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 Model Accurately Identifies High-Risk Surgical Patients

By HospiMedica International staff writers
Posted on 18 Jul 2023
Print article
Image: Accurate and flexible ML model predicts patients at high-risk for complications after surgery (Photo courtesy of Freepik)
Image: Accurate and flexible ML model predicts patients at high-risk for complications after surgery (Photo courtesy of Freepik)

Prior to the COVID-19 pandemic, complications occurring 30 days post-surgery were the third leading cause of death worldwide, claiming approximately 4.2 million lives annually. Recognizing patients at high risk for post-surgical complications is crucial to improving survival rates and reducing healthcare costs. Researchers have now employed machine learning to develop and implement an efficient, adaptable model for identifying hospitalized patients at high risk for post-surgical complications.

Researchers and physicians at the University of Pittsburgh (Pittsburgh, PA, USA) and UPMC (Pittsburgh, PA, USA) developed this model by training the algorithm on the medical records of over 1.25 million surgical patients. The focus of the model was on mortality and the occurrence of major cerebral or cardiac events, such as stroke or heart attack, following surgery. The model was then validated using the records of another 200,000 surgical patients from UPMC. After validation, the model was implemented across 20 UPMC hospitals. Each morning, the program reviews the electronic medical records of patients scheduled for surgery and flags those identified as high risk. This alert enables clinical teams to improve care coordination and introduce prehabilitation measures before surgery, such as healthier lifestyle choices or a referral to the UPMC Center for Perioperative Care, thus lowering the risk of complications. Clinicians can also activate the model on demand at any time.

Additionally, the research team compared their model with the industry standard, the American College of Surgeon’s National Surgical Quality Improvement Program (ACS NSQIP), to further gauge its effectiveness. The ACS NSQIP, used at hospitals nationwide, requires manual input of patient information by clinicians and is unable to make predictions if data is missing. The researchers found their model to be more effective at identifying high-risk patients than the ACS NSQIP. As the model continues to be fine-tuned and developed, the researchers plan to train the program to predict the likelihood of other complications, such as sepsis and respiratory issues, that often result in prolonged hospital stays after surgery.

“We designed our model with the health care worker in mind,” said Aman Mahajan, M.D., Ph.D., M.B.A., chair of Anesthesiology and Perioperative Medicine, Pitt School of Medicine, and director of UPMC Perioperative and Surgical Services. “Since our model is completely automated and can make educated predictions even if some data are missing, it adds almost no additional burden to clinicians while providing them a reliable and useful tool.”

Related Links:
University of Pittsburgh 
UPMC 

Gold Member
12-Channel ECG
CM1200B
Gold Member
STI Test
Vivalytic Sexually Transmitted Infection (STI) Array
New
Documentation System For Blood Banks
HettInfo II
New
Fetal and Maternal Monitor
F9 Series

Print article

Channels

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.