COVID-19 vaccination status should not be used in triage tie-breaking

This article discusses the triage response to the COVID-19 delta variant surge of 2021. One issue that distinguishes the delta wave from earlier surges is that by the time it became the predominant strain in the USA in July 2021, safe and effective vaccines against COVID-19 had been available for all US adults for several months. We consider whether healthcare professionals and triage committees would have been justified in prioritising patients with COVID-19 who are vaccinated above those who are unvaccinated in first-order or second-order triage. Given that lack of evidence for a correlation between short-term survival and vaccination, we argue that using vaccination status during first-order triage would be inconsistent with accepted triage standards. We then turn to notions of procedural fairness, equity and desert to argue that that there is also a lack of justification for using vaccination status in second-order triage. In planning for future surges, we recommend that medical institutions base their triage decisions on principles meant to save the most lives, minimise inequity and protect the public’s trust, which for the time being would not be served by the inclusion of vaccination status.

Who should get the scarce ICU bed? The US public’s view on triage in the time of COVID-19

ObjectiveTo determine the relative importance members of the US public place on different patient attributes in triage decisions about who should receive the last available intensive care unit (ICU) bed.MethodsA discrete choice experiment was conducted with a nationally representative sample of 2000 respondents from the YouGov internet panel of US households. Respondents chose which of three hypothetical patients with COVID-19 should receive an ICU bed if only one were available. The three patients differed in age, gender, Alzheimer’s-like disability and probability of survival if the patient received the ICU bed. An experimental design varied the values of the four attributes of the three hypothetical patients with COVID-19 that a respondent saw in four choice tasks.ResultsThe most important patient attribute to respondents was the probability the patient survives COVID-19 if they get the ICU bed (OR CI: 4.41 to 6.91). There was heterogeneity among different age groups of respondents about how much age of the patient mattered. Respondents under 30 years of age were more likely to choose young patients and old patients, and less likely to select patients aged 40–60 years old. For respondents in the age group 30–49 years old, as the age of the patient declined, their preference for saving the patient declined modestly in a linear fashion.ConclusionsRespondents favoured giving the last ICU bed available to the patient with the highest probability of surviving COVID-19. Public opinion suggests a simple guideline for physician choices based on likelihood of survival as opposed to the number of life-years saved. There was heterogeneity among respondents of different age groups for allocating the last ICU bed, as well as to the importance of the patient having an Alzheimer’s-like disability (where religion of the respondent is important) and the gender of the patient (where the gender and racial identity are important).

A lay perspective on prioritization for intensive care in pandemic times: Vaccination status matters

During a pandemic, demand for intensive care often exceeds availability. Experts agree that allocation should maximize benefits and must not be based on whether patients could have taken preventive measures. However, intensive care units (ICUs) are often overburdened by individuals with severe COVID-19 who have chosen not to be vaccinated to prevent the disease. This article reports an experiment that investigated the German public’s prioritization preferences during the fourth wave of the coronavirus pandemic (N = 1,014). In a series of scenarios, participants were asked to decide on ICU admission for patients who differed in terms of health condition, expected treatment benefits, and vaccination status. The results reveal an ingroup bias, as vaccinated individuals preferred to allocate more resources to the vaccinated than to the unvaccinated. Participants also favored admitting a heart attack patient rather than a COVID-19 patient with the same likelihood of benefiting from ICU admission, indicating a preference for maintaining regular ICU services rather than treating those with severe COVID-19. Finally, participants were more likely to admit a patient to intensive care when this meant withholding rather than withdrawing care from another patient. The results indicate that lay prioritizations violate established allocation principles, presaging potential conflicts between those in need of intensive care and those who provide and allocate it. It is therefore recommended that allocation principles should be explained to enhance public understanding. Additionally, vaccination rates should be increased to relieve ICUs and reduce the need for such triage decisions.

Medical Student Choices Regarding Ventilator Allocation for People With Disabilities

In the COVID-19 pandemic, concerns exist that ventilator triage policies may lead to discrimination against people with disabilities. This study evaluates whether preclinical medical students demonstrate bias towards people with disabilities during an educational ventilator-allocation exercise. Written student responses to a triage simulation activity were analyzed to describe ventilator priority rankings and to identify themes regarding disability. Disability status was not cited as a reason to withhold a ventilator. Key themes observed in ventilator triage decisions included life expectancy, comorbidities, and social worth. Although disability discrimination has historically been perpetuated by health care professionals, it is encouraging that preclinical medical students did not demonstrate explicit bias against people with disabilities in ventilator triage scenarios.

Machine Learning-based Derivation and External Validation of a Tool to Predict Death and Development of Organ Failure in Hospitalized Patients with COVID-19

BackgroundCOVID-19 mortality risk stratification tools could improve care, inform accurate and rapid triage decisions, and guide family discussions regarding goals of care. A minority of COVID-19 prognostic tools have been tested in external cohorts. Our objective was to compare machine learning algorithms and develop a tool for predicting subsequent clinical outcomes in COVID-19. MethodsWe conducted a retrospective cohort study that included hospitalized patients with COVID-19 from March 2020 to March 2021. 712 consecutive patients from University of Washington (UW) and 345 patients from Tongji Hospital in China were included. We applied three different machine learning algorithms to clinical and laboratory data collected within the initial 24 hours of hospital admission to determine the risk of in-hospital mortality, transfer to the intensive care unit (ICU), shock requiring vasopressors, and receipt of renal replacement therapy (RRT). Mortality risk models were derived, internally validated in UW and externally validated in Tongji Hospital. The risk models for ICU transfer, shock and RRT were derived and internally validated in the UW dataset. ResultsAmong the UW dataset, 122 patients died (17%) during hospitalization and the mean days to hospital mortality was 15.7 +/- 21.5 (mean +/- SD). Elastic net logistic regression resulted in a C-statistic for in-hospital mortality of 0.72 (95% CI, 0.64 to 0.81) in the internal validation and 0.85 (95% CI, 0.81 to 0.89) in the external validation set. Age, platelet count, and white blood cell count were the most important predictors of mortality. In the sub-group of patients > 50 years of age, the mortality prediction model continued to perform with a C-statistic of 0.82 (95% CI:0.76,0.87). Mortality prediction models also performed well for shock and RRT in the UW dataset but functioned with lower accuracy for ICU transfer. ConclusionsWe trained, internally and externally validated a prediction model using data collected within 24 hours of hospital admission to predict in-hospital mortality on average two weeks prior to death. We also developed models to predict RRT and shock with high accuracy. These models could be used to improve triage decisions, resource allocation, and support clinical trial enrichment.

With or without You: Co-Chaperones Mediate Health and Disease by Modifying Chaperone Function and Protein Triage

Heat shock proteins (HSPs) are a family of molecular chaperones that regulate essential protein refolding and triage decisions to maintain protein homeostasis. Numerous co-chaperone proteins directly interact and modify the function of HSPs, and these interactions impact the outcome of protein triage, impacting everything from structural proteins to cell signaling mediators. The chaperone/co-chaperone machinery protects against various stressors to ensure cellular function in the face of stress. However, coding mutations, expression changes, and post-translational modifications of the chaperone/co-chaperone machinery can alter the cellular stress response. Importantly, these dysfunctions appear to contribute to numerous human diseases. Therapeutic targeting of chaperones is an attractive but challenging approach due to the vast functions of HSPs, likely contributing to the off-target effects of these therapies. Current efforts focus on targeting co-chaperones to develop precise treatments for numerous diseases caused by defects in protein quality control. This review focuses on the recent developments regarding selected HSP70/HSP90 co-chaperones, with a concentration on cardioprotection, neuroprotection, cancer, and autoimmune diseases. We also discuss therapeutic approaches that highlight both the utility and challenges of targeting co-chaperones.

With or Without You: Co-chaperones Mediate Health and Disease by Modifying Chaperone Function and Protein Triage

Heat shock proteins (HSPs) are a family of molecular chaperones that regulate essential protein refolding and triage decisions to maintaining protein homeostasis. Numerous co-chaperone proteins directly interact and modify the function of HSPs, and these interactions impact the outcome of protein triage, impacting everything from structural proteins to cell signaling mediators. The chaperone/co-chaperone machinery protects against various stressors to ensuring cellular function in the face of stress. However, coding mutations, expression changes, and post-translational modifications of the chaperone/co-chaperone machinery can alter the cellular stress response. Importantly, these dysfunctions appear to contribute to numerous human diseases. Therapeutic targeting of chaperones is an attractive but challenging approach due to the vast functions of HSPs, likely contributing to the off-target effects of these therapies. Current efforts focus on targeting co-chaperones to develop precise treatments for numerous diseases caused by defects in protein quality control. This review focuses on the recent developments regarding selected HSP70/HSP90 co-chaperones, focusing on cardioprotection, neuroprotection, and cancer. We also discuss therapeutic approaches that highlight both the utility and challenges of targeting co-chaperones.

Reliability of COVID-19 symptom checkers as national triage tools: an international case comparison study

ObjectivesTriage is a critical component of the pandemic response. It affects morbidity, mortality and how effectively the available healthcare resources are used. In a number of nations the pandemic has sponsored the adoption of novel, online, patient-led triage systems—often referred to as COVID-19 symptom checkers. The current safety and reliability of these new automated triage systems remain unknown.MethodsWe tested six symptom checkers currently in use as triage tools at a national level against 52 cases simulating COVID-19 of various severities to determine if the symptom checkers appropriately triage time-critical cases onward to healthcare contact. We further analysed and compared each symptom checker to determine the discretionary aspects of triage decision-making that govern the automated advice generated.ResultsOf the 52 clinical presentations, the absolute rate of onward referral to any form of healthcare contact was: Singapore 100%, the USA 67%, Wales 65%, England 62%, Scotland 54% and Northern Ireland 46%. Triage decisions were broadly based on either estimates of ‘risk’ or ‘disease severity’. Risk-based symptom checkers were more reliable, with severity-based symptom checkers often triaging time-critical cases to stay home without clinical contact or follow-up.ConclusionThe COVID-19 symptom checkers analysed here were unable to reliably discriminate between mild and severe COVID-19. Risk-based symptom checkers may hold some promise of contributing to pandemic case management, while severity-based symptom checkers—the CDC and NHS 111 versions—confer too much risk to both public and healthcare services to be deemed a viable option for COVID-19 triage.