Viral pandemics can quickly overwhelm health system capacity. When a rapid increase in patients with coronavirus disease 2019 (COVID-19) led to ICU bed shortages in Northern Italy1 and elsewhere, clinicians were forced to make difficult ICU resource allocation decisions. Similar surges are now being seen in other parts of the world, including the United States.
What Frameworks Are Available to Support Resource Allocation Decisions?
Ethical frameworks for stewardship of scarce healthcare resources2 share the common dual aims of saving the most lives and maximizing gains in posttreatment length of life.3 However, fulfilling these goals requires clinicians to estimate both the patient’s probability of surviving the acute illness and life expectancy after the episode of critical illness. These estimations are particularly challenging in patients with underlying chronic respiratory diseases, and practical implementation frameworks are lacking.
Recently, several Canadian provinces published frameworks for ICU resource allocation that feature three levels of surge planning.4 Each surge level provides progressively more strict exclusion criteria for ICU admission (and continued ICU care in those already receiving it), as follows:
Level 1—Patients with >80% expected mortality during or in the 6 to 12 months after critical illness
Level 2—Patients with >50% expected mortality during or in the 6 to 12 months after critical illness
Level 3—Patients with >30% expected mortality during or in the 6 to 12 months after critical illness
To help clinicians to approximate these predicted mortalities in patients with chronic respiratory diseases, the Canadian Thoracic Society (CTS) produced a position statement describing corresponding characteristics in COPD, pulmonary fibrosis, cystic fibrosis (CF), and pulmonary arterial hypertension (PAH). This commentary summarizes those findings in an FAQ format. The full position statement,5 including detailed explanations, rationale, and approach for predicting mortalities can be found online.6
How Did We Estimate Predicted Mortalities in Patients With Chronic Respiratory Conditions?
Disease-based expert groups from across Canada prepared criteria for each respiratory condition independently. Criteria were informed by published survival data, and where possible, complemented by (mostly indirect) data to estimate the impact of critical illness. Accordingly, they are primarily based on expert opinion and should be individualized and supplemented with clinical judgment.
Descriptions for each more severe mortality threshold supersede those in the less severe threshold, such that level 3 descriptions should practically be applied to predict >30% to 50% mortality and level 2 descriptions to predict >50% to 80% mortality.
Cystic Fibrosis
Which cystic fibrosis patients have >80% predicted mortality during or in the 6 to 12 months after critical illness (level 1)?
Patients with FEV1 of <20% predicted when measured at the time of clinical stability fulfill this criterion.
Which cystic fibrosis patients have >50% predicted mortality during or in the 6 to 12 months after critical illness (level 2)?
Patients with FEV1 of <20% predicted when measured at the time of clinical stability also fall into this category.
Which cystic fibrosis patients have >30% predicted mortality during or in the 6 to 12 months after critical illness (level 3)?
Patients with FEV1 of <30% predicted when measured at the time of clinical stability are in this category.
Estimates were derived from the Canadian CF Registry, which captures data on more than 99% of Canadian CF patients. Although FEV 1 of <20% corresponds to an approximately 50% probability of death/transplantation at 1 year (level 2), this criterion was also recommended for level 1, given the additional expected mortality impact of the critical illness itself. The level 3 cutoff was based on the fact that approximately 30% of Canadian CF patients with FEV1 of <30% will have died or received a transplant by 2 years.
Pulmonary Fibrosis
Which pulmonary fibrosis patients have >80% predicted mortality during or in the 6 to 12 months after critical illness (level 1)?
This level includes patients with:
FVC <50%-60%; OR
Diffusing capacity of lung for carbon monoxide (Dlco) <30%-40% predicted; OR
Chronic supplemental oxygen use at home for >12 hours/day; OR
Echocardiographic evidence of pulmonary hypertension (estimated right ventricular systolic pressure >50 mm Hg)a; OR
Rapidly progressive diseaseb; OR
History of acute exacerbation of ILD in the last 12 months.
Which pulmonary fibrosis patients have >50% predicted mortality during or in the 6 to 12 months after critical illness (level 2)?
Level 2 also comprises patients with:
FVC <50%-60%; OR
Dlco <30%-40% predicted; OR
Chronic supplemental oxygen use at home for >12 hours/day; OR
Echocardiographic evidence of pulmonary hypertension (estimated right ventricular systolic pressure >50 mm Hg)a; OR
Rapidly progressive diseaseb (>10% decline in FVCover the last 6 months associated with pronounced radiographic and clinical deterioration); OR
History of AE-ILD in the last 12 months.
aProminent right ventricular dilation and hypokinesis preceding COVID-19 infection should also be considered. A conservative measure of 50 mm Hg was selected, given heterogeneous and predominantly retrospective supporting evidence and high prevalence of risk factors for group 2 pulmonary hypertension in ILD patients.
b10% decline in FVC over the last 6 months associated with pronounced radiographic and clinical deterioration.
Which pulmonary fibrosis patients have >30% predicted mortality during or in the 6 to 12 months after critical illness (level 3)?
Patients with the following criteria fall under level 3:
FVC <75%; OR
Dlco <55% predicted
The gender, age, physiology (GAP) prediction model is the most widely validated prognostic tool used in clinical practice.7 A ≥10% reduction in FVC over 6 to 12 months also predicts acute exacerbation, hospitalization, and death. Median survival after acute exacerbation of idiopathic pulmonary fibrosis (IPF; AE-IPF) is 3 to 4 months.8 Our criteria were derived from literature describing long-term IPF outcomes, predisposing factors, and clinical course of AE-IPF, and risk of poor outcomes after surgical lung biopsy.9 , 10 Because we could not identify clear criteria for >50% predicted mortality (level 2), we reiterated criteria for >80% predicted mortality (level 1). Level 3 criteria were validated in the GAP model, predicting a relatively low probability of 1-year mortality.7
COPD
Which COPD patients have >80% predicted mortality during or in the 6 to 12 months after critical illness (level 1)?
Patients with the following criteria fall under level 1:
FEV1 <50% predicted; AND
Chronic hypoxemia (Pao 2 ≤ 55 mm Hg) or chronic hypercapnia (Paco 2 > 55 mm Hg); AND
Clinical frailty score (CFS) of ≥ 7.
Which COPD patients have >50% predicted mortality during or in the 6 to 12 months after critical illness (level 2)?
Patients with the following conditions are considered to be in level 2:
FEV1 < 50% predicted; AND
CFS ≥ 6.
Which COPD patients have >30% predicted mortality during or in the 6 to 12 months after critical illness (level 3)?
Patients meeting the following criteria are considered to be in level 3:
FEV1 < 50% predicted; AND
≥2 hospitalizations within the last 12 months for an acute exacerbation of COPD; AND
CFS ≥ 5.
We recommend against relying solely on pulmonary function and dyspnea severity (eg, modified Medical Research Council dyspnea scale) to make triage decisions. COPD patients with documented chronic hypoxemia and hypercapnia have higher 1-year mortality.11 , 12 A history of frequent acute exacerbations of COPD is a strong predictor of mortality.13 , 14 The CFS is a validated measure of frailty that has been shown to predict mortality in the year after ICU admission.15 Accordingly, we recommend using the CFS to improve prognostication for all surge categories in COPD patients.
PAH
Which PAH patients have >80% predicted mortality during or in the 6 to 12 months after critical illness (level 1)?
PAH patients who are considered to be in level 1 include those with a high-risk profile (Registry to Evaluate Early and Long-Term Pulmonary Arterial Hypertension [REVEAL] 2.0 score ≥ 9 or high-risk European Respiratory Society/European Society of Cardiology [ESC/ERS] score) while on optimal therapy (at least two oral medications and a parenteral prostacyclin, if eligible).
Which PAH patients have >50% predicted mortality during or in the 6 to 12 months after critical illness (level 2)?
Patients with these conditions are considered to be in level 2:
An intermediate risk profile (REVEAL 2.0 score 7-8 or intermediate-risk ESC/ERS score) while on optimal therapy; AND
Age ≥ 75 years; AND
Either a recent hospitalization for worsening PAH/right heart failure in the past 3 months or the presence of other significant comorbidities (especially chronic renal failure)
Which PAH patients have >30% predicted mortality during or in the 6-12 months after critical illness (level 3)?
Patients meeting the following criteria are considered to be in level 3:
An intermediate-risk profile (REVEAL 2.0 score 7-8 or intermediate-risk ESC/ERS score) while on optimal therapy; AND
Age < 75 years AND
Either a recent hospitalization for worsening PAH/right heart failure in the past 3 months or the presence of other significant comorbidities (especially chronic renal failure)
Poor prognostic factors in PAH include systemic sclerosis origin of PAH; older age; male sex; severe symptoms (New York Heart Association Class III-IV); reduced exercise capacity; comorbidities (eg, renal dysfunction); severe right ventricular dysfunction; and hospitalizations for right heart failure.16 Available risk prediction tools include the U.S. REVEAL 2.0 risk score16 and the ESC/ERS risk assessment tool.17 We supplemented mortality predictions from these tools with estimates of the effects of critical illness. Our recommendations apply only to PAH (not to pulmonary hypertension groups 2-5).
Given that the pandemic is a rapidly evolving situation, the CTS plans to update this guidance as new information becomes available. We recommend monitoring the CTS website6 for updates.
Acknowledgments
Financial/nonfinancial disclosures: The authors have reported to CHEST the following: S. G. reports grants and personal fees from Ontario Lung Association (OLA), outside the submitted work. J. Ba. reports grants from the Canadian Institutes of Health Research (CIHR), Social Sciences and Humanities Research Council of Canada, OLA, outside the submitted work. K. R. C. reports grants from Amgen, grants and personal fees from Astra-Zeneca, grants and personal fees from Boehringer-Ingelheim (BI), personal fees from CSL Behring, grants and personal fees from GlaxoSmithKline (GSK), grants and personal fees from Grifols, grants and personal fees from Kamada, personal fees from Takeda, grants from Vertex, grants and personal fees from Mereo Biopharma, grants and personal fees from Novartis, grants and personal fees from Sanofi, grants and personal fees from Regeneron, outside the submitted work. J. Bo. reports grants from Canadian Respiratory Research Network, grants and personal fees from AZ, grants and personal fees from BI, grants and personal fees from GSK, grants from CIHR, grants from Respiratory Health Network of the Fonds de la recherche en santé du Québec, personal fees from CTS, personal fees from CHEST, grants and personal fees from Grifols, grants and personal fees from Novartis, grants and personal fees from Trudell, outside the submitted work. A. G. reports grants from CIHR and is a member of the Board of Directors of the Physicians’ Services Inc. Foundation in Ontario. J. G. reports grants from Bayer and Actelion/Janssen, outside the submitted work. A member of steering committee for clinical trials sponsored by United Therapeutics and Actelion/Janssen, outside the submitted work. N. H. reports grants and personal fees from Actelion, Bayer, BI, and Hoffman-La Roche Ltd. and personal fees from Novartis, outside the submitted work. P. H. reports grants from CIHR, Lung Association of Nova Scotia, Nova Scotia Health Authority Research Fund, AZ, BI, Cyclomedica, Grifols, Respivant, and Vertex; and personal fees from Actelion, AZ, BI, GSK, Novartis, Sanofi-Aventis, and Trudell, outside the submitted work. M. K. reports grants and personal fees from BI and Hoffman-La Roche Ltd., GSK, Gilead and Prometic, grants from Actelion, Respivert, Alkermes and Pharmaxis, and personal fees from Genoa, Indalo and Third Pole, outside the submitted work. S. M. reports grants from Heart and Stroke Foundation of Ontario/Canada, OLA, Academic Medical Organization of Southwest Ontario, CIHR, Bellerophon, Eiger, Reata, and United Therapeutics; grants and personal fees from Actelion-Janssen and Bayer, outside the submitted work. L. M. reports grants from AZ, Bayer, Janssen, and personal and speaker fees from AZ, Bayer, Janssen, Novartis, outside the submitted work. S. P. reports grants from AZ, Janssen and Resverlogix, outside the submitted work. A. L. S. reports grants from Cystic Fibrosis Canada, US Cystic Fibrosis Foundation; and personal fees from Vertex Pharmaceuticals and Cystic Fibrosis Canada, outside of the submitted work. J. S. reports grants and personal fees from Actelion, Johnson & Johnson, Bayer, Unither, and United Therapeutics, outside the submitted work. D. E. T. reports grants from Bayer, BI, Celtaxis, Corbus, Proteostasis, Spyryx and Vertex Pharmaceuticals and personal fees from Horizon, Proteostasis, Vertex, outside the submitted work. J. Wa. reports personal fees from GSK, grant from Fisher & Paykel, outside the submitted work. J. We. reports grants from CIHR, Lung Association of Alberta and NWT, Heart & Stroke Foundation of Canada, European Respiratory Society, CTS, Canadian Vascular Network; grants and personal fees from Actelion; grants and personal fees from Janssen; personal fees from Novartis and Bayer, outside the submitted work. M. B. reports personal fees and grants from AZ, BI, GSK, Novartis, Sanofi-Genzyme, CIHR, Alberta Innovates Health Solutions, outside the submitted work. None declared (N. T. V.)
Other contributions: We acknowledge the Lung Health Foundation for supporting the COPD estimates in this statement.
References
- 1.Rosenbaum L. Facing Covid-19 in Italy: ethics, logistics, and therapeutics on the epidemic’s front line. N Engl J Med. 2020;382(20):1873–1875. doi: 10.1056/NEJMp2005492. [DOI] [PubMed] [Google Scholar]
- 2.Daugherty Biddison E.L., Faden R., Gwon H.S. Too many patients…a framework to guide statewide allocation of scarce mechanical ventilation during disasters. Chest. 2019;155(4):848–854. doi: 10.1016/j.chest.2018.09.025. [DOI] [PubMed] [Google Scholar]
- 3.Emanuel E.J., Persad G., Upshur R. Fair allocation of scarce medical resources in the time of Covid-19. N Engl J Med. 2020;382(21):2049–2055. doi: 10.1056/NEJMsb2005114. [DOI] [PubMed] [Google Scholar]
- 4.Ministry of Health and Long-Term Care. Government of Ontario. Clinical Triage Protocol for Major Surge in COVID Pandemic. https://www.corhealthontario.ca/Clinical-Triage-Protocol-for-Major-Surge-in-COVID-Pandemic-March-28-2020.pdf Posted on March 28, 2020.
- 5.Gupta S., Batt J., Bourbeau J. Position statement from the Canadian Thoracic Society (CTS) on clinical triage thresholds in respiratory disease patients in the event of a major surge during the Covid-19 pandemic. Can J Respir Crit Care Sleep Med. 2020 doi: 10.1080/24745332.2020.1769436. [Published online ahead of print May 11, 2020] [DOI] [Google Scholar]
- 6.https://cts-sct.ca/covid-19/ Accessed June 29, 2020.
- 7.Ley B., Ryerson C.J., Vittinghoff E. A multidimensional index and staging system for idiopathic pulmonary fibrosis. Ann Intern Med. 2012;156(10):684–691. doi: 10.7326/0003-4819-156-10-201205150-00004. [DOI] [PubMed] [Google Scholar]
- 8.Kolb M., Bondue B., Pesci A. Acute exacerbations of progressive fibrosing interstitial lung disease. Eur Respir Rev. 2018;27:180071. doi: 10.1183/16000617.0071-2018. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Johannson K.A., Kolb M., Fell C.D. Evaluation of patients with fibrotic interstitial lung disease: A Canadian Thoracic Society position statement. Can J Respir Crit Care Sleep Med. 2017;1(3):133–141. [Google Scholar]
- 10.Ley B., Bradford W.Z., Vittinghoff E. Predictors of mortality poorly predict common measures of disease progression in idiopathic pulmonary fibrosis. Am J Respir Crit Care Med. 2016;194(6):711. doi: 10.1164/rccm.201508-1546OC. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Coleta K.D., Silveira L.V., Lima D.F. Predictors of first-year survival in patients with advanced COPD treated using long-term oxygen therapy. Respir Med. 2008;102(4):512–518. doi: 10.1016/j.rmed.2007.12.003. [DOI] [PubMed] [Google Scholar]
- 12.Ahmadi Z., Bornefalk-Hermansson A., Franklin K.A. Hypo- and hypercapnia predict mortality in oxygen-dependent chronic obstructive pulmonary disease: a population-based prospective study. Respir Res. 2014;15(1):30. doi: 10.1186/1465-9921-15-30. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Hurst J.R., Vestbo J., Anzueto A. Susceptibility to exacerbation in chronic obstructive pulmonary disease. N Engl J Med. 2010;363(12):1128–1138. doi: 10.1056/NEJMoa0909883. [DOI] [PubMed] [Google Scholar]
- 14.Mullerova H., Maselli D.J., Locantore N. Hospitalized exacerbations of COPD: risk factors and outcomes in the ECLIPSE cohort. Chest. 2015;147(4):999–1007. doi: 10.1378/chest.14-0655. [DOI] [PubMed] [Google Scholar]
- 15.Bagshaw S.M., Stelfox H.T., McDermid R.C. Association between frailty and short- and long-term outcomes among critically ill patients: a multicentre prospective cohort study. CMAJ. 2014;186(2):E95–E102. doi: 10.1503/cmaj.130639. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Benza R.L., Gomberg-Maitland M., Elliott C.G. Predicting survival in patients with pulmonary arterial hypertension: the REVEAL Risk Score Calculator 2.0 and comparison with ESC/ERS-based risk assessment strategies. Chest. 2019;156(2):323–337. doi: 10.1016/j.chest.2019.02.004. [DOI] [PubMed] [Google Scholar]
- 17.Galiè N., Humbert M., Vachiery J.-L. 2015 ESC/ERS Guidelines for the diagnosis and treatment of pulmonary hypertension: The Joint Task Force for the Diagnosis and Treatment of Pulmonary Hypertension of the European Society of Cardiology (ESC) and the European Respiratory Society (ERS): Endorsed by: Association for European Paediatric and Congenital Cardiology (AEPC), International Society for Heart and Lung Transplantation (ISHLT) Eur Respir J. 2015;46(4):903–975. doi: 10.1183/13993003.01032-2015. [DOI] [PubMed] [Google Scholar]