Antimicrobial Use during the SARS-CoV-2 Pandemic in a Greek Tertiary University Hospital.

In cases of SARS-CoV-2 hospitalization, despite low bacterial co-infection rates, antimicrobial use may be disproportionately high. Our aim was to quantify such usage in COVID-19 patients and identify factors linked to increased antibiotic use. We retrospectively studied patients with SARS-CoV-2 infection who were hospitalized at our institution during the pandemic. In the initial two waves of the pandemic, antimicrobial use was notably high (89% in the first wave and 92% in the second), but it decreased in subsequent waves. Elevated procalcitonin (>0.5 µg/mL) and C-reactive protein (>100 mg/L) levels were linked to antibiotic usage, while prior vaccination reduced antibiotic incidence. Antimicrobial use decreased in the pandemic, suggesting enhanced comprehension of SARS-CoV-2's natural course. Additionally, it was correlated with heightened SARS-CoV-2 severity, elevated procalcitonin, and C-reactive protein levels.

Machine Learning Models Predict the Need of Amputation and/or Peripheral Artery Revascularization in Hypertensive Patients Within 7-Years Follow-Up.

Lower extremity amputation and requirement of peripheral artery revascularization are common outcomes of undiagnosed peripheral artery disease patients. In the current work, prediction models for the need of amputation or peripheral revascularization focused on hypertensive patients within seven years follow up are employed. We applied machine learning (ML) models using classifiers such as Extreme Gradient Boost (XGBoost), Random Forest (RF) and Adaptive Boost (AdaBoost), that will allow clinicians to identify the patients at risk of these two endpoints using simple clinical data. We used the non-interventional cohort of the getABI study in the primary care setting, selecting 4,191 hypertensive patients out of 6,474 patients with age over 65 years old and followed up for vascular events or death up to 7 years. During this follow up period, 150 patients underwent either amputation or peripheral revascularization or both. Accuracy, Specificity, Sensitivity and Area under the receiver operating characteristic curve (AUC) were estimated for each machine learning model. The results demonstrate Random Forest as the most accurate model for the prediction of the composite endpoint in hypertensive patients within 7 years follow-up, achieving 73.27 % accuracy.Clinical Relevance-This study assists clinicians to better predict and treat these serious outcomes, amputation and peripheral revascularization in hypertensive patients.

Inflammation and Venous Thromboembolism in Hospitalized Patients with COVID-19.

BACKGROUND: A link between inflammation and venous thromboembolism (VTE) in COVID-19 disease has been suggested pathophysiologically and clinically. The aim of this study was to investigate the association between inflammation and disease outcomes in adult hospitalized COVID-19 patients with VTE. METHODS: This was a retrospective observational study, including quantitative and qualitative data collected from COVID-19 patients hospitalized at the Infectious Diseases Unit (IDU) of the University Hospital of Ioannina, from 1 March 2020 to 31 May 2022. Venous thromboembolism was defined as a diagnosis of pulmonary embolism (PE) and/or vascular tree-in-bud in the lungs. The burden of disease, assessed by computed tomography of the lungs (CTBoD), was quantified as the percentage (%) of the affected lung parenchyma. The study outcomes were defined as death, intubation, and length of hospital stay (LoS). A chi-squared test and univariate logistic regression analyses were performed in IBM SPSS 28.0. RESULTS: After propensity score matching, the final study cohort included 532 patients. VTE was found in 11.2% of the total population. In patients with VTE, we found that lymphocytopenia and a high neutrophil/lymphocyte ratio were associated with an increased risk of intubation and death, respectively. Similarly, CTBoD > 50% was associated with a higher risk of intubation and death in this group of patients. The triglyceride-glucose (TyG) index was also linked to worse outcomes. CONCLUSIONS: Inflammatory indices were associated with VTE. Lymphocytopenia and an increased neutrophil-to-lymphocyte ratio negatively impacted the disease's prognosis and outcomes. Whether these indices unfavorably affect outcomes in COVID-19-associated VTE must be further evaluated.

Coronary computed tomography angiography-based endothelial wall shear stress in normal coronary arteries.

Endothelial wall shear stress (ESS) is a biomechanical force which plays a role in the formation and evolution of atherosclerotic lesions. The purpose of this study is to evaluate coronary computed tomography angiography (CCTA)-based ESS in coronary arteries without atherosclerosis, and to assess factors affecting ESS values. CCTA images from patients with suspected coronary artery disease were analyzed to identify coronary arteries without atherosclerosis. Minimal and maximal ESS values were calculated for 3-mm segments. Factors potentially affecting ESS values were examined, including sex, lumen diameter and distance from the ostium. Segments were categorized according to lumen diameter tertiles into small (< 2.6 mm), intermediate (2.6-3.2 mm) or large (≥ 3.2 mm) segments. A total of 349 normal vessels from 168 patients (mean age 59 ± 9 years, 39% men) were included. ESS was highest in the left anterior descending artery compared to the left circumflex artery and right coronary artery (minimal ESS 2.3 Pa vs. 1.9 Pa vs. 1.6 Pa, p < 0.001 and maximal ESS 3.7 Pa vs. 3.0 Pa vs. 2.5 Pa, p < 0.001). Men had lower ESS values than women, also after adjusting for lumen diameter (p < 0.001). ESS values were highest in small segments compared to intermediate or large segments (minimal ESS 3.8 Pa vs. 1.7 Pa vs. 1.2 Pa, p < 0.001 and maximal ESS 6.0 Pa vs. 2.6 Pa vs. 2.0 Pa, p < 0.001). A weak to strong correlation was found between ESS and distance from the ostium (ρ = 0.22-0.62, p < 0.001). CCTA-based ESS values increase rapidly and become widely scattered with decreasing lumen diameter. This needs to be taken into account when assessing the added value of ESS beyond lumen diameter in highly stenotic lesions.

Machine Learning Models for Cardiovascular Disease Events Prediction.

Cardiovascular diseases (CVDs) are among the most serious disorders leading to high mortality rates worldwide. CVDs can be diagnosed and prevented early by identifying risk biomarkers using statistical and machine learning (ML) models, In this work, we utilize clinical CVD risk factors and biochemical data using machine learning models such as Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), Naïve Bayes (NB), Extreme Grading Boosting (XGB) and Adaptive Boosting (AdaBoost) to predict death caused by CVD within ten years of follow-up. We used the cohort of the Ludwigshafen Risk and Cardiovascular Health (LURIC) study and 2943 patients were included in the analysis (484 annotated as dead due to CVD). We calculated the Accuracy (ACC), Precision, Recall, F1-Score, Specificity (SPE) and area under the receiver operating characteristic curve (AUC) of each model. The findings of the comparative analysis show that Logistic Regression has been proven to be the most reliable algorithm having accuracy 72.20 %. These results will be used in the TIMELY study to estimate the risk score and mortality of CVD in patients with 10-year risk.

A proof-of-concept study for the prediction of the de-novo atherosclerotic plaque development using finite elements.

The type of the atherosclerotic plaque has significant clinical meaning since plaque vulnerability depends on its type. In this work, we present a computational approach which predicts the development of new plaques in coronary arteries. More specifically, we employ a multi-level model which simulates the blood fluid dynamics, the lipoprotein transport and their accumulation in the arterial wall and the triggering of inflammation using convection-diffusion-reaction equations and in the final level, we estimate the plaque volume which causes the arterial wall thickening. The novelty of this work relies on the conceptual approach that using the information from 94 patients with computed tomography coronary angiography (CTCA) imaging at two time points we identify the correlation of the computational results with the real plaque components detected in CTCA. In the next step, we use these correlations to generate two types of de-novo plaques: calcified and non-calcified. Evaluation of the model's performance is achieved using eleven patients, who present de-novo plaques at the follow-up imaging. The results demonstrate that the computationally generated plaques are associated significantly with the real plaques indicating that the proposed approach could be used for the prediction of specific plaque type formation.