Impact of cardiovascular disease on clinical outcomes in hospitalized patients with Covid-19: a systematic review and meta-analysis

The published trend of studies on COVID-19 and diabetes: bibliometric analysis

Background

Since the COVID-19 pandemic outbreak, diabetes mellitus (DM) has been at the core of the confirmed risk factors for fatal or critical care unit-treated COVID-19 and COVID-19 related complications. Although relevant studies on DM have developed rapidly during the COVID-19 pandemic. However, the aforementioned research results have not been systematically quantified by means of bibliometric analysis.

Purpose

The purpose of this study is to provide a comprehensive analysis of the current status and trends of publications related to DM research during the COVID19 epidemic.

Methods

A bibliometric analysis was performed using the Web of Science database. In this study, we used citespace, R software and R-Bibliometrix to analyze keywords, most-cited authors, most-cited countries, most-cited global documents, and co-occurrence and co-citation networks.

Results

A total of 1688 publications was included in this study. Investigators from the United States contributed the most publications. The United States, China and Europe have the most collaboration with the other countries/regions. A total of 3355 institutions made contributions to this study. Of the top 10 institutions with the most publications, N8 Research Partnership showed the most centrality. Among the top 10 journals, Diabetes Research and Clinical Practice published the most articles. Among authors included, Khunti Kamlesh is rated first with 27 papers and has the highest centrality. The most frequently co-cited article is entitled "Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study". The most popular keywords included diabetes, mortality, diabetes, outcome, occurrences, risk, and type 1 diabetes.

Conclusion

This bibliometric study provides an overall picture of DM research and research trends during the COVID-19 pandemic and provides a basis for researchers to develop their next research strategies.

Effect of Protective Measures Adopted in the COVID-19 Pandemic on Hemodialysis Patients

Introduction The use of masks and other preventive measures is nowadays an essential measure to prevent COVID-19 infections, particularly in hemodialysis patients. The aim of this study was to understand whether these protective measures adopted during the COVID-19 pandemic reduced or somehow contained the number of respiratory infections in a population of hemodialysis patients. Methods This was a longitudinal retrospective single-center study of hemodialysis patients with at least six months of follow-up in a central hospital. A total of 103 patients were evaluated for the study. Two groups were defined: a control group that was followed in the year before the beginning of the pandemic and a group that followed in the year after its beginning. Results Patients in the pandemic group had a higher prevalence of previous major cardiovascular events (48.9% vs 8.6%) and heart failure (31.3% vs 12.1%) than those in the control group. Vaccination rates for influenza and pneumococcus as well as the monthly analytical results were similar in both groups. There were no significant differences in lower respiratory infections, hospitalizations caused by lower respiratory infections, and mortality between both groups. However, not accounting for aspiration pneumonias, the pandemic group had half the mortality due to respiratory infections (2.2% vs 5.2%). Conclusion Despite patients in the pandemic group having a similar prevalence of respiratory infections and hospitalizations motivated by lower respiratory infections, they presented about half the mortality of the control group. This suggests that although there was no decrease in the number of infections, protective measures may have contributed to a decreased mortality.

A comparison of machine learning algorithms in predicting COVID-19 prognostics

ML algorithms are used to develop prognostic and diagnostic models and so to support clinical decision-making. This study uses eight supervised ML algorithms to predict the need for intensive care, intubation, and mortality risk for COVID-19 patients. The study uses two datasets: (1) patient demographics and clinical data (n = 11,712), and (2) patient demographics, clinical data, and blood test results (n = 602) for developing the prediction models, understanding the most significant features, and comparing the performances of eight different ML algorithms. Experimental findings showed that all prognostic prediction models reported an AUROC value of over 0.92, in which extra tree and CatBoost classifiers were often outperformed (AUROC over 0.94). The findings revealed that the features of C-reactive protein, the ratio of lymphocytes, lactic acid, and serum calcium have a substantial impact on COVID-19 prognostic predictions. This study provides evidence of the value of tree-based supervised ML algorithms for predicting prognosis in health care.

Clinical significance of prediabetes, undiagnosed diabetes and diagnosed diabetes on critical outcomes in COVID-19: Integrative analysis from the Japan COVID-19 task force

AIM

Diabetes mellitus (DM) is a known risk factor for severe coronavirus disease 2019 (COVID-19), but the clinical impact of undiagnosed diabetes and prediabetes in COVID-19 are unclear particularly in Japan. We clarify the difference in clinical characteristics, including age, sex, body mass index and co-morbidities, laboratory findings and critical outcomes, in a large Japanese COVID-19 cohort without diabetes, with prediabetes, undiagnosed diabetes and diagnosed diabetes, and to identify associated risk factors.

MATERIALS AND METHODS

This multicentre, retrospective cohort study used the Japan COVID-19 Task Force database, which included data on 2430 hospitalized COVID-19 patients from over 70 hospitals from February 2020 to October 2021. The prevalence of prediabetes, undiagnosed diabetes and diagnosed diabetes were estimated based on HbA1c levels or a clinical diabetes history. Critical outcomes were defined as the use of high-flow oxygen, invasive positive-pressure ventilation or extracorporeal membrane oxygenation, or death during hospitalization.

RESULTS

Prediabetes, undiagnosed diabetes and diagnosed diabetes were observed in 40.9%, 10.0% and 23.0%, respectively. Similar to diagnosed diabetes, prediabetes and undiagnosed diabetes were risk factors for critical COVID-19 outcomes (adjusted odds ratio [aOR] [95% CI]: 2.13 [1.31-3.48] and 4.00 [2.19-7.28], respectively). HbA1c was associated with COVID-19 severity in prediabetes patients (aOR [95% CI]: 11.2 [3.49-36.3]), but not other groups.

CONCLUSIONS

We documented the clinical characteristics and outcomes of Japanese COVID-19 patients according to HbA1c levels or diabetes co-morbidity. As well as undiagnosed and diagnosed diabetes, physicians should be aware of prediabetes related to COVID-19 severity.