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
77 ELEKTRONIKA

Download Mobile App




Events

31 Jul 2024 - 02 Aug 2024
02 Aug 2024 - 04 Aug 2024
20 Aug 2024 - 22 Aug 2024

AI Algorithm Monitors Vital Signs and Lab Results to Detect Sepsis before Symptom Onset

By HospiMedica International staff writers
Posted on 25 Jan 2024
Print article
Image: The AI surveillance tool successfully helps to predict sepsis (Photo courtesy of UC San Diego Health)
Image: The AI surveillance tool successfully helps to predict sepsis (Photo courtesy of UC San Diego Health)

Sepsis, a serious blood infection, can initiate a life-threatening chain reaction throughout the body and poses a significant global health challenge. As a dysregulated host response to infection, sepsis affects over 48.9 million people annually worldwide, resulting in approximately 11 million deaths. Early detection of sepsis is crucial for effective treatment, including fluid resuscitation, antibiotic administration, and source control. However, identifying sepsis can be difficult due to its heterogeneous nature. Algorithms designed to aid early sepsis recognition could potentially enhance patient outcomes, yet there is limited research on their real-world impact.

At UC San Diego Health (San Diego, CA, USA), researchers have developed an AI model named COMPOSER to rapidly identify patients at risk of sepsis. This model leverages real-time data to predict sepsis before clear clinical signs emerge. Operating discreetly, COMPOSER continually monitors each patient from the moment they enter the emergency department, analyzing over 150 variables linked to sepsis, including lab results, vital signs, medications, demographics, and medical history. The advanced AI algorithms in COMPOSER can detect subtle patterns not immediately apparent to clinicians. By evaluating these risk factors, the system generates highly accurate sepsis predictions.

Should a patient exhibit a combination of high-risk factors for sepsis, COMPOSER alerts the nursing staff through the hospital’s electronic health record. The nurses then collaborate with physicians to decide the best course of action. If the algorithm determines that the risk patterns are more likely attributed to other conditions, it does not send an alert. Since its activation in December 2022, COMPOSER has been implemented in various in-patient units at UC San Diego Health. A study involving over 6,000 patient admissions before and after deploying COMPOSER in UC San Diego Medical Center and Jacobs Medical Center emergency departments revealed a 17% reduction in mortality, marking the first reported instance of improved patient outcomes through an AI deep-learning model.

“It is because of this AI model that our teams can provide life-saving therapy for patients quicker,” said study co-author Gabriel Wardi, MD, chief of the Division of Critical Care in the Department of Emergency Medicine at UC San Diego School of Medicine.

Related Links:
UC San Diego Health

Gold Member
POC Blood Gas Analyzer
Stat Profile Prime Plus
Gold Member
STI Test
Vivalytic Sexually Transmitted Infection (STI) Array
Silver Member
Compact 14-Day Uninterrupted Holter ECG
NR-314P
New
Orthopedic Extension
AMSCO

Print article

Channels

Surgical Techniques

view channel
Image: Electronic prompt for surgeons may reduce breast cancer overtreatment (Photo courtesy of 123RF)

EHR–Based Nudge Intervention for Surgeons to Reduce Breast Cancer Overtreatment

Sentinel lymph node biopsy (SLNB) is a critical surgical technique used to assess if breast cancer has spread to the underarm lymph nodes, although it's not necessary for all patients. Undergoing SLNB... Read more

Patient Care

view channel
Image: The portable, handheld BeamClean technology inactivates pathogens on commonly touched surfaces in seconds (Photo courtesy of Freestyle Partners)

First-Of-Its-Kind Portable Germicidal Light Technology Disinfects High-Touch Clinical Surfaces in Seconds

Reducing healthcare-acquired infections (HAIs) remains a pressing issue within global healthcare systems. In the United States alone, 1.7 million patients contract HAIs annually, leading to approximately... 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: POCT offers cost-effective, accessible, and immediate diagnostic solutions (Photo courtesy of Flinders University)

POCT for Infectious Diseases Delivers Laboratory Equivalent Pathology Results

On-site pathology tests for infectious diseases in rural and remote locations can achieve the same level of reliability and accuracy as those conducted in hospital laboratories, a recent study suggests.... Read more

Business

view channel
Image: The Innovalve transseptal delivery system is designed to enable safe deployment of the Innovalve implant (Photo courtesy of Innovalve Bio)

Edwards Lifesciences Acquires Sheba Medical’s Innovalve Bio Medical

Edwards Lifesciences (Irvine, CA, USA), a leading company in medical innovations for structural heart disease and critical care, has acquired Innovalve Bio Medical LTD. (Ramat Gan, Israel), an early-stage... Read more
Copyright © 2000-2024 Globetech Media. All rights reserved.