scholarly journals COVID‐19 and critical care capacity: Can we mitigate demand?

Respirology ◽  
2021 ◽  
Author(s):  
Matthew Byrne ◽  
Timothy E. Scott ◽  
Jonathan Sinclair ◽  
Nachiappan Chockalingam
Keyword(s):  
Critical Care ◽  
Care Capacity

Bolstering Medical Education to Enhance Critical Care Capacity in Cambodia

2015 ◽  
Vol 12 (4) ◽  
pp. 491-497 ◽  
Author(s):  
Tyler J. Albert ◽  
Thomas Fassier ◽  
Meng Chhuoy ◽  
Youttiroung Bounchan ◽  
Sokhak Tan ◽  
...  
Keyword(s):  
Medical Education ◽  
Critical Care ◽  
Care Capacity

scholarly journals Confronting COVID-19: Surging Critical Care Capacity in Italy

2020 ◽  
Author(s):  
Jose Manuel Rodriguez Llanes ◽  
Rafael Castro Delgado ◽  
Morten Gram Pedersen ◽  
Pedro Arcos Gonzalez ◽  
Matteo Meneghini
Keyword(s):  
Critical Care ◽  
Care Capacity

scholarly journals Development and external validation of a prognostic tool for COVID-19 critical disease

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0242953
Author(s):  
Daniel S. Chow ◽  
Justin Glavis-Bloom ◽  
Jennifer E. Soun ◽  
Brent Weinberg ◽  
Theresa Berens Loveless ◽  
...  
Keyword(s):  
Critical Care ◽  
Prognostic Model ◽  
Validation Cohort ◽  
External Validation ◽  
Vital Signs ◽  
Clinical Tool ◽  
Derivation Cohort ◽  
Laboratory Values ◽  
Increased Risk ◽  
Care Capacity

Background The rapid spread of coronavirus disease 2019 (COVID-19) revealed significant constraints in critical care capacity. In anticipation of subsequent waves, reliable prediction of disease severity is essential for critical care capacity management and may enable earlier targeted interventions to improve patient outcomes. The purpose of this study is to develop and externally validate a prognostic model/clinical tool for predicting COVID-19 critical disease at presentation to medical care. Methods This is a retrospective study of a prognostic model for the prediction of COVID-19 critical disease where critical disease was defined as ICU admission, ventilation, and/or death. The derivation cohort was used to develop a multivariable logistic regression model. Covariates included patient comorbidities, presenting vital signs, and laboratory values. Model performance was assessed on the validation cohort by concordance statistics. The model was developed with consecutive patients with COVID-19 who presented to University of California Irvine Medical Center in Orange County, California. External validation was performed with a random sample of patients with COVID-19 at Emory Healthcare in Atlanta, Georgia. Results Of a total 3208 patients tested in the derivation cohort, 9% (299/3028) were positive for COVID-19. Clinical data including past medical history and presenting laboratory values were available for 29% (87/299) of patients (median age, 48 years [range, 21–88 years]; 64% [36/55] male). The most common comorbidities included obesity (37%, 31/87), hypertension (37%, 32/87), and diabetes (24%, 24/87). Critical disease was present in 24% (21/87). After backward stepwise selection, the following factors were associated with greatest increased risk of critical disease: number of comorbidities, body mass index, respiratory rate, white blood cell count, % lymphocytes, serum creatinine, lactate dehydrogenase, high sensitivity troponin I, ferritin, procalcitonin, and C-reactive protein. Of a total of 40 patients in the validation cohort (median age, 60 years [range, 27–88 years]; 55% [22/40] male), critical disease was present in 65% (26/40). Model discrimination in the validation cohort was high (concordance statistic: 0.94, 95% confidence interval 0.87–1.01). A web-based tool was developed to enable clinicians to input patient data and view likelihood of critical disease. Conclusions and relevance We present a model which accurately predicted COVID-19 critical disease risk using comorbidities and presenting vital signs and laboratory values, on derivation and validation cohorts from two different institutions. If further validated on additional cohorts of patients, this model/clinical tool may provide useful prognostication of critical care needs.

scholarly journals Confronting COVID-19: Surging critical care capacity in Italy

2020 ◽  
Author(s):  
Jose M Rodriguez-Llanes ◽  
Rafael Castro Delgado ◽  
Morten Gram Pedersen ◽  
Pedro Arcos González ◽  
Matteo Meneghini
Keyword(s):  
Critical Care ◽  
Care Capacity

scholarly journals Africa’s critical care capacity before COVID-19

2020 ◽  
pp. 162-164
Author(s):  
ET Ayebale ◽  
NJ Kassebaum ◽  
AM Roche ◽  
BM Biccard
Keyword(s):  
Critical Care ◽  
Surgical Outcomes ◽  
Outcomes Study ◽  
Novel Coronavirus ◽  
Care Capacity

Critical care capabilities in affluent countries have been overwhelmed by the 2019 novel coronavirus disease (COVID-19) pandemic. Data from the African Surgical Outcomes Study (ASOS)1 suggests that this critical care crisis will be significantly worse in Africa.

UK COVID-19 response will gain more urgency

2020 ◽  
Keyword(s):  
Critical Care ◽  
Scientific Community ◽  
Behavioural Change ◽  
Testing Strategy ◽  
Content Type ◽  
The United Kingdom ◽  
Hospital Patients ◽  
The Government ◽  
Care Capacity ◽  
The Impact

Subject UK COVID-19 response. Significance The United Kingdom has been pursuing a policy of gradual escalation to deal with the impact of the COVID-19 pandemic. This, in combination with its testing strategy which is now restricted to in-hospital patients only, departs from the WHO’s advice and contrasts with the more drastic social distancing approaches taken by governments elsewhere in Europe. London's strategy has generated significant backlash from parts of the scientific community. The government now accepts COVID-19 spread is faster than expected, and yesterday announced school and university closures. Impacts The full consequences of the government’s initially slower approach will become increasingly apparent over the upcoming weeks. The NHS is set to be overwhelmed by the surge of COVID-19 patients, especially its critical-care capacity. Confused communication and frequent changes in measures will hamper future efforts to bring about public behavioural change.

scholarly journals Critical Care Demand and Intensive Care Supply for Patients in Japan with COVID-19 at the Time of the State of Emergency Declaration in April 2020: A Descriptive Analysis

Medicina ◽  
2020 ◽  
Vol 56 (10) ◽  
pp. 530
Author(s):  
Yosuke Fujii ◽  
Kiichi Hirota
Keyword(s):  
Intensive Care Unit ◽  
Mechanical Ventilation ◽  
Critical Care ◽  
Intensive Care ◽  
Extracorporeal Membrane Oxygenation ◽  
Descriptive Analysis ◽  
State Of Emergency ◽  
Medical Institutions ◽  
Number Of Patients ◽  
Care Capacity

Background and objectives: The coronavirus disease 2019 (COVID-19) pandemic is overwhelming Japan’s intensive care capacity. This study aimed to determine the number of patients with COVID-19 who required intensive care and to compare the numbers with Japan’s intensive care capacity. Materials and Methods: Publicly available datasets were used to obtain the number of confirmed patients with COVID-19 undergoing mechanical ventilation and extracorporeal membrane oxygenation (ECMO) between 15 February and 19 July 2020 to determine and compare intensive care unit (ICU) and attending bed needs for patients with COVID-19, and to estimate peak ICU demands in Japan. Results: During the epidemic peak in late April, 11,443 patients (1.03/10,000 adults) had been infected, 373 patients (0.034/10,000 adults) were in ICU, 312 patients (0.028/10,000 adults) were receiving mechanical ventilation, and 62 patients (0.0056/10,000 adults) were under ECMO per day. At the peak of the epidemic, the number of infected patients was 651% of designated beds, and the number of patients requiring intensive care was 6.0% of ICU beds, 19.1% of board-certified intensivists, and 106% of designated medical institutions in Japan. Conclusions: The number of critically ill patients with COVID-19 continued to rise during the pandemic, exceeding the number of designated beds but not exceeding ICU capacity.

scholarly journals Projecting the transmission dynamics of SARS-CoV-2 through the postpandemic period

Science ◽  
2020 ◽  
Vol 368 (6493) ◽  
pp. 860-868 ◽  
Author(s):  
Stephen M. Kissler ◽  
Christine Tedijanto ◽  
Edward Goldstein ◽  
Yonatan H. Grad ◽  
Marc Lipsitch
Keyword(s):  
Critical Care ◽  
Time Series Data ◽  
Herd Immunity ◽  
The United States ◽  
Series Data ◽  
Social Distancing ◽  
Serological Studies ◽  
Cross Immunity ◽  
Care Capacity ◽  
Pandemic Wave

It is urgent to understand the future of severe acute respiratory syndrome–coronavirus 2 (SARS-CoV-2) transmission. We used estimates of seasonality, immunity, and cross-immunity for human coronavirus OC43 (HCoV-OC43) and HCoV-HKU1 using time-series data from the United States to inform a model of SARS-CoV-2 transmission. We projected that recurrent wintertime outbreaks of SARS-CoV-2 will probably occur after the initial, most severe pandemic wave. Absent other interventions, a key metric for the success of social distancing is whether critical care capacities are exceeded. To avoid this, prolonged or intermittent social distancing may be necessary into 2022. Additional interventions, including expanded critical care capacity and an effective therapeutic, would improve the success of intermittent distancing and hasten the acquisition of herd immunity. Longitudinal serological studies are urgently needed to determine the extent and duration of immunity to SARS-CoV-2. Even in the event of apparent elimination, SARS-CoV-2 surveillance should be maintained because a resurgence in contagion could be possible as late as 2024.

Augmentation of hospital critical care capacity after bioterrorist attacks or epidemics: Recommendations of the Working Group on Emergency Mass Critical Care‡

2005 ◽  
Vol 33 (10) ◽  
pp. E2393 ◽  
Author(s):  
Lewis Rubinson ◽  
Jennifer B. Nuzzo ◽  
Daniel S. Talmor ◽  
Tara O’Toole ◽  
Bradley R. Kramer ◽  
...  
Keyword(s):  
Critical Care ◽  
Working Group ◽  
Care Capacity

scholarly journals Assembly Line ICU: what the Long Shops taught us about managing surge capacity for COVID-19

2020 ◽  
Vol 9 (4) ◽  
pp. e001117
Author(s):  
Callum Oakley ◽  
Craig Pascoe ◽  
Daivd Balthazor ◽  
Davinia Bennett ◽  
Nandan Gautam ◽  
...  
Keyword(s):  
Critical Care ◽  
Critical Care Unit ◽  
Assembly Line ◽  
Tertiary Hospital ◽  
Multiple Organ ◽  
Adaptive Responses ◽  
Whole Process ◽  
The Uk ◽  
Care Capacity ◽  
And Training

ObjectivesTo safely expand and adapt the normal workings of a large critical care unit in response to the COVID-19 pandemic.MethodsIn April 2020, UK health systems were challenged to expand critical care capacity rapidly during the first wave of the COVID-19 pandemic so that they could accommodate patients with respiratory and multiple organ failure. Here, we describe the preparation and adaptive responses of a large critical care unit to the oncoming burden of disease. Our changes were similar to the revolution in manufacturing brought about by ‘Long Shops’ of 1853 when Richard Garrett and Sons of Leiston started mass manufacture of traction engines. This innovation broke the whole process into smaller parts and increased productivity. When applied to COVID-19 preparations, an assembly line approach had the advantage that our ICU became easily scalable to manage an influx of additional staff as well as the increase in admissions. Healthcare professionals could be replaced in case of absence and training focused on a smaller number of tasks.ResultsCompared with the equivalent period in 2019, the ICU provided 30.9% more patient days (2599 to 3402), 1845 of which were ventilated days (compared with 694 in 2019, 165.8% increase) while time from first referral to ICU admission reduced from 193.8±123.8 min (±SD) to 110.7±76.75 min (±SD). Throughout, ICU maintained adequate capacity and also accepted patients from neighbouring hospitals. This was done by managing an additional 205 doctors (70% increase), 168 nurses who had previously worked in ICU and another 261 nurses deployed from other parts of the hospital (82% increase).Our large tertiary hospital ensured a dedicated non-COVID ICU was staffed and equipped to take regional emergency referrals so that those patients requiring specialist surgery and treatment were treated throughout the COVID-19 pandemic.ConclusionsWe report how the challenge of managing a huge influx of patients and redeployed staff was met by deconstructing ICU care into its constituent parts. Although reported from the largest colocated ICU in the UK, we believe that this offers solutions to ICUs of all sizes and may provide a generalisable model for critical care pandemic surge planning.

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