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





Machine Learning Tool Gives Early Warning of Cardiac Issues or Blood Clots in COVID Patients

By HospiMedica International staff writers
Posted on 15 Jan 2021
Print article
Illustration
Illustration
A team of biomedical engineers and heart specialists have developed an algorithm that warns doctors several hours before hospitalized COVID-19 patients experience cardiac arrest or blood clots.

The COVID-HEART predictor developed using data from patients treated for COVID-19 by scientists at the Johns Hopkins University (JHU; Baltimore, MD, USA) can forecast cardiac arrest in COVID-19 patients with a median early warning time of 18 hours and predict blood clots three days in advance. The machine-learning algorithm was built with more than 100 clinical data points, demographic information and laboratory results obtained from the JH-CROWN registry that Johns Hopkins established to collect COVID data from every patient in the hospital system. The scientists also added other variables reported by doctors on Twitter and from other pre-print papers.

The team did not anticipate that electrocardiogram data would play a critical role in the prediction of blood clotting. But once it was added, ECG data became one of the most accurate indicators for the condition. The next step for the researchers is to develop the best method for setting up the technology in hospitals to aid with the care of COVID-19 patients.

“It’s an early warning system to predict in real time these two outcomes in hospitalized COVID patients,” said senior author Natalia Trayanova, the Murray B. Sachs professor of Biomedical Engineering and a professor of medicine. “The continuously updating predictor can help hospitals allocate the appropriate resources and proper interventions to attain the best outcomes for patients.”

“The COVID-HEART predictor tool could help in the rapid triage of COVID-19 patients in the clinical setting especially when resources are limited,” said Allison Hays, associate professor of medicine in the Johns Hopkins University School of Medicine and the project’s main clinical collaborator. “This may have implications for the treatment and closer monitoring of Covid-19 patients to help prevent these poor outcomes.”

Related Links:
Johns Hopkins University

Gold Member
Real-Time Diagnostics Onscreen Viewer
GEMweb Live
Gold Member
POC Blood Gas Analyzer
Stat Profile Prime Plus
New
Mobile Barrier
Tilted Mobile Leaded Barrier
New
Prenatal Risk Calculation System
PRISCA

Print article

Channels

Surgical Techniques

view channel
Image: By optimizing the material design during 3D printing, the enhancement of the bioactivity of porous tantalum (pTa) bone implants can be better achieved (Photo courtesy of Manyuan Wu, Mingchun Zhao, Ying Cai)

3D-Printed Porous Tantalum an Emerging Material for New Generation of Orthopedic Implants

Bones are the hard organs that form the endoskeleton of vertebrates, featuring a complex inner and outer structure that allows them to maintain hardness while minimizing weight. Their roles include facilitating... 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.