A Framework for Rationing Ventilators and Critical Care Beds During the COVID-19 Pandemic

White, Douglas B.; Lo, Bernard

doi:10.1001/jama.2020.5046

Cited by 660 publications

(729 citation statements)

References 9 publications

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“…The Toward A COVID-19 score is designed to be used within an overall rationing approach when critical care resources are scarce (2)(3)(4)(5). It provides an earlier and conceptually superior prediction of outcomes compared to SOFA.…”

Section: Resultsmentioning

confidence: 99%

Toward a Covid-19 Score-Risk Assessments and Registry

Guillamet¹,

Guillamet²,

Kramer³

et al. 2020

Preprint

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Importance Critical care resources like ventilators, used to manage the current COVID-19 pandemic, are potentially inadequate. Established triage standards and guidelines may not contain the most appropriate severity assessment and outcome prediction models.Objectives Develop a draft pandemic specific triage assessment score for the current COVID-19 pandemic. Design a website where initial Toward a COVID-19 Scores (TACS) can be quickly calculated and used to compare various treatment strategies. Create a TACS Registry where data and outcomes for suspected and confirmed COVID-19 patients can be recorded. Use the TACS Registry to develop an influenza epidemic specific database and score for use in future respiratory based epidemics.Design, Setting, Participants Retrospective analysis of 3,301 ICU admissions with respiratory failure admitted to 41 U.S. Intensive Care Units from 2015-19. Independent external validation on 1,175 similar ICU Admissions using identical entry criteria from Barnes Jewish Hospital (BJH), Washington University from 2016-2019.Main Outcomes TACS was created with 16 readily available predictive variables for risk assessment of hospital mortality 24 hours after ICU Admission and the need for prolonged assisted mechanical ventilation (PAMV) ( > 96 hours) at 24-and 48-hours post ICU admission.Results TACS achieved an Area Under the Curve (AUC) for hospital mortality after 24 hours of 0.80 in the development dataset; 0.81 in the internal validation dataset. At a probability of 50% hospital mortality, positive predictive value (PPV) was 0.55, negative predictive value (NPV) 0.89; sensitivity 22%, specificity 97%. For PAMV after 24 hours, the AUC was 0.84 in the development dataset, 0.81 in the validation dataset. For PAMV after 48 hours, the AUC was 0.82 in the development dataset, 0.78 in the validation dataset. In the external validation the AUC for TACS was 0.76 +/-0.024. We launched a website that is scaled for mobile device use ( https://covid19score.azurewebsites.net/) that provides open access to a user-friendly TACS Calculator for all predictions. We also designed a voluntary TACS Registry for collection of data and outcomes on ICU Admissions with COVID-19.

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Section: Resultsmentioning

confidence: 99%

Toward a Covid-19 Score-Risk Assessments and Registry

Guillamet¹,

Guillamet²,

Kramer³

et al. 2020

Preprint

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“…For the intensive care application considered here, the tool may also be used at a later stage of the pandemic in order to understand when elective cardiac surgery may safely resume following any projected reduction in COVID-19 intensive care occupancy. Further development of the model may also unlock the ability to assess the implication of different intensive care admission criteria on aggregate measures of mortality (as suggested in Utley et al, 2011 andWhite &Lo, 2020). In such case the model may be used to understand the effects of rejecting intensive care admissions from patients of cohorts known to have negligible survival likelihood, in the interests of maintaining available beds for those known to have more favourable chances.…”

Section: Discussionmentioning

confidence: 99%

COVID-19 scenario modelling for the mitigation of capacity-dependent deaths in intensive care: computer simulation study

Wood

McWilliams

Bourdeaux

et al. 2020

Preprint

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Background. Managing healthcare demand and capacity is especially difficult in the context of the COVID-19 pandemic, where limited intensive care resources can be overwhelmed by a large number of cases requiring admission in a short space of time. If patients are unable to access this specialist resource, then death is a likely outcome. The aim of this study is to estimate the extent to which such capacity-dependent deaths can be mitigated through demand-side initiatives involving nonpharmaceutical interventions and supply-side measures to increase surge capacity or reduce length of stay. Methods.A stochastic discrete event simulation model is developed to represent the key dynamics of the intensive care admissions process for COVID-19 patients. Model inputs are aligned to levers available to planners with key outputs including duration of time at maximum capacity (to inform workforce requirements), peak daily deaths (for mortuary planning), and total deaths (as an ultimate marker of intervention efficacy). The model -freely available -is applied to the COVID-19 response at a large hospital in England for which the effect of a number of possible interventions are simulated.Results. Capacity-dependent deaths are closely associated with both the nature and effectiveness of non-pharmaceutical interventions and availability of intensive care beds. For the hospital considered, results suggest that capacity-dependent deaths can be reduced five-fold through a combination of isolation policies, a doubling of bed capacity, and 25% reduced length of stay. Conclusions.Without treatment or vaccination there is little that can be done to reduce deaths occurring when patients have otherwise been treated in the most appropriate hospital setting. Healthcare planners should therefore focus on minimising the capacity-dependent deaths that are within their influence.

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“…Although protocols may vary depending on resource, scarcity, and setting, several criteria should be considered in their development. 12,[22][23][24][25][26][27][28] Preferably multiple criteria will be integrated into an allocation protocol since no single element incorporates every applicable moral consideration. 29 Allocations frameworks with particular relevance include (and summarized in Table 1):…”

Section: General Considerations For the Allocation Of Scarce Resourcesmentioning

confidence: 99%

The Ethics of Creating a Resource Allocation Strategy During the COVID-19 Pandemic

et al. 2020

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The coronavirus disease 2019 pandemic has affected nearly every aspect of medicine and raises numerous moral dilemmas for clinicians. Foremost of these quandaries is how to delineate and implement crisis standards of care and, specifically, how to consider how health care resources should be distributed in times of shortage. We review basic principles of disaster planning and resource stewardship with ethical relevance for this and future public health crises, explore the role of illness severity scoring systems and their limitations and potential contribution to health disparities, and consider the role for exceptionally resource-intensive interventions. We also review the philosophical and practical underpinnings of crisis standards of care and describe historical approaches to scarce resource allocation to offer analysis and guidance for pediatric clinicians. Particular attention is given to the impact on children of this endeavor. Although few children have required hospitalization for symptomatic infection, children nonetheless have the potential to be profoundly affected by the strain on the health care system imposed by the pandemic and should be considered prospectively in resource allocation frameworks.

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A Framework for Rationing Ventilators and Critical Care Beds During the COVID-19 Pandemic

Cited by 660 publications

References 9 publications

Toward a Covid-19 Score-Risk Assessments and Registry

Toward a Covid-19 Score-Risk Assessments and Registry

COVID-19 scenario modelling for the mitigation of capacity-dependent deaths in intensive care: computer simulation study

The Ethics of Creating a Resource Allocation Strategy During the COVID-19 Pandemic

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