Purpose Infectious diseases are more frequent and can be associated with worse outcomes in patients with diabetes. The aim of this study was to systematically review and conduct a meta-analysis of the available observational studies reporting the effect of diabetes on mortality among hospitalized patients with COVID-19. Methods The Medline, Embase, Google Scholar, and medRxiv databases were reviewed for identification of eligible studies. A random effects model meta-analysis was used, and I² was utilized to assess the heterogeneity. In-hospital mortality was defined as the endpoint. Sensitivity, subgroup, and meta-regression analyses were performed. Results A total of 18,506 patients were included in this meta-analysis (3713 diabetics and 14,793 non-diabetics). Patients with diabetes were associated with a higher risk of death compared with patients without diabetes (OR 1.65; 95% CI 1.35–1.96; I² 77.4%). The heterogeneity was high. A study-level meta-regression analysis was performed for all the important covariates, and no significant interactions were found between the covariates and the outcome of mortality. Conclusion This meta-analysis shows that that the likelihood of death seems to be higher in diabetic patients hospitalized with COVID-19 compared with non-diabetic patients. Further studies are needed to assess whether this association is independent or not, as well as to investigate the role of adequate glycemic control prior to infection with COVID-19.

Background: Patients with sepsis are at risk for developing sepsis-induced cardiomyopathy (SIC). Previous studies offer inconsistent results regarding the association of SIC and mortality. This study sought to assess whether SIC is linked to mortality in patients with sepsis and to evaluate predictors of the development of SIC.Methods: In this retrospective study, patients admitted to the medical intensive care unit with a diagnosis of sepsis in the absence of acute coronary syndrome were included. SIC was identified using transthoracic echo and was defined by a new onset decline in left ventricular ejection fraction (LVEF) ≤50%, or ≥10% decline in LVEF compared to baseline in patients with a history of heart failure with reduced ejection fraction. Multivariable logistic regression analysis was performed using the R software program.Results: Of the 359 patients in the final analysis, 19 (5.3%) had SIC. Eight (42.1%) of the 19 patients in the SIC group and 60 (17.6%) of the 340 patients in the non-SIC group died during hospitalization. SIC was associated with an increased risk for all-cause in-hospital mortality (odds ratio [OR], 4.46; 95% confidence interval [CI], 1.15–18.69; P=0.03). Independent predictors for the development of SIC were albumin level (OR, 0.47; 95% CI, 0.23–0.93; P=0.03) and culture positivity (OR, 8.47; 95% CI, 2.24–55.61; P=0.006). Concomitant right ventricular hypokinesis was noted in 13 (68.4%) of the 19 SIC patients.Conclusions: SIC was associated with an increased risk for all-cause in-hospital mortality. Low albumin level and culture positivity were independent predictors of SIC.

Acute lower extremity proximal deep venous thrombosis (DVT) requires accurate diagnosis and treatment in order to prevent embolization and other complications. Point-of-care ultrasound (POCUS), a clinician performed, and clinician interpreted bedside ultrasound examination has been increasingly used for DVT evaluation mainly in the urgent and critical care setting, but also in the ambulatory clinics and the medical wards. Studies have demonstrated that POCUS has excellent diagnostic accuracy for acute proximal DVT when performed by well-trained users. However, there is significant heterogeneity among studies on the necessary extent of training and universally acceptable standardized education protocols are needed. In this review, we summarize the evidence that supports the use of POCUS to diagnose acute proximal DVT and focus on methodology and current technology, sensitivity and specificity, pre-test probability and the role of D-dimer, time and resources, education, limitations, and future directions.

Cardiovascular and cerebrovascular disease incidence has risen mainly due to poor control of preventable risk factors and still constitutes a significant financial and health burden worldwide. ChatGPT is an artificial intelligence language-based model developed by OpenAI. Due to the model’s unique cognitive capabilities beyond data processing and the production of high-quality text, there has been a surge of research interest concerning its role in the scientific community and contemporary clinical practice. To fully exploit ChatGPT’s potential benefits and reduce its possible misuse, extreme caution must be taken to ensure its implications ethically and equitably. In this narrative review, we explore the language model’s possible applications and limitations while emphasizing its potential value for diagnosing, managing, and prognosis of cardiovascular and cerebrovascular disease.

Objective: Previous studies have unveiled a relationship between the severity of coronavirus disease 2019 (COVID‐19) pneumonia and obesity. The aims of this multicenter retrospective cohort study were to disentangle the association of BMI and associated metabolic risk factors (diabetes, hypertension, hyperlipidemia, and current smoking status) in critically ill patients with COVID‐19.Methods: Patients admitted to intensive care units for COVID‐19 in 21 centers (in Europe, Israel, and the United States) were enrolled in this study between February 19, 2020, and May 19, 2020. Primary and secondary outcomes were the need for invasive mechanical ventilation (IMV) and 28‐day mortality, respectively.Results: A total of 1,461 patients were enrolled; the median (interquartile range) age was 64 years (40.9‐72.0); 73.2% of patients were male; the median BMI was 28.1 kg/m² (25.4‐32.3); a total of 1,080 patients (73.9%) required IMV; and the 28‐day mortality estimate was 36.1% (95% CI: 33.0‐39.5). An adjusted mixed logistic regression model showed a significant linear relationship between BMI and IMV: odds ratio = 1.27 (95% CI: 1.12‐1.45) per 5 kg/m². An adjusted Cox proportional hazards regression model showed a significant association between BMI and mortality, which was increased only in obesity class III (≥40; hazard ratio = 1.68 [95% CI: 1.06‐2.64]).Conclusions: In critically ill COVID‐19 patients, a linear association between BMI and the need for IMV, independent of other metabolic risk factors, and a nonlinear association between BMI and mortality risk were observed.