Comparative Analysis of COVID-19 Outcomes in Type 1 and Type 2 Diabetes: A Three-Year Retrospective Study.

Background and Objectives: This comprehensive retrospective study assesses COVID-19 outcomes in type 1 (T1D) and type 2 diabetes (T2D) patients across three years, focusing on how these outcomes varied with the evolving pandemic and changes in diabetes management. The study aims to determine if COVID-19 outcomes, including severity, intensive care unit (ICU) admission rates, duration of hospitalization, and mortality, are significantly different between these diabetes subtypes. Materials and Methods: The study analyzed data from patients admitted to the Victor Babes Hospital for Infectious Diseases and Pulmonology with confirmed COVID-19 and pre-existing diabetes, from the years 2020, 2021, and 2022. Results: Among 486 patients (200 without diabetes, 62 with T1D, 224 with T2D), T2D patients showed notably higher severity, with 33.5% experiencing severe cases, compared to 25.8% in T1D. Mortality rates were 11.6% in T2D and 8.1% in T1D. T2D patients had longer hospital stays (11.6 ± 7.0 days) compared to T1D (9.1 ± 5.8 days) and were more likely to require ICU admission (OR: 2.24) and mechanical ventilation (OR: 2.46). Hyperglycemia at admission was significantly higher in the diabetes groups, particularly in T2D (178.3 ± 34.7 mg/dL) compared to T1D (164.8 ± 39.6 mg/dL). Conclusions: The study reveals a discernible difference in COVID-19 outcomes between T1D and T2D, with T2D patients having longer hospital admissions, mechanical ventilation necessities, and mortality risks.

Impact of Pre-Infection COVID-19 Vaccination on the Incidence and Severity of Post-COVID Syndrome: A Systematic Review and Meta-Analysis.

This systematic review critically evaluated the impact of a pre-infection COVID-19 vaccination on the incidence and severity of post-COVID-19 syndrome and aimed to assess the potential protective effect across different vaccines and patient demographics. This study hypothesized that vaccination before infection substantially reduces the risk and severity of post-COVID-19 syndrome. In October 2023, a comprehensive literature search was conducted across three databases, PubMed, Embase, and Scopus, focusing on studies published up to that date. Utilizing a wide array of keywords, the search strategy adhered to the PRISMA guidelines and was registered in the Open Science Framework. The inclusion criteria comprised studies focusing on patients with a breakthrough SARS-CoV-2 infection who developed post-COVID-19 syndrome. We included a total of 13 articles that met the inclusion criteria, analyzing more than 10 million patients with a mean age of 50.6 years, showing that the incidence of intensive care unit (ICU) admissions post-vaccination was as low as 2.4%, with a significant reduction in mortality risk (OR 0.66, 95% CI 0.58-0.74). The prevalence of post-COVID-19 syndrome symptoms was lower in vaccinated individuals (9.5%) compared to unvaccinated (14.6%), with a notable decrease in activity-limiting symptoms (adjusted OR 0.59, 95% CI 0.48-0.73). Vaccinated patients also showed a quicker recovery and return to work (HR 1.37, 95% CI 1.04-1.79). The pooled odds ratio of 0.77 indicates that vaccination is associated with a 23% reduction in the risk of developing post-COVID-19 syndrome (95% CI 0.75-0.79). Despite the protective effects observed, a substantial heterogeneity among the studies was noted. In conclusion, a pre-infection COVID-19 vaccination is associated with a significant reduction in the risk and severity of post-COVID-19 syndrome. However, the observed heterogeneity across studies suggests a need for further research with standardized methods to fully comprehend vaccine efficacy against long COVID.

The Effect of Comorbidities and Complications on COVID-19 Mortality: A Detailed Retrospective Study in Western Romania.

COVID-19 continues to impact global health systems even after being declared over, with some patients exhibiting severe complications linked to pre-existing conditions. This study aimed to investigate the association between comorbidities, complications, and survival outcomes among COVID-19 survivors in Western Romania. Our hypothesis posited that comorbidities and complications significantly influence survival rates. We conducted a retrospective analysis of 1948 COVID-19 survivors admitted from January to December 2021, with 192 selected for detailed analysis based on inclusion and exclusion criteria. The severity of COVID-19 was classified according to WHO guidelines, and conditions like hypertension and obesity were defined using criteria from the European Society of Hypertension (ESH), the European Society of Cardiology (ESC), and WHO, respectively. Among the 192 patients, 33 had mild, 62 had moderate, and 97 had severe COVID-19. The median age across the severity groups was 63.2 years. Patients undergoing tracheostomy had a mortality rate of 83.3% versus 22.2% for non-tracheostomy patients (p < 0.001) and presented with significantly higher lung injury, hospitalization duration, and complications. Remarkably, tracheostomized patients were 17.50 times more likely to succumb to the disease (95% CI 4.39-116.91, p < 0.001). Furthermore, pneumothorax increased the mortality risk significantly (OR 22.11, 95% CI 5.72-146.03, p < 0.001). Intriguingly, certain conditions like grade I hypertension and grade II obesity showed a protective effect against mortality, whereas type 2 diabetes mellitus increased mortality risk (univariate OR 2.89, p = 0.001). The presence of certain comorbidities and complications significantly impacts the survival rates of COVID-19 patients in Western Romania. Notably, tracheostomy, pneumothorax, and T2DM were associated with increased mortality. This study underscores the importance of personalized patient care and provides insights for healthcare policymakers in Western Romania to improve clinical management strategies.

A new hybrid method for image approximation using the easy path wavelet transform.

The easy path wavelet transform (EPWT) has recently been proposed by one of the authors as a tool for sparse representations of bivariate functions from discrete data, in particular from image data. The EPWT is a locally adaptive wavelet transform. It works along pathways through the array of function values and exploits the local correlations of the given data in a simple appropriate manner. However, the EPWT suffers from its adaptivity costs that arise from the storage of path vectors. In this paper, we propose a new hybrid method for image approximation that exploits the advantages of the usual tensor product wavelet transform for the representation of smooth images and uses the EPWT for an efficient representation of edges and texture. Numerical results show the efficiency of this procedure.