Predicting outcomes following endovascular aortoiliac revascularization using machine learning.

Endovascular aortoiliac revascularization is a common treatment option for peripheral artery disease that carries non-negligible risks. Outcome prediction tools may support clinical decision-making but remain limited. We developed machine learning algorithms that predict 30-day post-procedural outcomes.

Mitochondrial Transplantation Reduces Injury and Improves Liver Function in a Porcine Model of Hemi-hepatic Ischemia/Reperfusion.

Objective: To assess mitochondrial transplantation (MitoTx) via portal vein infusion to reduce liver ischemia-reperfusion injury (I/R) in a survival porcine model.

Summary Background Data: MitoTx has been shown to alleviate I/R injury in various organs.

Methods: Male Yorkshire pigs (38±1 kg) were subjected to 2 hours of ischemia in the left hemi-liver (left portal-triad clamping), and at the beginning of reperfusion (marked as t=0 h), animals received a 1-hour infusion of autologous mitochondria (MT, 7×10^9/kg) or saline (controls) via the portal vein.

Predicting mortality risk following major lower extremity amputation using machine learning.

Objective: Major lower extremity amputation for advanced vascular disease involves significant perioperative risks. Although outcome prediction tools could aid in clinical decision-making, they remain limited. To address this, we developed machine learning (ML) algorithms capable of predicting 1-year mortality following major lower extremity amputation.

Predicting outcomes following open abdominal aortic aneurysm repair using machine learning.

Patients undergoing open surgical repair of abdominal aortic aneurysm (AAA) have a high risk of post-operative complications. However, there are no widely used tools to predict surgical risk in this population. We used machine learning (ML) techniques to develop automated algorithms that predict 30-day outcomes following open AAA repair.

Predicting Outcomes Following Carotid Artery Stenting Using Machine Learning.

Background: Carotid artery stenting (CAS) carries important perioperative risks. Outcome prediction tools may help guide clinical decision-making but remain limited. We developed machine learning (ML) algorithms that predict 30-day outcomes following transfemoral CAS.

Using Machine Learning to Predict Outcomes Following Thoracic and Complex Endovascular Aortic Aneurysm Repair.

Background: Thoracic endovascular aortic repair (TEVAR) and complex endovascular aneurysm repair (EVAR) are complex procedures that carry a significant risk of complications. While risk prediction tools can aid in clinical decision making, they remain limited. We developed machine learning algorithms to predict outcomes following TEVAR and complex EVAR.

Mitochondrial Transplantation: A Novel Therapy for Liver Ischemia/Reperfusion Injury.

Objective: To investigate the hepatoprotective effects of mitochondrial transplantation (MTx) in a murine liver ischemia/reperfusion (I/R) model.

Background: Sequential liver ischemia, followed by reperfusion (I/R), is a pathophysiological process underlying hepatocellular injury in a number of clinical contexts, such as hemorrhagic shock/resuscitation, major elective liver surgery, and organ transplantation. A unifying pathogenic consequence of I/R is mitochondrial dysfunction.

Using machine learning to predict outcomes following transcarotid artery revascularization.

Transcarotid artery revascularization (TCAR) is a relatively new and technically challenging procedure that carries a non-negligible risk of complications. Risk prediction tools may help guide clinical decision-making but remain limited. We developed machine learning (ML) algorithms that predict 1-year outcomes following TCAR.

Freeze-dried plasma: Hemostasis and biophysical analyses for damage control resuscitation.

Background: Effective hemorrhage protocols prioritize immediate hemostatic resuscitation to manage hemorrhagic shock. Prehospital resuscitation using blood products, such as whole blood or alternatively dried plasma in its absence, has the potential to improve outcomes in hemorrhagic shock patients. However, integrating blood products into prehospital care poses substantial logistical challenges due to issues with storage, transport, and administration in field environments.

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) after vein ablation may help guide clinical decision-making but remain limited.

SR3677 IS HEPATOPROTECTIVE IN MURINE LIVER ISCHEMIA/REPERFUSION INJURY: POTENTIAL ROLE OF BNIP3L/NIX (BCL2/ADENOVIRUS E1B 19 KDA PROTEIN-INTERACTING PROTEIN 3-LIKE).

SR3677, a highly selective rho-associated protein kinase 2 inhibitor, administered prior to liver ischemia/reperfusion injury, induced hepatoprotection in both wild-type and Parkin2-/- mice. Experiments in hepatocytes identified BNIP3L/NIX, as a potential mediator of the hepatoprotective effects of SR3677.

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.

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.

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.