carolina at Chapel Hill Gillings School of Global Public HealthChapel HillNorth CarolinaUSA
| | - Sophia Bartels
- Department of Health BehaviorUniversity of North Carolina at Chapel Hill Gillings School of Global Public HealthChapel HillNorth CarolinaUSA
| | - Paul Barach
- College of Population HealthThomas Jefferson UniversityPhiladelphiaPennsylvaniaUSA
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Sunderraj A, Cho C, Cai X, Gupta S, Mehta R, Isakova T, Leaf DE, Srivastava A. Modulation of the Association Between Age and Death by Risk Factor Burden in Critically Ill Patients With COVID-19. Crit Care Explor 2022; 4:e0755. [PMID: 36050992 PMCID: PMC9426819 DOI: 10.1097/cce.0000000000000755] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023] Open
Abstract
Older age is a key risk factor for adverse outcomes in critically ill patients with COVID-19. However, few studies have investigated whether preexisting comorbidities and acute physiologic ICU factors modify the association between age and death. DESIGN Multicenter cohort study. SETTING ICUs at 68 hospitals across the United States. PATIENTS A total of 5,037 critically ill adults with COVID-19 admitted to ICUs between March 1, 2020, and July 1, 2020. INTERVENTIONS None. MEASUREMENTS AND MAIN RESULTS The primary exposure was age, modeled as a continuous variable. The primary outcome was 28-day inhospital mortality. Multivariable logistic regression tested the association between age and death. Effect modification by the number of risk factors was assessed through a multiplicative interaction term in the logistic regression model. Among the 5,037 patients included (mean age, 60.9 yr [± 14.7], 3,179 [63.1%] male), 1,786 (35.4%) died within 28 days. Age had a nonlinear association with 28-day mortality (p for nonlinearity <0.001) after adjustment for covariates that included demographics, preexisting comorbidities, acute physiologic ICU factors, number of ICU beds, and treatments for COVID-19. The number of preexisting comorbidities and acute physiologic ICU factors modified the association between age and 28-day mortality (p for interaction <0.001), but this effect modification was modest as age still had an exponential relationship with death in subgroups stratified by the number of risk factors. CONCLUSIONS In a large population of critically ill patients with COVID-19, age had an independent exponential association with death. The number of preexisting comorbidities and acute physiologic ICU factors modified the association between age and death, but age still had an exponential association with death in subgroups according to the number of risk factors present. Additional studies are needed to identify the mechanisms underpinning why older age confers an increased risk of death in critically ill patients with COVID-19.
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Affiliation(s)
- Ashwin Sunderraj
- Graduate Medical Education, Northwestern University Feinberg School of Medicine, Chicago, IL
| | - Chloe Cho
- Undergraduate Medical Education, Northwestern University, Evanston, IL
| | - Xuan Cai
- Division of Nephrology & Hypertension, Center for Translational Metabolism and Health, Institute of Public Health and Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL
| | - Shruti Gupta
- Department of Medicine, Division of Renal Medicine, Brigham and Women's Hospital, Boston, MA
| | - Rupal Mehta
- Division of Nephrology & Hypertension, Center for Translational Metabolism and Health, Institute of Public Health and Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL
| | - Tamara Isakova
- Division of Nephrology & Hypertension, Center for Translational Metabolism and Health, Institute of Public Health and Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL
| | - David E Leaf
- Department of Medicine, Division of Renal Medicine, Brigham and Women's Hospital, Boston, MA
| | - Anand Srivastava
- Division of Nephrology & Hypertension, Center for Translational Metabolism and Health, Institute of Public Health and Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL
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Bottle A, Faitna P, Brett S, Aylin P. Factors associated with, and variations in, COVID-19 hospital death rates in England's first two waves: observational study. BMJ Open 2022; 12:e060251. [PMID: 35772812 PMCID: PMC9247323 DOI: 10.1136/bmjopen-2021-060251] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
Abstract
OBJECTIVES To assess patient-level and hospital-level predictors of death and variation in death rates following admission for COVID-19 in England's first two waves after accounting for random variation. To quantify the correlation between hospitals' first and second wave death rates. DESIGN Observational study using administrative data. SETTING Acute non-specialist hospitals in England. PARTICIPANTS All patients admitted with a primary diagnosis of COVID-19. PRIMARY AND SECONDARY OUTCOMES In-hospital death. RESULTS Hospital Episode Statistics (HES) data were extracted for all acute hospitals in England for COVID-19 admissions from March 2020 to March 2021. In wave 1 (March to July 2020), there were 74 484 admissions and 21 883 deaths (crude rate 29.4%); in wave 2 (August 2020 to March 2021), there were 165 642 admissions and 36 040 deaths (21.8%). Wave 2 patients were younger, with more hypertension and obesity but lower rates of other comorbidities. Mortality improved for all ages; in wave 2, it peaked in December 2020 at 24.2% (lower than wave 1's peak) but halved by March 2021. In multiple multilevel modelling combining HES with hospital-level data from Situational Reports, wave 2 and wave 1 variables significantly associated with death were mostly the same. The median odds ratio for wave 1 was just 1.05 and for wave 2 was 1.07. At 99.8% control limits, 3% of hospitals were high and 7% were low funnel plot outliers in wave 1; these figures were 9% and 12% for wave 2. Four hospitals were (low) outliers in both waves. The correlation between hospitals' adjusted mortality rates between waves was 0.45 (p<0.0001). Length of stay was similar in each wave. CONCLUSIONS England's first two COVID-19 waves were similar regarding predictors and moderate interhospital variation. Despite the challenges, variation in death rates and length of stay between hospitals was modest and might be accounted for by unobserved patient factors.
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Affiliation(s)
- Alex Bottle
- School of Public Health, Imperial College London, London, UK
| | - Puji Faitna
- School of Public Health, Imperial College London, London, UK
| | - Stephen Brett
- Department of Surgery and Cancer, Imperial College London, London, UK
- Critical Care, Imperial College Healthcare NHS Trust, London, UK
| | - Paul Aylin
- School of Public Health, Imperial College London, London, UK
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First-line Vasopressor Use in Septic Shock and Route of Administration: An Epidemiologic Study. Ann Am Thorac Soc 2022; 19:1713-1721. [PMID: 35709214 DOI: 10.1513/annalsats.202203-222oc] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
RATIONALE Norepinephrine is a first-line agent for treatment of hypotension in septic shock. However, its frequency of use, and potential barriers to its use are unclear. OBJECTIVES To evaluate the frequency of use of norepinephrine in septic shock, to identify potential barriers to its use, and to evaluate trends in use of vasopressors over time. METHODS Retrospective population-based cohort study of patients with septic shock in Alberta, Canada between July 1, 2012 and December 31, 2018. The primary outcome was receipt of a first-line vasopressor other than norepinephrine ("non-norepinephrine vasopressor"). Predictors of receiving a non-norepinephrine vasopressor were assessed using a multivariable-adjusted, multilevel logistic regression model with intensive care unit (ICU) as a random effect. RESULTS Among 6343 patients with septic shock, the proportion of patients receiving non-norepinephrine vasopressors as first-line treatment decreased steadily from 11.5% in 2012 to 3.0% in 2018. Two factors most strongly associated with their receipt were having peripheral intravenous access only (adjusted odds ratio (aOR) 6.15, 95% confidence interval (CI) 4.58-8.26, p<0.001) and year of admission (aOR 0.74 per year after 2012, 95% CI 0.69-0.80, p<0.001). Other factors that had associations after adjustment included admission to a non-teaching hospital (aOR 2.19, 95% CI 1.23-3.89, p=0.007), admission to a coronary care unit (aOR 2.56, 95% CI 1.001-6.54, p=0.05), SOFA score (aOR 0.92 per unit increase, 95% CI 0.88-0.96, p<0.001) and heart rate (aOR 0.92 per 10 beat per minute increase, 95% CI 0.87-0.97, p=0.002). CONCLUSIONS In a large cohort of patients in Alberta, Canada, we found a steady decrease in use of first-line vasopressors other than norepinephrine in septic shock. The strongest factor associated with their use was the presence of only peripheral venous access, suggesting this may still be considered a barrier to administration of norepinephrine.
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Reply: COVID-19 Mortality Differences: Patient-related Data and ICU Load Are Prerequisites. Ann Am Thorac Soc 2022; 19:1624-1625. [PMID: 35522443 PMCID: PMC9447382 DOI: 10.1513/annalsats.202204-313le] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
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Xia Y, Ma H, Buckeridge DL, Brisson M, Sander B, Chan A, Verma A, Ganser I, Kronfli N, Mishra S, Maheu-Giroux M. Mortality trends and lengths of stay among hospitalized COVID-19 patients in Ontario and Québec (Canada): a population-based cohort study of the first three epidemic waves. Int J Infect Dis 2022; 121:1-10. [PMID: 35477050 PMCID: PMC9040412 DOI: 10.1016/j.ijid.2022.04.048] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 03/08/2022] [Accepted: 04/20/2022] [Indexed: 12/15/2022] Open
Abstract
Background Epidemics of COVID-19 strained hospital resources. We describe temporal trends in mortality risk and length of stays in hospital and intensive care units (ICUs) among patients with COVID-19 hospitalized through the first three epidemic waves in Canada. Methods We used population-based provincial hospitalization data from the epicenters of Canada's epidemics (Ontario and Québec). Adjusted estimates were obtained using marginal standardization of logistic regression models, accounting for patient-level and hospital-level determinants. Results Using all hospitalizations from Ontario (N = 26,538) and Québec (N = 23,857), we found that unadjusted in-hospital mortality risks peaked at 31% in the first wave and was lowest at the end of the third wave at 6–7%. This general trend remained after adjustments. The odds of in-hospital mortality in the highest patient load quintile were 1.2-fold (95% CI: 1.0–1.4; Ontario) and 1.6-fold (95% CI: 1.3–1.9; Québec) that of the lowest quintile. Mean hospital and ICU length of stays decreased over time but ICU stays were consistently higher in Ontario than Québec. Conclusions In-hospital mortality risks and length of ICU stays declined over time despite changing patient demographics. Continuous population-based monitoring of patient outcomes in an evolving epidemic is necessary for health system preparedness and response.
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Affiliation(s)
- Yiqing Xia
- Department of Epidemiology and Biostatistics, School of Population and Global Health, McGill University, Montréal, QC, Canada
| | - Huiting Ma
- MAP Centre for Urban Health Solutions, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada
| | - David L Buckeridge
- Department of Epidemiology and Biostatistics, School of Population and Global Health, McGill University, Montréal, QC, Canada
| | - Marc Brisson
- Département de médecine sociale et préventive, Faculté de médecine, Université Laval, Québec, QC, Canada
| | - Beate Sander
- Management and Evaluation (IHPME), Dalla Lana School of Public Health, Institute of Health Policy, University of Toronto; Toronto Health Economics and Technology Assessment (THETA) collaborative, University Health Network; Public Health Ontario, Toronto, ON, Canada; ICES, Toronto, ON, Canada
| | - Adrienne Chan
- Management and Evaluation (IHPME), Dalla Lana School of Public Health, Institute of Health Policy, University of Toronto; Division of Infectious Diseases, Department of Medicine, University of Toronto, Toronto, ON, Canada
| | - Aman Verma
- Department of Epidemiology and Biostatistics, School of Population and Global Health, McGill University, Montréal, QC, Canada
| | - Iris Ganser
- Department of Epidemiology and Biostatistics, School of Population and Global Health, McGill University, Montréal, QC, Canada
| | - Nadine Kronfli
- Centre for Outcomes Research and Evaluation (CORE), Research Institute of the McGill University Health Centre, Montréal, QC, Canada; Department of Medicine, Division of Infectious Diseases and Chronic Viral Illness Service, McGill University Health Centre, Montréal, QC, Canada
| | - Sharmistha Mishra
- Division of Infectious Diseases, Department of Medicine, University of Toronto, Toronto, ON, Canada; MAP Centre for Urban Health Solutions, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada; Management and Evaluation (IHPME), Dalla Lana School of Public Health, Institute of Health Policy, University of Toronto; Institute of Medical Sciences, University of Toronto
| | - Mathieu Maheu-Giroux
- Department of Epidemiology and Biostatistics, School of Population and Global Health, McGill University, Montréal, QC, Canada.
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Mortality of Mechanically Ventilated COVID-19 Patients in Traditional versus Expanded ICUs in NY. Ann Am Thorac Soc 2022; 19:1346-1354. [PMID: 35213292 PMCID: PMC9353963 DOI: 10.1513/annalsats.202106-705oc] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
RATIONALE During the first wave of the coronavirus disease 2019 (COVID-19) pandemic in New York City, the number of mechanically ventilated COVID-19 patients rapidly surpassed the capacity of traditional Intensive Care Units (ICUs), resulting in health systems utilizing other areas as expanded ICUs to provide critical care. OBJECTIVES To evaluate the mortality of patients admitted to expanded ICUs compared with those admitted to traditional ICUs. METHODS Multicenter, retrospective, cohort study of mechanically ventilated patients with COVID-19 admitted to the ICUs at 11 Northwell Health hospitals in the greater New York City area between March 1, 2020 and April 30, 2020. MEASUREMENTS In-hospital mortality up to 28 days after intubation of COVID-19 patients. RESULTS Among 1,966 mechanically ventilated patients with COVID-19, 1,198 (61%) died within 28 days after intubation, 46 (2%) were transferred to other hospitals outside of the Northwell Health system, 722 (37%) survived in the hospital until 28 days or were discharged after recovery. The risk of mortality of mechanically ventilated patients admitted to expanded ICUs was not different from those admitted to traditional ICUs (HR, 1.07; 95% CI, 0.95-1.20; p = 0.28), while hospital occupancy for critically ill patients itself was associated with increased risk of mortality (HR, 1.28; 95% CI, 1.12-1.45; p < 0.001). CONCLUSIONS Although increased hospital occupancy for critically ill patients itself was associated with increased mortality, the risk of 28-day in-hospital mortality of mechanically ventilated patients with COVID-19 who were admitted to expanded ICUs was not different from those admitted to traditional ICUs.
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Camporota L, Cronin JN, Busana M, Gattinoni L, Formenti F. Pathophysiology of coronavirus-19 disease acute lung injury. Curr Opin Crit Care 2022; 28:9-16. [PMID: 34907979 PMCID: PMC8711311 DOI: 10.1097/mcc.0000000000000911] [Citation(s) in RCA: 40] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
PURPOSE OF REVIEW More than 230 million people have tested positive for severe acute respiratory syndrome-coronavirus-2 infection globally by September 2021. The infection affects primarily the function of the respiratory system, where ∼20% of infected individuals develop coronavirus-19 disease (COVID-19) pneumonia. This review provides an update on the pathophysiology of the COVID-19 acute lung injury. RECENT FINDINGS In patients with COVID-19 pneumonia admitted to the intensive care unit, the PaO2/FiO2 ratio is typically <26.7 kPa (200 mmHg), whereas lung volume appears relatively unchanged. This hypoxaemia is likely determined by a heterogeneous mismatch of pulmonary ventilation and perfusion, mainly associated with immunothrombosis, endothelialitis and neovascularisation. During the disease, lung weight, elastance and dead space can increase, affecting respiratory drive, effort and dyspnoea. In some severe cases, COVID-19 pneumonia may lead to irreversible pulmonary fibrosis. SUMMARY This review summarises the fundamental pathophysiological features of COVID-19 in the context of the respiratory system. It provides an overview of the key clinical manifestations of COVID-19 pneumonia, including gas exchange impairment, altered pulmonary mechanics and implications of abnormal chemical and mechanical stimuli. It also critically discusses the clinical implications for mechanical ventilation therapy.
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Affiliation(s)
- Luigi Camporota
- Centre for Human and Applied Physiological Sciences, School of Basic and Medical Biosciences, King's College London
- Intensive Care Unit, Guy's and St Thomas' NHS Foundation Trust
| | - John N Cronin
- Centre for Human and Applied Physiological Sciences, School of Basic and Medical Biosciences, King's College London
- Department of Anaesthetics, Royal Brompton and Harefield, part of Guy's and St. Thomas' NHS Foundation Trust, London, UK
| | - Mattia Busana
- Department of Anesthesiology, University Medical Center of Göttingen, Göttingen, Germany
| | - Luciano Gattinoni
- Department of Anesthesiology, University Medical Center of Göttingen, Göttingen, Germany
| | - Federico Formenti
- Centre for Human and Applied Physiological Sciences, School of Basic and Medical Biosciences, King's College London
- Nuffield Division of Anaesthetics, University of Oxford, Oxford, UK
- Department of Biomechanics, University of Nebraska Omaha, Omaha, Nebraska, USA
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Sinha P, Furfaro D, Cummings MJ, Abrams D, Delucchi K, Maddali MV, He J, Thompson A, Murn M, Fountain J, Rosen A, Robbins-Juarez SY, Adan MA, Satish T, Madhavan M, Gupta A, Lyashchenko AK, Agerstrand C, Yip NH, Burkart KM, Beitler JR, Baldwin MR, Calfee CS, Brodie D, O'Donnell MR. Latent Class Analysis Reveals COVID-19-related Acute Respiratory Distress Syndrome Subgroups with Differential Responses to Corticosteroids. Am J Respir Crit Care Med 2021; 204:1274-1285. [PMID: 34543591 PMCID: PMC8786071 DOI: 10.1164/rccm.202105-1302oc] [Citation(s) in RCA: 123] [Impact Index Per Article: 41.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
Rationale Two distinct subphenotypes have been identified in acute respiratory distress syndrome (ARDS), but the presence of subgroups in ARDS associated with coronavirus disease (COVID-19) is unknown. Objectives To identify clinically relevant, novel subgroups in COVID-19–related ARDS and compare them with previously described ARDS subphenotypes. Methods Eligible participants were adults with COVID-19 and ARDS at Columbia University Irving Medical Center. Latent class analysis was used to identify subgroups with baseline clinical, respiratory, and laboratory data serving as partitioning variables. A previously developed machine learning model was used to classify patients as the hypoinflammatory and hyperinflammatory subphenotypes. Baseline characteristics and clinical outcomes were compared between subgroups. Heterogeneity of treatment effect for corticosteroid use in subgroups was tested. Measurements and Main Results From March 2, 2020, to April 30, 2020, 483 patients with COVID-19–related ARDS met study criteria. A two-class latent class analysis model best fit the population (P = 0.0075). Class 2 (23%) had higher proinflammatory markers, troponin, creatinine, and lactate, lower bicarbonate, and lower blood pressure than class 1 (77%). Ninety-day mortality was higher in class 2 versus class 1 (75% vs. 48%; P < 0.0001). Considerable overlap was observed between these subgroups and ARDS subphenotypes. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RT-PCR cycle threshold was associated with mortality in the hypoinflammatory but not the hyperinflammatory phenotype. Heterogeneity of treatment effect to corticosteroids was observed (P = 0.0295), with improved mortality in the hyperinflammatory phenotype and worse mortality in the hypoinflammatory phenotype, with the caveat that corticosteroid treatment was not randomized. Conclusions We identified two COVID-19–related ARDS subgroups with differential outcomes, similar to previously described ARDS subphenotypes. SARS-CoV-2 PCR cycle threshold had differential value for predicting mortality in the subphenotypes. The subphenotypes had differential treatment responses to corticosteroids.
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Affiliation(s)
- Pratik Sinha
- Department of Anesthesiology, Washington University Medical School, Saint Louis, Missouri
| | - David Furfaro
- Division of Pulmonary, Allergy, and Critical Care Medicine
| | | | - Darryl Abrams
- Division of Pulmonary, Allergy, and Critical Care Medicine
| | | | | | - June He
- Department of Anesthesiology, Washington University Medical School, Saint Louis, Missouri
| | | | - Michael Murn
- Division of Pulmonary, Allergy, and Critical Care Medicine
| | | | | | | | - Matthew A Adan
- Vagelos College of Physicians and Surgeons, Columbia University, New York, New York
| | - Tejus Satish
- Vagelos College of Physicians and Surgeons, Columbia University, New York, New York
| | | | | | - Alexander K Lyashchenko
- Department of Pathology and Cell Biology, Columbia University Irving Medical Center and NewYork-Presbyterian Hospital, New York, New York
| | | | - Natalie H Yip
- Division of Pulmonary, Allergy, and Critical Care Medicine
| | | | | | | | - Carolyn S Calfee
- Department of Medicine, Division of Pulmonary, Critical Care, Allergy and Sleep Medicine.,Cardiovascular Research Institute, and.,Department of Anesthesia, University of California, San Francisco, San Francisco, California; and
| | - Daniel Brodie
- Division of Pulmonary, Allergy, and Critical Care Medicine
| | - Max R O'Donnell
- Division of Pulmonary, Allergy, and Critical Care Medicine.,Department of Epidemiology, Mailman School of Public Health, and
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Brogan M, Ross MJ. The Impact of Chronic Kidney Disease on Outcomes of Patients with COVID-19 Admitted to the Intensive Care Unit. Nephron Clin Pract 2021; 146:67-71. [PMID: 34634789 PMCID: PMC8678261 DOI: 10.1159/000519530] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Accepted: 09/04/2021] [Indexed: 12/18/2022] Open
Abstract
CONTEXT Coronavirus disease 2019 (COVID-19) disproportionately impacts patients with chronic kidney disease (CKD), especially those with kidney failure requiring replacement therapy (KFRT). Patients with KFRT have increased risk of developing COVID-19, and though initial reports suggested that mortality of these patients in the intensive care unit (ICU) setting is prohibitively high, those studies suffered from significant limitations. Subject of Review: The Study of the Treatment and Outcomes in Critically Ill Patients With COVID-19 (STOP-COVID) is a multicenter cohort study that enrolled adults with COVID-19 admitted to ICUs in 68 medical centers across the USA. STOP-COVID investigators compared characteristics at the time of ICU admission and clinical outcomes in 143 patients with KFRT, 521 with nondialysis-dependent CKD (ND-CKD), and 3,600 patients without CKD. Patients with KFRT were less likely to have typical COVID-19 symptoms but more likely to have altered mental status at the time of ICU admission and were less likely to require mechanical ventilation during hospitalization than those without kidney disease. Approximately, 50% of patients with KFRT and ND-CKD died within 28 days of ICU admission, and in fully adjusted models, patients with KFRT and ND-CKD had 1.41- and 1.25-fold higher risk of 28-day mortality than those without CKD. Patients with KFRT and ND-CKD were also less likely to receive emerging therapies for COVID-19 than those without CKD. Second Opinion: This study provides important new data demonstrating differences in clinical presentation in patients with KFRT and ND-CKD with COVID-19. Alhough patients with severe CKD had higher mortality than those without CKD, approximately half survived after 28 days, demonstrating that patients with COVID-19 and severe CKD can benefit from ICU care. The markedly lower use of emerging COVID-19 treatments in patients with severe CKD highlights the need to include these patients in clinical trials of new COVID-19 therapies and for clinicians to ensure equal access to care in patients with severe CKD and COVID-19.
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Affiliation(s)
- Maureen Brogan
- Department of Medicine, Division of Nephrology, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, New York, USA
| | - Michael J. Ross
- Department of Medicine, Division of Nephrology, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, New York, USA
- Department of Development and Molecular Biology, Albert Einstein College of Medicine, Bronx, New York, USA
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da Silva Ramos FJ, de Freitas FGR, Machado FR. Sepsis in patients hospitalized with coronavirus disease 2019: how often and how severe? Curr Opin Crit Care 2021; 27:474-479. [PMID: 34292175 PMCID: PMC8452249 DOI: 10.1097/mcc.0000000000000861] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
PURPOSE OF REVIEW To discuss why severe COVID-19 should be considered sepsis and how co-infection and secondary infection can aggravate this condition and perpetuate organ dysfunction leading to high mortality rates. RECENT FINDINGS In severe COVID-19, there is both direct viral toxicity and dysregulated host response to infection. Although both coinfection and/or secondary infection are present, the latest is of greater concern mainly in resource-poor settings. Patients with severe COVID-19 present a phenotype of multiorgan dysfunction that leads to death in an unacceptable high percentage of the patients, with wide variability around the world. Similarly to endemic sepsis, the mortality of COVID-19 critically ill patients is higher in low-income and middle-income countries as compared with high-income countries. Disparities, including hospital strain, resources limitations, higher incidence of healthcare-associated infections (HAI), and staffing issues could in part explain this variability. SUMMARY The high mortality rates of critically ill patients with severe COVID-19 disease are not only related to the severity of patient disease but also to modifiable factors, such as the ICU strain, HAI incidence, and organizational aspects. Therefore, HAI prevention and the delivery of best evidence-based care for these patients to avoid additional damage is important. Quality improvement interventions might help in improving outcomes mainly in resource-limited settings.
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Budinger GS, Misharin AV, Ridge KM, Singer BD, Wunderink RG. Distinctive features of severe SARS-CoV-2 pneumonia. J Clin Invest 2021; 131:149412. [PMID: 34263736 PMCID: PMC8279580 DOI: 10.1172/jci149412] [Citation(s) in RCA: 47] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
The coronavirus disease 2019 (COVID-19) pandemic is among the most important public health crises of our generation. Despite the promise of prevention offered by effective vaccines, patients with severe COVID-19 will continue to populate hospitals and intensive care units for the foreseeable future. The most common clinical presentation of severe COVID-19 is hypoxemia and respiratory failure, typical of the acute respiratory distress syndrome (ARDS). Whether the clinical features and pathobiology of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pneumonia differ from those of pneumonia secondary to other pathogens is unclear. This uncertainty has created variability in the application of historically proven therapies for ARDS to patients with COVID-19. We review the available literature and find many similarities between patients with ARDS from pneumonia attributable to SARS-CoV-2 versus other respiratory pathogens. A notable exception is the long duration of illness among patients with COVID-19, which could result from its unique pathobiology. Available data support the use of care pathways and therapies proven effective for patients with ARDS, while pointing to unique features that might be therapeutically targeted for patients with severe SARS-CoV-2 pneumonia.
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Akoumianaki E, Jonkman A, Sklar MC, Georgopoulos D, Brochard L. A rational approach on the use of extracorporeal membrane oxygenation in severe hypoxemia: advanced technology is not a panacea. Ann Intensive Care 2021; 11:107. [PMID: 34250563 PMCID: PMC8273031 DOI: 10.1186/s13613-021-00897-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Accepted: 06/30/2021] [Indexed: 12/16/2022] Open
Abstract
Veno-venous extracorporeal membrane oxygenation (ECMO) is a helpful intervention in patients with severe refractory hypoxemia either because mechanical ventilation cannot ensure adequate oxygenation or because lung protective ventilation is not feasible. Since ECMO is a highly invasive procedure with several, potentially devastating complications and its implementation is complex and expensive, simpler and less invasive therapeutic options should be first exploited. Low tidal volume and driving pressure ventilation, prone position, neuromuscular blocking agents and individualized ventilation based on transpulmonary pressure measurements have been demonstrated to successfully treat the vast majority of mechanically ventilated patients with severe hypoxemia. Veno-venous ECMO has a place in the small portion of severely hypoxemic patients in whom these strategies fail. A combined analysis of recent ARDS trials revealed that ECMO was used in only 2.15% of patients (n = 145/6736). Nevertheless, ECMO use has sharply increased in the last decade, raising questions regarding its thoughtful use. Such a policy could be harmful both for patients as well as for the ECMO technique itself. This narrative review attempts to describe together the practical approaches that can be offered to the sickest patients before going to ECMO, as well as the rationale and the limitations of ECMO. The benefit and the drawbacks associated with ECMO use along with a direct comparison with less invasive therapeutic strategies will be analyzed.
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Affiliation(s)
- Evangelia Akoumianaki
- Department of Intensive Care Medicine, University Hospital of Heraklion, Medical School, University of Crete, Heraklion, Greece
| | - Annemijn Jonkman
- Keenan Research Centre, Li Ka Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada.,Interdepartmental Division of Critical Care Medicine, University of Toronto, Toronto, ON, Canada
| | - Michael C Sklar
- Keenan Research Centre, Li Ka Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada.,Interdepartmental Division of Critical Care Medicine, University of Toronto, Toronto, ON, Canada
| | - Dimitris Georgopoulos
- Department of Intensive Care Medicine, University Hospital of Heraklion, Medical School, University of Crete, Heraklion, Greece
| | - Laurent Brochard
- Keenan Research Centre, Li Ka Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada. .,Interdepartmental Division of Critical Care Medicine, University of Toronto, Toronto, ON, Canada.
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Teja B, Wunsch H. Pinpointing the Cause of Variation in Mortality in COVID-19. Am J Respir Crit Care Med 2021; 204:381-382. [PMID: 34139143 PMCID: PMC8480249 DOI: 10.1164/rccm.202105-1244ed] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Affiliation(s)
- Bijan Teja
- University of Toronto, 7938, Department of Anesthesiology and Pain Medicine & Interdepartmental Division of Critical Care Medicine, Toronto, Ontario, Canada.,St. Michael's Hospital, Department of Anesthesiology, Toronto, Ontario, Canada
| | - Hannah Wunsch
- University of Toronto, 7938, Critical Care Medicine, Toronto, Ontario, Canada.,University of Toronto, 7938, Anesthesia, Toronto, Ontario, Canada;
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Sinha P, Calfee CS. Immunotherapy in COVID-19: why, who, and when? THE LANCET RESPIRATORY MEDICINE 2021; 9:549-551. [PMID: 34015326 PMCID: PMC8128672 DOI: 10.1016/s2213-2600(21)00232-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Accepted: 05/11/2021] [Indexed: 12/21/2022]
Affiliation(s)
- Pratik Sinha
- Division of Clinical and Translational Research and Division of Critical Care, Department of Anesthesiology, Washington University School of Medicine, St Louis, MO 63110, USA.
| | - Carolyn S Calfee
- Department of Medicine, Division of Pulmonary, Critical Care, Allergy and Sleep Medicine, and Cardiovascular Research Institute, University of California San Francisco, San Francisco, CA, USA
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