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
Sign In
Advertise with Us

Download Mobile App




Events

ATTENTION: Due to the COVID-19 PANDEMIC, many events are being rescheduled for a later date, converted into virtual venues, or altogether cancelled. Please check with the event organizer or website prior to planning for any forthcoming event.

Machine Learning Helps Predict Critical Circulatory Failure

By HospiMedica International staff writers
Posted on 26 Mar 2020
Print article
A new study shows that an artificial intelligence (AI) method that fuses medically relevant information enables critical circulatory failure to be predicted in the intensive care unit (ICU) several hours before it occurs.

Developed at the Swiss Federal Institute of Technology (ETH; Zurich, Switzerland) and Bern University Hospital (Inselspital; Switzerland), the early-warning platform integrates measurements from multiple systems using a high-resolution database that holds 240 patient-years of data. For the study, the researchers used anonymized data from 36,000 admissions to ICUs, and were able to show that just 20 of these variables, including blood pressure, pulse, various blood values, the patient's age, and medications administered were sufficient to make accurate predictions.

In a trial run of the algorithms developed, they were able to predict 90% of circulatory-failure events, with 82% of them identified more than two hours in advance. On average, the system raised 0.05 alarms per patient and hour. The model was also externally validated in an independent patient cohort. The researchers concluded that the model can provide early identification of patients at risk for circulatory failure with a much lower false-alarm rate than conventional threshold-based systems. The study was published on March 9, 2020, in Nature Medicine.

“In intensive care units today, we have to deal with a multitude of alarm systems, but they're not very accurate. Often, they trigger false alarms or they give us only a short advance warning, which can delay initiation of adequate measures to support a patient’s circulation,” said senior author Tobias Merz, MD, of Inselspital. “The aim is to use the method for real-time evaluation of hospital patients' vital signs to provide an early warning system for the medical staff on duty, who, in turn, can take appropriate action at an early stage.”

The constant sounds of alarms from blood pressure machines, ventilators, and heart monitors cause a "tuning out" of the sounds due to the brain adjusting to stimulation. This issue is present in hospitals, in home care providers, nursing homes and other medical facilities alike.

Related Links:
Swiss Federal Institute of Technology
Bern University Hospital


Gold Supplier
12-Channel ECG
CM1200B
New
Mobile X-Ray Table
X Mobil
New
Abdominal Stent Graft Platform
Ovation iX
New
Flat Panel Detector (FPD)
DRX-LC Detector

Print article
FIME - Informa

Channels

AI

view channel
Image: The AI tool can also tackle dangerous inequalities in heart attack diagnosis (Photo courtesy of Freepik)

AI Algorithm Integrates Cardiac Troponin Test Results with Clinical Data to Quickly Rule out Heart Attacks in Patients

The accepted standard for diagnosing myocardial infarction, or heart attack, involves assessing the blood for troponin levels. However, this approach applies the same benchmark for all patients, failing... Read more

Surgical Techniques

view channel
Image: The deployable electrodes are ideal for minimally invasive craniosurgery (Photo courtesy of EPFL)

Soft Robotic Electrode Offers Minimally Invasive Solution for Craniosurgery

Minimally invasive medical procedures offer numerous benefits to patients, including decreased tissue damage and shorter recovery periods. However, creating equipment that can pass through a small opening... 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: IntelliSep is the first FDA-cleared diagnostic tool to assess cellular host response to aid in identifying ED patients with sepsis (Photo courtesy of Cytovale)

Rapid Microfluidic Test Demonstrates Efficacy as Diagnostic Aid to Improve Sepsis Triage in ED

Sepsis is the primary cause of mortality worldwide, accounting for over 350,000 fatalities annually in the United States alone, a figure that surpasses deaths from opioid overdoses, prostate cancer, and... Read more
Copyright © 2000-2023 Globetech Media. All rights reserved.