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




Machine Learning Model Improves Mortality Risk Prediction for Cardiac Surgery Patients

By HospiMedica International staff writers
Posted on 26 May 2023
Print article
Image: First ever institution-specific model provides significant performance advantage over current population-derived models (Photo courtesy of Mount Sinai)
Image: First ever institution-specific model provides significant performance advantage over current population-derived models (Photo courtesy of Mount Sinai)

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. In cardiac surgery, risk scores provided by The Society of Thoracic Surgeons (STS) are often used to evaluate a patient's procedural risk. While these scores remain vital for hospitals to assess and improve their performance, they are drawn from population-wide data, which can fall short of accurately predicting risk for specific patients with complex pathologies.

Now, cardiovascular surgeons and data science specialists at Mount Sinai (New York, NY, USA) have developed a machine learning-based model that predicts mortality risk for individual cardiac surgery patients, offering a considerable performance advantage over current population-based models. This data-driven algorithm, built on extensive electronic health records (EHR), is the first institution-specific model of its kind for pre-surgery cardiac patient risk assessment. It allows healthcare providers to determine the optimal treatment strategy for each patient.

The team theorized that models based on EHR data from their own institution, created via machine learning, could provide a useful solution. Using routinely gathered EHR data, they developed a robust machine learning framework to generate a risk prediction model for post-surgery mortality that is customized to both the patient and the hospital. This model incorporates vital data about Mount Sinai’s patient population, including demographic, socioeconomic, and health characteristics. This is in contrast to population-based models like STS, which rely on data from various health systems across the U.S. The effectiveness of this approach is further enhanced by an efficient open-source prediction algorithm called XGBoost, which assembles a group of decision trees by progressively focusing on harder-to-predict segments of training data.

The research team utilized XGBoost to model 6,392 cardiac surgeries conducted at The Mount Sinai Hospital from 2011 to 2016, encompassing heart valve procedures, coronary artery bypass grafts, aortic resections, replacements, or anastomoses, and reoperative cardiac surgeries, which significantly increase mortality risk. The team then compared the performance of their model to STS models for the same patient sets. The study found that the XGBoost model outshone STS risk scores for mortality in all frequently performed cardiac surgery categories for which STS scores were designed. The predictive performance of the XGBoost model across all types of surgeries was also high, indicating the potential of machine learning and EHR data for constructing effective institution-specific models.

“The standard-of-care risk models used today are limited by their applicability to specific types of surgeries, leaving out significant numbers of patients undergoing complex or combination procedures for which no models exist,” said senior author Ravi Iyengar, PhD, the Dorothy H. and Lewis Rosenstiel Professor of Pharmacological Sciences at the Icahn School of Medicine at Mount Sinai, and Director of the Mount Sinai Institute for Systems Biomedicine. “Our team rigorously combined electronic health record data and machine learning methods to demonstrate for the first time how individual institutions can build their own risk models for post-cardiac surgery mortality.”

Related Links:
Mount Sinai 

Gold Member
Solid State Kv/Dose Multi-Sensor
AGMS-DM+
Gold Member
STI Test
Vivalytic Sexually Transmitted Infection (STI) Array
Silver Member
Compact 14-Day Uninterrupted Holter ECG
NR-314P
New
Surgial Headlight
MedLED Chrome

Print article

Channels

Critical Care

view channel
Image: The permeable wearable electronics developed for long-term biosignal monitoring (Photo courtesy of CityUHK)

Super Permeable Wearable Electronics Enable Long-Term Biosignal Monitoring

Wearable electronics have become integral to enhancing health and fitness by offering continuous tracking of physiological signals over extended periods. This monitoring is crucial for understanding an... Read more

Surgical Techniques

view channel
Image: NTT and Olympus have begun the world\'s first joint demonstration experiment of a cloud endoscopy system (Photo courtesy of Olympus)

Cloud Endoscopy System Enables Real-Time Image Processing on the Cloud

Endoscopes, which are flexible tubes inserted into the body's natural openings for internal examination and biopsy collection, are becoming increasingly vital in medical diagnostics. Their minimal invasiveness... Read more

Patient Care

view channel
Image: The newly-launched solution can transform operating room scheduling and boost utilization rates (Photo courtesy of Fujitsu)

Surgical Capacity Optimization Solution Helps Hospitals Boost OR Utilization

An innovative solution has the capability to transform surgical capacity utilization by targeting the root cause of surgical block time inefficiencies. Fujitsu Limited’s (Tokyo, Japan) Surgical Capacity... Read more

Point of Care

view channel
Image: The PATHFAST hs-cTnI-II high-sensitivity troponin assay has been developed for the PATHFAST Biomarker Analyzer (Photo courtesy of Polymedco)

POC Myocardial Infarction Test Delivers Results in 17 Minutes

Chest pain is the second leading cause of emergency department (ED) visits by adults in the United States, generating over 7 million visits annually. In the event of a suspected heart attack, physicians... Read more
Copyright © 2000-2024 Globetech Media. All rights reserved.