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.

Preoperative radiological features in predicting complications of carotid body tumor resection.

Objective: Carotid body tumors (CBTs) are rare neoplasms that pose significant surgical challenges. This study aims to evaluate the predictive utility of preoperative radiological characteristics on postoperative complications in patients undergoing CBT resection at a tertiary care center.

Methods: A retrospective analysis was conducted on 106 patients who underwent CBT resection between 2003 and 2023.

Pediatric carotid body tumors: A case report and systematic review.

Carotid body tumors (CBTs), or chemodectomas, are rare, especially in the pediatric population. They often present with minimal symptoms, making timely diagnosis challenging. This case report and systematic review highlights a distinctive presentation and summarize the current evidence on pediatric CBTs.

Is there an association between arteriovenous fistulas and axillary artery aneurysms? A report of two cases.

Introduction: Axillary artery aneurysms are rare vascular conditions that can present with various clinical manifestations, including neurological deficits and vascular compromise. While the underlying pathophysiology remains complex and multifactorial, potential associations with trauma, arteriovenous fistula formation, and atherosclerosis have been reported.

Presentation Of Case: Two male patients, aged 33 and 38, with a history of kidney transplantation and previous arteriovenous fistula (AVF) presented with symptoms of upper limb ischemia and neurological compromise.

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.

Predicting Outcomes Following Lower Extremity Endovascular Revascularization Using Machine Learning.

Background: Lower extremity endovascular revascularization for peripheral artery disease carries nonnegligible perioperative risks; however, outcome prediction tools remain limited. Using machine learning, we developed automated algorithms that predict 30-day outcomes following lower extremity endovascular revascularization.

Methods And Results: The National Surgical Quality Improvement Program targeted vascular database was used to identify patients who underwent lower extremity endovascular revascularization (angioplasty, stent, or atherectomy) for peripheral artery disease between 2011 and 2021.

Predicting the need for subclavian artery revascularization in thoracic endovascular aortic repair: A systematic review and meta-analysis.

Objective: This systematic review and meta-analysis aims to investigate the effectiveness of left subclavian artery revascularization compared with non-revascularization in thoracic endovascular aortic repair, and to summarize the current evidence on its indications.

Methods: A computerized search was conducted across multiple databases, including MEDLINE, SCOPUS, Cochrane Library, and Web of Science, for studies published up to November 2023. Study selection, data abstraction, and quality assessment (using the Newcastle-Ottawa Scale) were independently conducted by two reviewers, with a third author resolving discrepancies.

Development and evaluation of a prediction model for peripheral artery disease-related major adverse limb events using novel biomarker data.

Objective: Prognostic tools for individuals with peripheral artery disease (PAD) remain limited. We developed prediction models for 3-year PAD-related major adverse limb events (MALE) using demographic, clinical, and biomarker data previously validated by our group.

Methods: We performed a prognostic study using a prospectively recruited cohort of patients with PAD (n = 569).

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.

Predicting outcomes following lower extremity open revascularization using machine learning.

Lower extremity open revascularization is a treatment option for peripheral artery disease that carries significant peri-operative risks; however, outcome prediction tools remain limited. Using machine learning (ML), we developed automated algorithms that predict 30-day outcomes following lower extremity open revascularization. The National Surgical Quality Improvement Program targeted vascular database was used to identify patients who underwent lower extremity open revascularization for chronic atherosclerotic disease between 2011 and 2021.

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.

Predicting Major Adverse Cardiovascular Events Following Carotid Endarterectomy Using Machine Learning.

Background Carotid endarterectomy (CEA) is a major vascular operation for stroke prevention that carries significant perioperative risks; however, outcome prediction tools remain limited. The authors developed machine learning algorithms to predict outcomes following CEA. Methods and Results The National Surgical Quality Improvement Program targeted vascular database was used to identify patients who underwent CEA between 2011 and 2021.

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.

Predicting outcomes following open revascularization for aortoiliac occlusive disease using machine learning.

Objective: Open surgical treatment options for aortoiliac occlusive disease carry significant perioperative risks; however, outcome prediction tools remain limited. Using machine learning (ML), we developed automated algorithms that predict 30-day outcomes following open aortoiliac revascularization.

Methods: The National Surgical Quality Improvement Program (NSQIP) targeted vascular database was used to identify patients who underwent open aortoiliac revascularization for atherosclerotic disease between 2011 and 2021.

Predicting Outcomes Following Endovascular Abdominal Aortic Aneurysm Repair Using Machine Learning.

Objective: To develop machine learning (ML) models that predict outcomes following endovascular aneurysm repair (EVAR) for abdominal aortic aneurysm (AAA).

Background: EVAR carries non-negligible perioperative risks; however, there are no widely used outcome prediction tools.

Methods: The National Surgical Quality Improvement Program targeted database was used to identify patients who underwent EVAR for infrarenal AAA between 2011 and 2021.

Using machine learning to predict outcomes following carotid endarterectomy.

Objective: Prediction of outcomes following carotid endarterectomy (CEA) remains challenging, with a lack of standardized tools to guide perioperative management. We used machine learning (ML) to develop automated algorithms that predict outcomes following CEA.

Methods: The Vascular Quality Initiative (VQI) database was used to identify patients who underwent CEA between 2003 and 2022.

A novel Canadian multidisciplinary acute care pathway for people hospitalised with a diabetic foot ulcer.

This manuscript describes the implementation and initial evaluation of a novel Canadian acute care pathway for people with a diabetic foot ulcer (DFU). A multidisciplinary team developed and implemented an acute care pathway for patients with a DFU who presented to the emergency department (ED) and required hospitalisation at a tertiary care hospital in Canada. Processes of care, length of stay (LOS), and hospitalisation costs were considered through retrospective cohort study of all DFU hospitalizations from pathway launch in December 2018 to December 2020.

The influence of diabetes on temporal trends in lower extremity revascularisation and amputation for peripheral artery disease: A population-based repeated cross-sectional analysis.

Aim/hypothesis: To describe the influence of diabetes on temporal changes in rates of lower extremity revascularisation and amputation for peripheral artery disease (PAD) in Ontario, Canada.

Methods: In this population-based repeated cross-sectional study, we calculated annual rates of lower extremity revascularisation (open or endovascular) and amputation (toe, foot or leg) related to PAD among Ontario residents aged ≥40 years between 2002 and 2019. Annual rate ratios (relative to 2002) adjusted for changes in diabetes prevalence alone, as well as fully adjusted for changes in demographics, diabetes and other comorbidities, were estimated using generalized estimating equation models to model population-level effects while accounting for correlation within units of observation.

Regional variation in lower extremity revascularization and amputation for peripheral artery disease.

Objective: The aim of this study was to quantify the recent and historical extent of regional variation in revascularization and amputation for peripheral artery disease (PAD).

Methods: This was a repeated cross-sectional analysis of all Ontarians aged 40 years or greater between 2002 and 2019. The co-primary outcomes were revascularization (endovascular or open) and major (above-ankle) amputation for PAD.