Clinical characteristics and risk factors for disease severity and mortality of COVID-19 patients with diabetes mellitus in Kazakhstan: A nationwide study

Severity of COVID-19 infection in patients with COVID-19 combined with diabetes

PURPOSE

This study aimed to analyse the correlation between blood glucose control and the severity of COVID-19 infection in patients with diabetes.

METHODS

Clinical and imaging data of a total of 146 patients with diabetes combined with COVID-19 who visited our hospital between December 2022 and January 2023 were retrospectively collected. The patients were divided into the 'good blood glucose control' group and the 'poor blood glucose control' group based on an assessment of their blood glucose control. The clinical data, computed tomography (CT) appearance and score and the severity of COVID-19 infection of the two groups were compared, with the severity of COVID-19 infection being the dependent variable to analyse other influencing factors.

RESULTS

The group with poor blood glucose control showed a higher lobar involvement degree and total CT severity score (CTSS) than the group with good blood glucose control (13.30 ± 5.25 vs. 10.38 ± 4.84, p < 0.05). The two groups exhibited no statistically significant differences in blood lymphocyte, leukocyte, C-reaction protein, pleural effusion, consolidation, ground glass opacity or crazy-paving signs. Logistic regression analysis showed that the total CTSS significantly influences the clinical severity of patients (odds ratio 1.585, p < 0.05), whereas fasting plasma glucose and blood glucose control are not independent factors influencing clinical severity (both p > 0.05). The area under the curve (AUC) of CTSS prediction of critical COVID-19 was 0.895 with sensitivity of 79.3% and specificity of 88.1% when the threshold value is 12.

CONCLUSION

Blood glucose control is significantly correlated with the CTSS; the higher the blood glucose is, the more severe the lung manifestation. The CTSS can also be used to evaluate and predict the clinical severity of COVID-19.

Utilizing machine learning for survival analysis to identify risk factors for COVID-19 intensive care unit admission: A retrospective cohort study from the United Arab Emirates

BACKGROUND

The current situation of the unprecedented COVID-19 pandemic leverages Artificial Intelligence (AI) as an innovative tool for addressing the evolving clinical challenges. An example is utilizing Machine Learning (ML) models-a subfield of AI that take advantage of observational data/Electronic Health Records (EHRs) to support clinical decision-making for COVID-19 cases. This study aimed to evaluate the clinical characteristics and risk factors for COVID-19 patients in the United Arab Emirates utilizing EHRs and ML for survival analysis models.

METHODS

We tested various ML models for survival analysis in this work we trained those models using a different subset of features extracted by several feature selection methods. Finally, the best model was evaluated and interpreted using goodness-of-fit based on calibration curves,Partial Dependence Plots and concordance index.

RESULTS

The risk of severe disease increases with elevated levels of C-reactive protein, ferritin, lactate dehydrogenase, Modified Early Warning Score, respiratory rate and troponin. The risk also increases with hypokalemia, oxygen desaturation and lower estimated glomerular filtration rate and hypocalcemia and lymphopenia.

CONCLUSION

Analyzing clinical data using AI models can provide vital information for clinician to measure the risk of morbidity and mortality of COVID-19 patients. Further validation is crucial to implement the model in real clinical settings.

Neutrophilia and its correlation with increased inflammatory response in COVID-19 in diabetic and pre-diabetic patients

Background: Hyperglycemic patients are at a high risk of COVID-19 severity. Neutrophils have been considered critical effector cells in COVID-19 development. Vitamin D deficiency is prevalent in hyperglycemic patients and was found to adversely associate with the neutrophil count.

Aim: The goal of this work was to evaluate the characteristics of diabetic and pre-diabetic COVID-19 patients and discovered changes in neutrophils and their correlation, if any, with disease clinical presentation.

Patients and Methods: The study included total of (514) Covid-19 positive patients confirmed by PCR and recruited from the Prince Mohammad Bin Abdulaziz Hospital in Riyadh, Saudi Arabia. Patient’s clinical characteristics were collected for all patients. Laboratory tests include HbA1c, neutrophil count, C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), ferritin, D- dimer, 25 hydroxy vitamin D (25(OH)D), and folate.

Results: The results found that 286 patients (55.6%) were diabetic, 77 patients (15%) were pre-diabetic and 151 (29.4%) were normoglycaemic. A significant difference was exhibited regarding the neutrophil count and inflammatory factors of COVID-19 severity. Furthermore, the neutrophil count was found to be directly correlated with the severity monitoring biochemical markers for Covid-19: CRP, ESR, ferritin, and D-dimer and inversely associated with vitamin D levels in diabetic and pre-diabetic patients.

Conclusion: Our findings highlight the change of neutrophils in COVID-19 diabetic and pre-diabetic patients that was found to correlate positively with CRP, ESR, ferritin, and D-dimer, and negatively with 25(OH)D, but their correlation with the clinical presentation of the disease need further large investigations.

Epidemiology, Clinico-Pathological Characteristics, and Comorbidities of SARS-CoV-2-Infected Pakistani Patients

SARS-CoV-2 is a causative agent for COVID-19 disease, initially reported from Wuhan, China. The infected patients experienced mild to severe symptoms, resulting in several fatalities due to a weak understanding of its pathogenesis, which is the same even to date. This cross-sectional study has been designed on 452 symptomatic mild-to-moderate and severe/critical patients to understand the epidemiology and clinical characteristics of COVID-19 patients with their comorbidities and response to treatment. The mean age of the studied patients was 58 ± 14.42 years, and the overall male to female ratio was 61.7 to 38.2%, respectively. In total, 27.3% of the patients had a history of exposure, and 11.9% had a travel history, while for 60% of patients, the source of infection was unknown. The most prevalent signs and symptoms in ICU patients were dry cough, myalgia, shortness of breath, gastrointestinal discomfort, and abnormal chest X-ray (p < 0.001), along with a high percentage of hypertension (p = 0.007) and chronic obstructive pulmonary disease (p = 0.029) as leading comorbidities. The complete blood count indicators were significantly disturbed in severe patients, while the coagulation profile and D-dimer values were significantly higher in mild-to-moderate (non-ICU) patients (p < 0.001). The serum creatinine (1.22 μmol L^-1; p = 0.016) and lactate dehydrogenase (619 μmol L^-1; p < 0.001) indicators were significantly high in non-ICU patients, while raised values of total bilirubin (0.91 μmol L^-1; p = 0.054), C-reactive protein (84.68 mg L^-1; p = 0.001), and ferritin (996.81 mg L^-1; p < 0.001) were found in ICU patients. The drug dexamethasone was the leading prescribed and administrated medicine to COVID-19 patients, followed by remdesivir, meropenem, heparin, and tocilizumab, respectively. A characteristic pattern of ground glass opacities, consolidation, and interlobular septal thickening was prominent in severely infected patients. These findings could be used for future research, control, and prevention of SARS-CoV-2-infected patients.