Rationale: Coronavirus disease (COVID-19) is a global threat to health. Its inflammatory characteristics are incompletely understood. Objectives: To define the cytokine profile of COVID-19 and to identify evidence of immunometabolic alterations in those with severe illness. Methods: Levels of IL-1β, IL-6, IL-8, IL-10, and sTNFR1 (soluble tumor necrosis factor receptor 1) were assessed in plasma from healthy volunteers, hospitalized but stable patients with COVID-19 (COVID stable patients), patients with COVID-19 requiring ICU admission (COVID ICU patients), and patients with severe community-acquired pneumonia requiring ICU support (CAP ICU patients). Immunometabolic markers were measured in circulating neutrophils from patients with severe COVID-19. The acute phase response of AAT (alpha-1 antitrypsin) to COVID-19 was also evaluated. Measurements and Main Results: IL-1β, IL-6, IL-8, and sTNFR1 were all increased in patients with COVID-19. COVID ICU patients could be clearly differentiated from COVID stable patients, and demonstrated higher levels of IL-1β, IL-6, and sTNFR1 but lower IL-10 than CAP ICU patients. COVID-19 neutrophils displayed altered immunometabolism, with increased cytosolic PKM2 (pyruvate kinase M2), phosphorylated PKM2, HIF-1α (hypoxia-inducible factor-1α), and lactate. The production and sialylation of AAT increased in COVID-19, but this antiinflammatory response was overwhelmed in severe illness, with the IL-6:AAT ratio markedly higher in patients requiring ICU admission ( P < 0.0001). In critically unwell patients with COVID-19, increases in IL-6:AAT predicted prolonged ICU stay and mortality, whereas improvement in IL-6:AAT was associated with clinical resolution ( P < 0.0001). Conclusions: The COVID-19 cytokinemia is distinct from that of other types of pneumonia, leading to organ failure and ICU need. Neutrophils undergo immunometabolic reprogramming in severe COVID-19 illness. Cytokine ratios may predict outcomes in this population.

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A survey of doctors working in two large NHS hospitals identified over 120 laboratory tests, imaging investigations and investigational procedures that they considered not to be overused. A common suggestion in this survey was that more training was required. And, this prompted the development of a list of core principles for high-quality, high-value testing. The list can be used as a framework for training and as a reference source. The core principles are: (1) Base testing practices on the best available evidence. (2) Apply the evidence on test performance with careful judgement. (3) Test efficiently. (4) Consider the value (and affordability) of a test before requesting it. (5) Be aware of the downsides and drivers of overdiagnosis. (6) Confront uncertainties. (7) Be patient-centred in your approach. (8) Consider ethical issues. (9) Be aware of normal cognitive limitations and biases when testing. (10) Follow the ‘knowledge journey’ when teaching and learning these core principles.

Background With the growing adoption of electronic medical records, there are increasing demands for the use of this electronic clinical data in observational research. A frequent ethics board requirement for such secondary use of personal health information in observational research is that the data be de-identified. De-identification heuristics are provided in the Health Insurance Portability and Accountability Act Privacy Rule, funding agency and professional association privacy guidelines, and common practice.Objective The aim of the study was to evaluate whether the re-identification risks due to record linkage are sufficiently low when following common de-identification heuristics and whether the risk is stable across sample sizes and data sets.Methods Two methods were followed to construct identification data sets. Re-identification attacks were simulated on these. For each data set we varied the sample size down to 30 individuals, and for each sample size evaluated the risk of re-identification for all combinations of quasi-identifiers. The combinations of quasi-identifiers that were low risk more than 50% of the time were considered stable.Results The identification data sets we were able to construct were the list of all physicians and the list of all lawyers registered in Ontario, using 1% sampling fractions. The quasi-identifiers of region, gender, and year of birth were found to be low risk more than 50% of the time across both data sets. The combination of gender and region was also found to be low risk more than 50% of the time. We were not able to create an identification data set for the whole population.Conclusions Existing Canadian federal and provincial privacy laws help explain why it is difficult to create an identification data set for the whole population. That such examples of high re-identification risk exist for mainstream professions makes a strong case for not disclosing the high-risk variables and their combinations identified here. For professional subpopulations with published membership lists, many variables often needed by researchers would have to be excluded or generalized to ensure consistently low re-identification risk. Data custodians and researchers need to consider other statistical disclosure techniques for protecting privacy.

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