The Impact of Diabetes Mellitus and Hyperglycemia on the Severity and Outcome of Patients with COVID-19 Disease: A Single-Center Experience

Factors associated with full COVID-19 vaccination among persons living with diabetes mellitus in Uganda-A cross-sectional study

BACKGROUND

Diabetes mellitus (DM) is associated with severe outcomes of coronavirus disease (COVID-19), including death. COVID-19 vaccination is crucial for preventing infection and severe disease in the general population, but most importantly, among high-risk populations such as persons with DM. However, while COVID-19 vaccination uptake in the general population is regularly tracked, information on vaccination behavior specific to persons with DM is scarce. This study aimed to identify factors associated with the uptake of full COVID-19 vaccination among persons living with DM at Kiruddu National Referral Hospital (KRH).

METHODS

We employed a cross-sectional study design and administered a structured questionnaire on a systematically selected random sample of 340 people with DM attending Kiruddu National Referral Hospital from March 08 to May 25, 2023. We used a Modified Poisson Regression model to identify the factors associated with full COVID-19 vaccination and present adjusted prevalence ratios, along with their 95% confidence intervals. Full COVID-19 vaccination in this study was defined as having completed the last dose in the primary series of a COVID-19 vaccine.

RESULTS

We administered the questionnaire to 340 participants, with 75% (255 of 340) being female. The mean age of the participants was 52 years (± 12 years) and their median duration with diabetes mellitus was 5 years (± 7.3 years). Overall, 195 out of 340 participants (57.35%, 95% CI: 52-63%) completed the last dose in the primary series of a COVID-19 vaccine. The likelihood of receiving full vaccination was higher when a health provider advised one to receive a vaccine (aPR = 1.91, 95% CI: 1.20-3.02), when one reported having a comorbidity (aPR = 1.26, 95% CI: 1.06-1.53), and when one had a strong perceived benefit of vaccination (aPR = 1.76, 95% CI: 1.23-2.53). However, having a strong perceived barrier to vaccination was negatively associated with the likelihood of being fully vaccinated (aPR = 0.71, 95% CI: 0.60-0.84).

CONCLUSION

The uptake of COVID-19 vaccines among persons with DM at KRH is low, with only 57% having a full COVID-19 vaccination status. This underscores the progress in meeting the WHO recommendation of having a 100% COVID-19 vaccination uptake among people with underlying health conditions, including diabetes mellitus. At the policy level, the Ugandan Ministry of Health (MoH) may implement a provider-initiated vaccination strategy where healthcare providers who attend to DM patients during their routine clinic visits initiate the idea of vaccinating, sensitize, and spearhead myth-bursting around COVID-19 vaccines.

Stress Hyperglycemia Ratio as a Prognostic Marker in Diabetic Patients Hospitalized with COVID-19

Evidence is conflicting about the diabetes characteristics associated with worse outcome among hospitalized COVID-19 patients. We aimed to assess the role of stress hyperglycemia ratio (SHR) as a prognostic marker among them. In our retrospective cohort study, patients were stratified according to SHR, admission glucose, and glycated hemoglobin tertiles. The primary outcome was a composite endpoint of invasive mechanical ventilation, intensive care unit admission, and in-hospital mortality. The study included 395 patients with a mean age of 59 years, and 50.1% were males. Patients in the third tertile of SHR developed more primary events, and the difference was significant compared to the first tertile (p = 0.038) and close to significance compared to the second tertile (p = 0.054). There was no significant difference in the outcomes across admission glucose and glycated hemoglobin tertiles. A higher SHR tertile was an independent risk factor for the primary outcome (OR, 1.364; 95% CI: 1.014–1.836; p = 0.040) after adjustment for other covariables. In hospitalized COVID-19 diabetic patients, SHR third tertile was significantly associated with worse outcome and death. SHR can be a better prognostic marker compared to admission glucose and glycated hemoglobin. A higher SHR was an independent risk factor for worse outcome and in-hospital mortality.

Influence of Co-morbidities During SARS-CoV-2 Infection in an Indian Population

Background

Since the outbreak of COVID-19 pandemic the interindividual variability in the course of the disease has been reported, indicating a wide range of factors influencing it. Factors which were the most often associated with increased COVID-19 severity include higher age, obesity and diabetes. The influence of cytokine storm is complex, reflecting the complexity of the immunological processes triggered by SARS-CoV-2 infection. A modern challenge such as a worldwide pandemic requires modern solutions, which in this case is harnessing the machine learning for the purpose of analysing the differences in the clinical properties of the populations affected by the disease, followed by grading its significance, consequently leading to creation of tool applicable for assessing the individual risk of SARS-CoV-2 infection.

Methods

Biochemical and morphological parameters values of 5,000 patients (Curisin Healthcare (India) were gathered and used for calculation of eGFR, SII index and N/L ratio. Spearman's rank correlation coefficient formula was used for assessment of correlations between each of the features in the population and the presence of the SARS-CoV-2 infection. Feature importance was evaluated by fitting a Random Forest machine learning model to the data and examining their predictive value. Its accuracy was measured as the F1 Score.

Results

The parameters which showed the highest correlation coefficient were age, random serum glucose, serum urea, gender and serum cholesterol, whereas the highest inverse correlation coefficient was assessed for alanine transaminase, red blood cells count and serum creatinine. The accuracy of created model for differentiating positive from negative SARS-CoV-2 cases was 97%. Features of highest importance were age, alanine transaminase, random serum glucose and red blood cells count.

Conclusion

The current analysis indicates a number of parameters available for a routine screening in clinical setting. It also presents a tool created on the basis of these parameters, useful for assessing the individual risk of developing COVID-19 in patients. The limitation of the study is the demographic specificity of the studied population, which might restrict its general applicability.