Predicting lack of clinical improvement following varicose vein ablation using machine learning.

Objective: Varicose vein ablation is generally indicated in patients with active/healed venous ulcers. However, patient selection for intervention in individuals without venous ulcers is less clear. Tools that predict lack of clinical improvement (LCI) following vein ablation may help guide clinical decision-making but remain limited.

Comprehensive review of virtual assistants in vascular surgery.

Virtual assistants, broadly defined as digital services designed to simulate human conversation and provide personalized responses based on user input, have the potential to improve health care by supporting clinicians and patients in terms of diagnosing and managing disease, performing administrative tasks, and supporting medical research and education. These tasks are particularly helpful in vascular surgery, where the clinical and administrative burden is high due to the rising incidence of vascular disease, the medical complexity of the patients, and the potential for innovation and care advancement. The rapid development of artificial intelligence, machine learning, and natural language processing techniques have facilitated the training of large language models, such as GPT-4 (OpenAI), which can support the development of increasingly powerful virtual assistants.

Using Machine Learning to Predict Outcomes Following Transfemoral Carotid Artery Stenting.

Background: Transfemoral carotid artery stenting (TFCAS) carries important perioperative risks. Outcome prediction tools may help guide clinical decision-making but remain limited. We developed machine learning algorithms that predict 1-year stroke or death following TFCAS.

Predicting inferior vena cava filter complications using machine learning.

Objective: Inferior vena cava (IVC) filter placement is associated with important long-term complications. Predictive models for filter-related complications may help guide clinical decision-making but remain limited. We developed machine learning (ML) algorithms that predict 1-year IVC filter complications using preoperative data.

Bridging the gap between in vitro and in vivo models: a way forward to clinical translation of mitochondrial transplantation in acute disease states.

Mitochondrial transplantation and transfer are being explored as therapeutic options in acute and chronic diseases to restore cellular function in injured tissues. To limit potential immune responses and rejection of donor mitochondria, current clinical applications have focused on delivery of autologous mitochondria. We recently convened a Mitochondrial Transplant Convergent Working Group (CWG), to explore three key issues that limit clinical translation: (1) storage of mitochondria, (2) biomaterials to enhance mitochondrial uptake, and (3) dynamic models to mimic the complex recipient tissue environment.

Electrical impedance tomography-guided positive end-expiratory pressure titration in ARDS: a systematic review and meta-analysis.

Purpose: Assessing efficacy of electrical impedance tomography (EIT) in optimizing positive end-expiratory pressure (PEEP) for acute respiratory distress syndrome (ARDS) patients to enhance respiratory system mechanics and prevent ventilator-induced lung injury (VILI), compared to traditional methods.

Methods: We carried out a systematic review and meta-analysis, spanning literature from January 2012 to May 2023, sourced from Scopus, PubMed, MEDLINE (Ovid), Cochrane, and LILACS, evaluated EIT-guided PEEP strategies in ARDS versus conventional methods. Thirteen studies (3 randomized, 10 non-randomized) involving 623 ARDS patients were analyzed using random-effects models for primary outcomes (respiratory mechanics and mechanical power) and secondary outcomes (PaO/FiO ratio, mortality, stays in intensive care unit (ICU), ventilator-free days).

Machine Learning to Predict Outcomes of Endovascular Intervention for Patients With PAD.

Importance: Endovascular intervention for peripheral artery disease (PAD) carries nonnegligible perioperative risks; however, outcome prediction tools are limited.

Objective: To develop machine learning (ML) algorithms that can predict outcomes following endovascular intervention for PAD.

Design, Setting, And Participants: This prognostic study included patients who underwent endovascular intervention for PAD between January 1, 2004, and July 5, 2023, with 1 year of follow-up.

GLP-1 receptor agonists and atherosclerosis protection: the vascular endothelium takes center stage.

Atherosclerotic cardiovascular disease is a chronic condition that often copresents with type 2 diabetes and obesity. Glucagon-like peptide-1 receptor agonists (GLP-1RAs) are incretin mimetics endorsed by major professional societies for improving glycemic status and reducing atherosclerotic risk in people living with type 2 diabetes. Although the cardioprotective efficacy of GLP-1RAs and their relationship with traditional risk factors are well established, there is a paucity of publications that have summarized the potentially direct mechanisms through which GLP-1RAs mitigate atherosclerosis.

Restoration of blood vessel regeneration in the era of combination SGLT2i and GLP-1RA therapy for diabetes and obesity.

Ischaemic cardiovascular diseases, including peripheral and coronary artery disease, myocardial infarction, and stroke, remain major comorbidities for individuals with type 2 diabetes (T2D) and obesity. During cardiometabolic chronic disease (CMCD), hyperglycaemia and excess adiposity elevate oxidative stress and promote endothelial damage, alongside an imbalance in circulating pro-vascular progenitor cells that mediate vascular repair. Individuals with CMCD demonstrate pro-vascular 'regenerative cell exhaustion' (RCE) characterized by excess pro-inflammatory granulocyte precursor mobilization into the circulation, monocyte polarization towards pro-inflammatory vs.

Using Machine Learning (XGBoost) to Predict Outcomes After Infrainguinal Bypass for Peripheral Artery Disease.

Objective: To develop machine learning (ML) algorithms that predict outcomes after infrainguinal bypass.

Background: Infrainguinal bypass for peripheral artery disease carries significant surgical risks; however, outcome prediction tools remain limited.

Methods: The Vascular Quality Initiative database was used to identify patients who underwent infrainguinal bypass for peripheral artery disease between 2003 and 2023.

Therapeutic Effects of Mesenchymal Stromal Cells Require Mitochondrial Transfer and Quality Control.

Due to their beneficial effects in an array of diseases, Mesenchymal Stromal Cells (MSCs) have been the focus of intense preclinical research and clinical implementation for decades. MSCs have multilineage differentiation capacity, support hematopoiesis, secrete pro-regenerative factors and exert immunoregulatory functions promoting homeostasis and the resolution of injury/inflammation. The main effects of MSCs include modulation of immune cells (macrophages, neutrophils, and lymphocytes), secretion of antimicrobial peptides, and transfer of mitochondria (Mt) to injured cells.

Using machine learning to predict outcomes following suprainguinal bypass.

Objective: Suprainguinal bypass for peripheral artery disease (PAD) carries important surgical risks; however, outcome prediction tools remain limited. We developed machine learning (ML) algorithms that predict outcomes following suprainguinal bypass.

Methods: The Vascular Quality Initiative database was used to identify patients who underwent suprainguinal bypass for PAD between 2003 and 2023.

Empagliflozin improves circulating vascular regenerative cell content in people without diabetes with risk factors for adverse cardiac remodeling.

Sodium glucose-cotransporter 2 (SGLT2) inhibitors have been reported to reduce cardiovascular events and heart failure in people with and without diabetes. These medications have been shown to counter regenerative cell exhaustion in the context of prevalent diabetes. This study sought to determine if empagliflozin attenuates regenerative cell exhaustion in people without diabetes.

Machine learning to predict outcomes following endovascular abdominal aortic aneurysm repair.

Background: Endovascular aneurysm repair (EVAR) for abdominal aortic aneurysm (AAA) carries important perioperative risks; however, there are no widely used outcome prediction tools. The aim of this study was to apply machine learning (ML) to develop automated algorithms that predict 1-year mortality following EVAR.

Methods: The Vascular Quality Initiative database was used to identify patients who underwent elective EVAR for infrarenal AAA between 2003 and 2023.

Using machine learning to predict outcomes following open abdominal aortic aneurysm repair.

Objective: Prediction of outcomes following open abdominal aortic aneurysm (AAA) repair remains challenging with a lack of widely used tools to guide perioperative management. We developed machine learning (ML) algorithms that predict outcomes following open AAA repair.

Methods: The Vascular Quality Initiative (VQI) database was used to identify patients who underwent elective open AAA repair between 2003 and 2023.

SARS-CoV-2 RBD and Its Variants Can Induce Platelet Activation and Clearance: Implications for Antibody Therapy and Vaccinations against COVID-19.

The COVID-19 pandemic caused by SARS-CoV-2 virus is an ongoing global health burden. Severe cases of COVID-19 and the rare cases of COVID-19 vaccine-induced-thrombotic-thrombocytopenia (VITT) are both associated with thrombosis and thrombocytopenia; however, the underlying mechanisms remain inadequately understood. Both infection and vaccination utilize the spike protein receptor-binding domain (RBD) of SARS-CoV-2.

Key Role of Mesenchymal Stromal Cell Interaction with Macrophages in Promoting Repair of Lung Injury.

Lung macrophages (Mφs) are essential for pulmonary innate immunity and host defense due to their dynamic polarization and phenotype shifts. Mesenchymal stromal cells (MSCs) have secretory, immunomodulatory, and tissue-reparative properties and have shown promise in acute and chronic inflammatory lung diseases and in COVID-19. Many beneficial effects of MSCs are mediated through their interaction with resident alveolar and pulmonary interstitial Mφs.

Characterizing the Impact of Procedure Funding on the Covid-19 Generated Procedure Gap in Ontario: A Population-Based Analysis.

Background: Surgical procedures in Canada were historically funded through global hospital budgets. Activity-based funding models were developed to improve access, equity, timeliness, and value of care for priority areas. COVID-19 upended health priorities and resulted in unprecedented disruptions to surgical care, which created a significant procedure gap.

Validating fatty acid binding protein 3 as a diagnostic and prognostic biomarker for peripheral arterial disease: A three-year prospective follow-up study.

Background: Patients with peripheral arterial disease (PAD) often remain undiagnosed and therefore suboptimally managed. Here, we investigated the diagnostic and prognostic potential of fatty acid binding protein 3 (FABP3) in patients with PAD.

Methods: In the discovery phase, 374 PAD and 184 non-PAD patients were recruited from vascular surgery ambulatory clinics at St.

Non-steroidal mineralocorticoid receptor antagonists in cardiorenal disease.

Despite existing treatments, patients with heart failure and chronic kidney disease (CKD) remain at high risk for adverse outcomes and progression to end-stage disease. Steroidal mineralocorticoid receptor antagonists (MRAs) such as spironolactone and eplerenone reduce mortality but remain under-prescribed due to the perceived risk of hyperkalaemia and hormonal side effects. The discovery of non-steroidal MRAs represents a major new dimension in cardiorenal disease therapy.