Is there a shift of the oxygen-hemoglobin dissociation curve in COVID-19?

Erythrocytes Functionality in SARS-CoV-2 Infection: Potential Link with Alzheimer's Disease

Coronavirus disease 2019 (COVID-19) is a rapidly spreading acute respiratory infection caused by SARS-CoV-2. The pathogenesis of the disease remains unclear. Recently, several hypotheses have emerged to explain the mechanism of interaction between SARS-CoV-2 and erythrocytes, and its negative effect on the oxygen-transport function that depends on erythrocyte metabolism, which is responsible for hemoglobin-oxygen affinity (Hb-O2 affinity). In clinical settings, the modulators of the Hb-O2 affinity are not currently measured to assess tissue oxygenation, thereby providing inadequate evaluation of erythrocyte dysfunction in the integrated oxygen-transport system. To discover more about hypoxemia/hypoxia in COVID-19 patients, this review highlights the need for further investigation of the relationship between biochemical aberrations in erythrocytes and oxygen-transport efficiency. Furthermore, patients with severe COVID-19 experience symptoms similar to Alzheimer's, suggesting that their brains have been altered in ways that increase the likelihood of Alzheimer's. Mindful of the partly assessed role of structural, metabolic abnormalities that underlie erythrocyte dysfunction in the pathophysiology of Alzheimer's disease (AD), we further summarize the available data showing that COVID-19 neurocognitive impairments most probably share similar patterns with known mechanisms of brain dysfunctions in AD. Identification of parameters responsible for erythrocyte function that vary under SARS-CoV-2 may contribute to the search for additional components of progressive and irreversible failure in the integrated oxygen-transport system leading to tissue hypoperfusion. This is particularly relevant for the older generation who experience age-related disorders of erythrocyte metabolism and are prone to AD, and provide an opportunity for new personalized therapies to control this deadly infection.

The oxygen dissociation curve of blood in COVID-19-An update

An impressive effect of the infection with SARS-Co-19 is the impairment of oxygen uptake due to lung injury. The reduced oxygen diffusion may potentially be counteracted by an increase in oxygen affinity of hemoglobin. However, hypoxia and anemia associated with COVID-19 usually decrease oxygen affinity due to a rise in [2,3-bisphosphoglycerate]. As such, COVID-19 related changes in the oxygen dissociation curve may be critical for oxygen uptake and supply, but are hard to predict. A Pubmed search lists 14 publications on oxygen affinity in COVID-19. While some investigations show no changes, three large studies found an increased affinity that was related to a good prognosis. Exact causes remain unknown. The cause of the associated anemia in COVID-19 is under discussion. Erythrocytes with structural alterations of membrane and cytoskeleton have been observed, and virus binding to Band 3 and also to ACE2 receptors in erythroblasts has been proposed. COVID-19 presentation is moderate in many subjects suffering from sickle cell disease. A possible explanation is that COVID-19 counteracts the unfavorable large right shift of the oxygen dissociation curve in these patients. Under discussion for therapy are mainly affinity-increasing drugs.

The Predictive Score for Patients Hospitalized With COVID-19 in Resource-Limited Settings

Background and aims

The second wave of coronavirus disease 2019 (COVID-19) has been devastating in India and many developing countries. The mortality reported has been 40% higher than in the first wave, overwhelming the nation’s health infrastructure. Despite a better understanding of the disease and established treatment protocols including steroids and heparin, the second wave was disastrous. Subsequent waves have the potential to further cripple healthcare deliveries, also affecting non-COVID-19 care across many developing economies. It is then important to identify and triage high-risk patients to best use the limited resources. Routine tests such as neutrophil and monocyte counts have been identified but have not been successfully validated uniformly, and their utility is still being understood in COVID-19.

Various predictive models that are available require online resources and calculators and additionally await validation across all populations. These, although useful, might not be available or accessible across all institutions. It is then important to identify easy-to-use scores that utilize tests done routinely.

In identifying with this goal, we did a retrospective review of the institutional database to identify potential predictors of intensive care unit (ICU) admission and mortality in patients hospitalized during the second wave who accessed healthcare at our academic setup.

Results

Three predictors of mortality and four predictors of ICU admission were identified. Absolute neutrophil count was a common predictor of both ICU admission and mortality but with two separate cut points. An absolute neutrophil count of >4,200 predicted need for ICU admission (odds ratio (OR): 3.1 (95% confidence interval (CI): 2.0, 4.8)), and >7,200 predicted mortality (adjusted OR: 4.2 (95% CI: 1.9, 9.4)). We observed that a blood urea level greater than 45 was predictive of needing ICU care (adjusted OR: 8.0 (95% CI: 3.7, 17.6)). In our dataset, serum ferritin of >500 was predictive of ICU admission (adjusted OR: 2.7 (95% CI: 1.2, 5.9)). We noted a right shift of partial pressure (p50 is the oxygen tension at which hemoglobin is 50% saturated) (p50c) in SARS-CoV-2 as a predictor of ICU care (OR: 2.6 (95% CI: 1.7, 3.9)) when partial pressure is >26.5. In our analysis, a serum protein of less than 7 g/dL (OR: 2.8 (95% CI: 1.7, 4.4)) was a predictive variable for ICU admission. An LDH value of >675 was predictive of severity with a need for ICU admission (OR: 9.2 (95% CI: 5.4, 15.5)) in our series. We then assigned a score to each of the predictive variables based on the adjusted odds ratio.

Conclusion

We identified a set of easy-to-use predictive variables and scores to recognize the subset of patients hospitalized with COVID-19 with the highest risk of death or clinical worsening requiring ICU care.