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
Detecto

Download Mobile App





AI Algorithm Identifies Hospitalized Patients at Highest Risk of Dying From COVID-19

By HospiMedica International staff writers
Posted on 18 May 2022
Print article
Image: Early warning system forecasts who needs critical care for COVID-19 (Photo courtesy of Unsplash)
Image: Early warning system forecasts who needs critical care for COVID-19 (Photo courtesy of Unsplash)

Scientists have developed and validated an algorithm that can help healthcare professionals identify who is most at risk of dying from COVID-19 when admitted to a hospital. The tool, which uses artificial intelligence (AI), could help doctors direct critical care resources to those who need them most, and will be especially valuable to resource-limited countries.

To develop the tool, an international team led by the University of Vienna (Vienna, Austria) used biochemical data from routine blood draws performed on nearly 30,000 patients hospitalised in over 150 hospitals in Spain, the US, Honduras, Bolivia and Argentina between March 2020 and February 2022. This means they were able to capture data from people with different immune statuses - vaccinated, unvaccinated and those with natural immunity - and from people infected with every SARS-CoV-2 variant, from the virus that emerged in Wuhan, China, to the latest Omicron variant.

The resulting algorithm - called COVID-19 Disease Outcome Predictor (CODOP) - uses measurements of 12 blood molecules that are normally collected during admission. This means the predictive tool can be easily integrated into the clinical care of any hospital. CODOP was developed in a multistep process, initially using data from patients hospitalized in more than 120 hospitals in Spain, to ‘train’ the AI system to predict hallmarks of a poor prognosis. The next step was to ensure the tool worked regardless of patients’ immune status or COVID-19 variant, so they tested the algorithm in several subgroups of geographically dispersed patients. The tool still performed well at predicting the risk of in-hospital death during this fluctuating scenario of the pandemic, suggesting the measurements CODOP is based on are truly meaningful biomarkers of whether a patient with COVID-19 is likely to deteriorate.

To test whether the time of taking blood tests affects the tool’s performance, the team compared data from different time points of blood drawn before patients either recovered or died. They found that the algorithm can predict the survival or death of hospitalized patients with high accuracy until nine days before either outcome occurs. Finally, they created two different versions of the tool for use in scenarios where healthcare resources are either operating normally or are under severe pressure. Under normal operational burden, doctors may opt to use an ‘overtriage’ version, which is highly sensitive at picking up people at increased risk of death, at the expense of detecting some people who did not require critical care. The alternative ‘undertriage’ model minimizes the possibility of wrongly selecting people at lower risk of dying, providing doctors with greater certainty that they are directing care to those at the highest risk when resources are severely limited.

“The performance of CODOP in diverse and geographically dispersed patient groups and the ease of use suggest it could be a valuable tool in the clinic, especially in resource-limited countries,” said the leader of this international project and senior author David Gómez-Varela, former Max Planck Group Leader and current Senior Scientist at the Division of Pharmacology and Toxicology, University of Vienna. “We are now working on a follow-up dual model tailored to the current pandemic scenario of increasing infections and cumulative immune protection, which will predict the need for hospitalization within 24 hours for patients within primary care, and intensive care admission within 48 hours for those already hospitalized. We hope to help healthcare systems restore previous standards of routine care before the pandemic took hold.”

Related Links:
University of Vienna 

BMP Whole Blood Analyzer: GEM Premier ChemSTAT
Gold Supplier
Hematology Workflow Solution
DIFF-Line
New
Automatic IFA Processor
AP 16 IF ELITE
New
Lab Incubator
IN-601

Print article
Radcal

Channels

AI

view channel
Image: AI transforms smartwatch ECG signals into a diagnostic tool for heart failure (Photo courtesy of Pexels)

AI-Based Smartwatch Accurately Detects Heart Failure Using ECG Signals

People with a weak heart pump might not have symptoms, but this common form of heart disease affects about 2% of the population and 9% of people over 60. When the heart cannot pump enough oxygen-rich blood,... Read more

Critical Care

view channel
Image: The genetically engineered FcMBL protein can capture more than 100 different microbial species (Photo courtesy of Wyss Institute)

Rapid Pathogen Capture Technology Could Accelerate Diagnosis of Bloodstream Infections and Sepsis

Bloodstream infections (BSIs) with various microbial pathogens can rapidly escalate to life-threatening sepsis when the body is overwhelmed by the multiplying invaders and shuts down its organs’ functions.... Read more

Surgical Techniques

view channel
Image: CystoSmart image enhancement and AI diagnostic tool will enhance cancer detection (Photo courtesy of Claritas HealthTech)

AI Diagnostic Tool Improves Cancer Detection in Cystoscope Images of Bladder

Bladder cancer is the 10th commonest cancer worldwide and the 6th commonest cancer amongst men. It is known to have high recurrence rates and significant risks of disease progression. Early detection of... Read more

Patient Care

view channel
Image: Automated cleaning system allows endoscopes to be cleaned direct from clinic (Photo courtesy of Aston University)

World’s First Automated Endoscope Cleaner Fights Antimicrobial Resistance

Endoscopes are long, thin tubes with a light and camera at one end. Due to the sensitivity of the materials and electronics they cannot be sterilized in an autoclave (a machine that uses steam under pressure),... Read more

Health IT

view channel
Image: Using digital data can improve health outcomes (Photo courtesy of Unsplash)

Electronic Health Records May Be Key to Improving Patient Care, Study Finds

When a patient gets transferred from a hospital to a nearby specialist or rehabilitation facility, it is often difficult for personnel at the new facility to access the patient’s electronic health records... Read more

Business

view channel
Image: The global multiparameter patient monitoring systems market is expected to surpass USD 15 billion by 2028 (Photo courtesy of Unsplash)

Global Multiparameter Patient Monitoring Systems Market Driven by Rising Chronic Illnesses

Multi-parameter patient monitoring equipment is used to assess the vital signs of patients who are suffering from a serious illness. These devices are meant to give the number of data sets on one screen... Read more
Copyright © 2000-2022 Globetech Media. All rights reserved.