We use cookies to understand how you use our site and to improve your experience. This includes personalizing content and advertising. To learn more, click here. By continuing to use our site, you accept our use of cookies. Cookie Policy.

Features Partner Sites Information LinkXpress hp
Sign In
Advertise with Us
ARAB HEALTH - INFORMA

Download Mobile App




Events

27 Jan 2025 - 30 Jan 2025
15 Feb 2025 - 17 Feb 2025

New Aneurysm Prediction System Demonstrates 98% Accuracy

By HospiMedica International staff writers
Posted on 21 Dec 2023
Print article
Image: A 4D flow MRI scan of a human aorta (Photo courtesy of Ethan Johnson)
Image: A 4D flow MRI scan of a human aorta (Photo courtesy of Ethan Johnson)

An aortic aneurysm is a condition where the aorta, the body's largest artery, swells to more than 1.5 times its normal size, leading to a weakened arterial wall. Over time, this weakening can progress to the point where the aorta can't handle the pressure from blood flow, potentially causing a rupture. While this occurrence is relatively rare, it's typically unforeseen and is almost invariably lethal. Despite the seriousness of aortic aneurysms, the precise mechanics behind their development have remained a mystery, leaving the medical field without a standardized method for predicting them. Researchers have now introduced the first physics-based metric to predict the possibility of an individual experiencing a fatal aortic aneurysm, which often exhibits no warning signs until it's too late.

Traditionally, medical professionals gauge the risk of rupture by considering various factors such as the patient's age, lifestyle, and the aorta's size. They monitor the aorta's growth through periodic imaging tests. When the aorta enlarges rapidly or excessively, the patient might undergo surgery to reinforce the weakened wall, a procedure not without its own set of risks. Researchers at Northwestern University (Evanston, IL, USA) set out to eliminate the guesswork from predicting future aneurysms by focusing on the fundamental physics underlying the problem. In their study, they identified abnormal aortic growth by detecting subtle oscillations in the blood vessel's wall. These oscillations or "flutterings" occur as blood flows through the aorta, causing it to ripple similarly to a flag in the wind. Whereas a stable flow signifies healthy, regular growth, an unstable flutter strongly indicates potential abnormal growth and risk of rupture.

This groundbreaking approach led to the development of the "flutter instability parameter" (FIP), a metric that has shown a 98% accuracy rate in predicting future aneurysms, typically three years post-initial measurement. For a personalized FIP assessment, patients require just one 4D flow magnetic resonance imaging (MRI) scan. This predictive metric could enable doctors to proactively manage high-risk patients with medications or other interventions, potentially preventing the aorta from reaching a critical size. The research team is now investigating whether the FIP can throw light on the development of other cardiovascular conditions and determining which prevention strategies might be most effective based on patient-specific FIPs to halt aneurysm progression.

“Aortic aneurysms are colloquially referred to as ‘silent killers’ because they often go undetected until catastrophic dissection or rupture occurs,” said Northwestern’s Neelesh A. Patankar, senior author of the study. “The fundamental physics driving aneurysms has been unknown. As a result, there is no clinically approved protocol to predict them. Now, we have demonstrated the efficacy of a physics-based metric that helps predict future growth. This could be transformational in predicting cardiac pathologies.”

Related Links:
Northwestern University

Gold Member
SARS‑CoV‑2/Flu A/Flu B/RSV Sample-To-Answer Test
SARS‑CoV‑2/Flu A/Flu B/RSV Cartridge (CE-IVD)
Gold Member
POC Blood Gas Analyzer
Stat Profile Prime Plus
New
Hospital Data Analytics Software
OR Companion
New
Monitor Cart
Tryten S5

Print article

Channels

Surgical Techniques

view channel
Image: The surgical team and the Edge Multi-Port Endoscopic Surgical Robot MP1000 surgical system (Photo courtesy of Wei Zhang)

Endoscopic Surgical System Enables Remote Robot-Assisted Laparoscopic Hysterectomy

Telemedicine enables patients in remote areas to access consultations and treatments, overcoming challenges related to the uneven distribution and availability of medical resources. However, the execution... Read more

Patient Care

view channel
Image: The portable biosensor platform uses printed electrochemical sensors for the rapid, selective detection of Staphylococcus aureus (Photo courtesy of AIMPLAS)

Portable Biosensor Platform to Reduce Hospital-Acquired Infections

Approximately 4 million patients in the European Union acquire healthcare-associated infections (HAIs) or nosocomial infections each year, with around 37,000 deaths directly resulting from these infections,... Read more

Health IT

view channel
Image: First ever institution-specific model provides significant performance advantage over current population-derived models (Photo courtesy of Mount Sinai)

Machine Learning Model Improves Mortality Risk Prediction for Cardiac Surgery Patients

Machine learning algorithms have been deployed to create predictive models in various medical fields, with some demonstrating improved outcomes compared to their standard-of-care counterparts.... Read more

Point of Care

view channel
Image: The acoustic pipette uses sound waves to test for biomarkers in blood (Photo courtesy of Patrick Campbell/CU Boulder)

Handheld, Sound-Based Diagnostic System Delivers Bedside Blood Test Results in An Hour

Patients who go to a doctor for a blood test often have to contend with a needle and syringe, followed by a long wait—sometimes hours or even days—for lab results. Scientists have been working hard to... Read more
Copyright © 2000-2024 Globetech Media. All rights reserved.