Development of a clinical risk score to predict death in patients with COVID-19

Type 2 diabetes and Covid-19: Lessons learnt, unanswered questions and hints for the future

Type 2 diabetes (T2DM) and COVID-19 represent a considerable burden of disease for patients and healthcare systems. New evidence is transpiring detailing the existence of a bidirectional relationship between T2DM and COVID-19. Alongside the acute influence of pre-existing T2DM on the course of COVID-19 and the exacerbation of dysglycemia following acute infection, long-term sequalae resulting from the synergistic interplay between the two is emerging, namely the development of COVID-induced diabetes and long-COVID in patients with pre-existing diabetes. This review presents our current understanding of the bidirectionality between these two conditions with a view to highlighting questions which remain unanswered and suggesting avenues for future research. In doing so, it emphasises critical gaps where concentrated research efforts are likely to yield the most beneficial improvements in understanding of the relationship between the two conditions, translating to tangible optimisations in care for the affected population.

A wastewater-based risk index for SARS-CoV-2 infections among three cities on the Canadian Prairie

Wastewater surveillance (WWS) is useful to better understand the spreading of coronavirus disease 2019 (COVID-19) in communities, which can help design and implement suitable mitigation measures. The main objective of this study was to develop the Wastewater Viral Load Risk Index (WWVLRI) for three Saskatchewan cities to offer a simple metric to interpret WWS. The index was developed by considering relationships between reproduction number, clinical data, daily per capita concentrations of virus particles in wastewater, and weekly viral load change rate. Trends of daily per capita concentrations of SARS-CoV-2 in wastewater for Saskatoon, Prince Albert, and North Battleford were similar during the pandemic, suggesting that per capita viral load can be useful to quantitatively compare wastewater signals among cities and develop an effective and comprehensible WWVLRI. The effective reproduction number (R_t) and the daily per capita efficiency adjusted viral load thresholds of 85 × 10⁶ and 200 × 10⁶ N2 gene counts (gc)/population day (pd) were determined. These values with rates of change were used to categorize the potential for COVID-19 outbreaks and subsequent declines. The weekly average was considered 'low risk' when the per capita viral load was 85 × 10⁶ N2 gc/pd. A 'medium risk' occurs when the per capita copies were between 85 × 10⁶ and 200 × 10⁶ N2 gc/pd. with a rate of change <100 %. The start of an outbreak is indicated by a 'medium-high' risk classification when the week-over-week rate of change was >100 %, and the absolute magnitude of concentrations of viral particles was >85 × 10⁶ N2 gc/pd. Lastly, a 'high risk' occurs when the viral load exceeds 200 × 10⁶ N2 gc/pd. This methodology provides a valuable resource for decision-makers and health authorities, specifically given the limitation of COVID-19 surveillance based on clinical data.

Diabetes, COVID-19, and questions unsolved

Recent evidence suggests a role for Diabetes Mellitus in adverse outcomes from COVID-19 infection; yet the underlying mechanisms are not clear. Moreover, attention has turned to prophylactic vaccination to protect the population from COVID-19-related illness and mortality. We performed a comprehensive peer-reviewed literature search on an array of key terms concerning diabetes and COVID-19 seeking to address the following questions: 1. What role does diabetes play as an accelerator for adverse outcomes in COVID-19?; 2. What mechanisms underlie the differences in outcomes seen in people with diabetes?; 3. Are vaccines against COVID-19 efficacious in people with diabetes? The current literature demonstrates that diabetes is associated with an increased risk of adverse outcomes from COVID-19 infection, and post-COVID sequelae. Potential mechanisms include dysregulation of Angiotensin Converting Enzyme 2, Furin, CD147, and impaired immune cell responses. Hyperglycaemia is a key exacerbator of these mechanisms. Limited studies are available on COVID-19 vaccination in people with diabetes; however, the current literature suggests that vaccination is protective against adverse outcomes for this population. In summary, people with diabetes are a high-risk group that should be prioritised in vaccination efforts. Glycaemic optimisation is paramount to protecting this group from COVID-19-associated risk. Unsolved questions remain as to the molecular mechanisms underlying the adverse outcomes seen in people with diabetes; the functional impact of post-COVID symptoms on people with diabetes, their persistence, and management; how long-term vaccine efficacy is affected by diabetes, and the antibody levels that confer protection from adverse outcomes in COVID-19.

Prognostic models in COVID-19 infection that predict severity: a systematic review

Current evidence on COVID-19 prognostic models is inconsistent and clinical applicability remains controversial. We performed a systematic review to summarize and critically appraise the available studies that have developed, assessed and/or validated prognostic models of COVID-19 predicting health outcomes. We searched six bibliographic databases to identify published articles that investigated univariable and multivariable prognostic models predicting adverse outcomes in adult COVID-19 patients, including intensive care unit (ICU) admission, intubation, high-flow nasal therapy (HFNT), extracorporeal membrane oxygenation (ECMO) and mortality. We identified and assessed 314 eligible articles from more than 40 countries, with 152 of these studies presenting mortality, 66 progression to severe or critical illness, 35 mortality and ICU admission combined, 17 ICU admission only, while the remaining 44 studies reported prediction models for mechanical ventilation (MV) or a combination of multiple outcomes. The sample size of included studies varied from 11 to 7,704,171 participants, with a mean age ranging from 18 to 93 years. There were 353 prognostic models investigated, with area under the curve (AUC) ranging from 0.44 to 0.99. A great proportion of studies (61.5%, 193 out of 314) performed internal or external validation or replication. In 312 (99.4%) studies, prognostic models were reported to be at high risk of bias due to uncertainties and challenges surrounding methodological rigor, sampling, handling of missing data, failure to deal with overfitting and heterogeneous definitions of COVID-19 and severity outcomes. While several clinical prognostic models for COVID-19 have been described in the literature, they are limited in generalizability and/or applicability due to deficiencies in addressing fundamental statistical and methodological concerns. Future large, multi-centric and well-designed prognostic prospective studies are needed to clarify remaining uncertainties.

Comorbid Asthma Increased the Risk for COVID-19 Mortality in Asia: A Meta-Analysis

We aimed to explore the influence of comorbid asthma on the risk for mortality among patients with coronavirus disease 2019 (COVID-19) in Asia by using a meta-analysis. Electronic databases were systematically searched for eligible studies. The pooled odds ratio (OR) with 95% confidence interval (CI) was estimated by using a random-effect model. An inconsistency index (I2) was utilized to assess the statistical heterogeneity. A total of 103 eligible studies with 198,078 COVID-19 patients were enrolled in the meta-analysis; our results demonstrated that comorbid asthma was significantly related to an increased risk for COVID-19 mortality in Asia (pooled OR = 1.42, 95% CI: 1.20−1.68; I2 = 70%, p < 0.01). Subgroup analyses by the proportion of males, setting, and sample sizes generated consistent findings. Meta-regression indicated that male proportion might be the possible sources of heterogeneity. A sensitivity analysis exhibited the reliability and stability of the overall results. Both Begg’s analysis (p = 0.835) and Egger’s analysis (p = 0.847) revealed that publication bias might not exist. In conclusion, COVID-19 patients with comorbid asthma might bear a higher risk for mortality in Asia, at least among non-elderly individuals.

Biochemical and hematological factors associated with COVID-19 severity among Gabonese patients: A retrospective cohort study

The COVID-19 disease presents a large range of clinical manifestations and includes asymptomatic, mild, and severe cases. The level of severity is related to parameters associated with immunity, genetics, and biochemistry. Africa shows one of the lowest COVID-19 fatality rates but very few data on the biochemical markers of COVID-19 in patients and the factors associated with disease severity are available for the continent. In Gabon, the COVID-19 fatality rate is only 0.63% but almost no data on biomarkers in COVID-19 patients have been published. Both the number of COVID-19 cases and the mortality rate reported in Africa in general, and in Gabon in particular, are lower than in non-African countries. As such, understanding the factors associated with disease severity in Gabonese patients is a crucial step to better understand the disease in the African context and prepare for future COVID-19 waves and other epidemics of emerging diseases. Here, we compared biochemical and hematological markers among 753 Gabonese COVID-19 patients with asymptomatic (184/753), mild/moderate (420/753), and severe/critical (149/753) forms of the disease using an Analysis of Variance (ANOVA) or a Kruskal-Wallis (KW) test. We modeled these parameters together with comorbidities, age, and sex to predict factors associated with disease severity by using a "binomial generalized linear model" utilizing the "package" stats of R software version 4.0.2. Our results showed that almost all the biochemical and hematological parameters (except creatinine, phosphorus, D-dimers, platelets, and monocytes) varied according to disease severity. However, age and the dysfunction of organs like the kidney, liver, and lung together with the decrease of electrolytes (chloride, potassium, and sodium) are the best predictors of disease severity in Gabonese patients.

Bedside Scoring System for Predicting Adverse Outcomes Among Patients Suffering From SARS-CoV-2 Infection

Aim To develop a clinical risk score to predict adverse outcomes among diabetic hospitalized COVID-19 patients Methods The data was collected retrospectively from patients hospitalized with the SARS-CoV-2 virus at Sri Ramachandra Institute of Higher education and research. It integrated independent variables such as sex, age, glycemic status, socioeconomic status, and preexisting lung conditions. Each variable was assigned a value and the final score was calculated as a sum of all the variables. The final score was then compared with patient outcomes. The patients were scored from 0 to 8 and a score of 3 or more was considered as being at greater risk for developing complications. Number of mortalities in each group, any clinical deterioration requiring ICU admission, and the number of patients requiring a prolonged hospital stay of more than 10 days in each group were noted and the results compared. Results Higher blood glucose levels and preexisting lung conditions like chronic obstructive pulmonary disease (COPD), asthma, and pulmonary tuberculosis have been associated with a higher risk of developing complications related to SARS-CoV-2 illness. Of the 5023 patients enrolled in the study, 2402 had a score of 2 or below, and 2621 had a score of 3 or above. Among patients with a score of 2 or below 1.7% of the patients contracted a severe disease resulting in death. 2.9% were shifted to ICU, but recovered and 12.2% of patients had a prolonged hospital stay. Of those with a score of 3 or greater, 5.1% died, 7.36% were shifted to ICU, but recovered, and 19.5% required a prolonged hospital stay. The observed results were analyzed using the Chi-square test and were found to be significant at a p-level of 0.0001. Conclusion This clinical risk score has been built with routinely available data to help predict adverse outcomes in diabetic patients hospitalized with the SARS-CoV-2 virus. It is a good tool for resource-limited areas as it uses readily available data. It can also be used for other severe acute respiratory illnesses or influenza-like illnesses.

Inflammation and vascular dysfunction: The negative synergistic combination of diabetes and COVID-19

AIMS

Several reports indicate that diabetes determines an increased mortality risk in patients with coronavirus disease 19 (COVID-19) and a good glycaemic control appears to be associated with more favourable outcomes. Evidence also supports that COVID-19 pneumonia only accounts for a part of COVID-19 related deaths. This disease is indeed characterised by abnormal inflammatory response and vascular dysfunction, leading to the involvement and failure of different systems, including severe acute respiratory distress syndrome, coagulopathy, myocardial damage and renal failure. Inflammation and vascular dysfunction are also well-known features of hyperglycemia and diabetes, making up the ground for a detrimental synergistic combination that could explain the increased mortality observed in hyperglycaemic patients.

MATERIALS AND METHODS

In this work, we conduct a narrative review on this intriguing connection. Together with this, we also present the clinical characteristics, outcomes, laboratory and histopathological findings related to this topic of a cohort of nearly 1000 subjects with COVID-19 admitted to a third-level Hospital in Milan.

RESULTS

We found an increased mortality in subjects with COVID-19 and diabetes, together with an altered inflammatory profile.

CONCLUSIONS

This may support the hypothesis that diabetes and COVID-19 meet at the crossroads of inflammation and vascular dysfunction. (ClinicalTrials.gov NCT04463849 and NCT04382794).