Biomarkers for Prediction of Cardiovascular Complications in Hip Fracture Surgery in the Elderly - Cureus

Credit: camacho9999/ iStock / Getty Images Plus Patients taking GLP-1 medications for diabetes or weight loss had fewer major cardiovascular complications and infections after surgery, according to a study of more than two million adults, including 146,000 taking GLP-1s, presented at the ANESTHESIOLOGY 2026 annual meeting in San Diego. Researchers at UTHealth Houston found that GLP-1 receptor agonist use was associated with 11% lower odds of major adverse cardiovascular events, including heart attack, myocardial injury, stroke and death, and 8% lower odds of postoperative infections.

GLP-1 medications, widely prescribed for diabetes and weight loss, may also help protect people from serious complications after surgery. People taking GLP-1 medications had lower rates of major heart-related complications and infections after surgery than those not taking the drugs, according to results of a study of more than 2 million adults undergoing surgery presented at the ANESTHESIOLOGY 2026 annual meeting.

Mobile app-based monitoring may help address the vulnerable days and weeks after surgery when recovery shifts from the hospital to home with little direct supervision. Patients using mobile apps had a lower risk of serious complications, emergency room visits and readmissions after surgery, but the benefits depended on physician or other clinician involvement, rather than the technology alone, according to research presented at the Anesthesiology 2026 annual meeting. "Just using an app may not make recovery safer," said Mouad Elganga, B. S., lead author of the study and a medical student at the Temerty Faculty of Medicine, University of Toronto.

We’re sharing this article early to provide faster access to peer-reviewed, accepted research. It is citable and carries a permanent DOI. This version is subject to further edits and will be replaced automatically by the final Version of Record. All legal disclaimers apply. AbstractLiver tumors are life-threatening diseases that occur due to the rapid growth of liver cells and even result in a chronic condition. Nevertheless, conventional liver tumor detection struggled with the limitations of poor robustness, difficulties in identifying the complex boundaries, and inconsistent segmentation. Hence, the research introduced a hybrid dual attention-based pyramidal deep convolutional network and deep belief network (HDPCBN) for accurate liver tumor detection. Significantly, the HDPCBN approach deployed a pyramidal architecture to learn deep hierarchical features and intricate patterns of the tumor effectively. Moreover, the integration of the dual attention technique gathers the long-range relationships of both spatial and channel dimensions for accurate liver tumor detection.

The ASCO Quality Care Symposium 2026 (ASCOQLTY26) brought together oncologists, researchers, healthcare professionals, and patient advocates to discuss advances in cancer care quality, patient safety, and healthcare delivery. The meeting focused on improving patient outcomes, addressing disparities in cancer care, and sharing research aimed at strengthening oncology practice. Held on October 16–17, 2026, at the Hynes Convention Center in Boston, Massachusetts, and online, the symposium featured discussions on improving cancer care delivery, advancing patient-centered approaches, and ensuring equitable access to high-quality oncology services.