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.

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.