Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Association of tricuspid regurgitation with clinical events and quality of life after surgery for severe ischemic mitral regurgitation.

JTCVS structural and endovascular·2026
Same author

Genome-Wide Association Study of Genetic Variants Associated with Lower Extremity Amputation Risk in Peripheral Artery Disease.

International journal of molecular sciences·2026
Same author

Cardiovascular signaling proteins as predictors of the cardiac effects of doxorubicin treatment in children with acute lymphoblastic leukemia.

Cardio-oncology (London, England)·2026
Same author

Effect of prophylactic antibiotics on days of antibiotic spectrum coverage in comatose post-cardiac arrest patients: a secondary analysis of PROTECT.

JAC-antimicrobial resistance·2026
Same author

Retinoic acid promotes expression of inflammatory factors in proliferative adult human heart cells.

American journal of physiology. Cell physiology·2025
Same author

Human myocardial-derived highly proliferative cells improve cardiac remodeling after myocardial infarction in mice.

The Journal of pharmacology and experimental therapeutics·2025
Same journal

Comparative Evaluation of Pretrained Large Language Models for Suicide Risk Prediction from Clinical Notes in U.S. Veterans.

medRxiv : the preprint server for health sciences·2026
Same journal

Nocturnal Respiratory Rate and Variability Predict Long-term Mortality in Stable Outpatients with Cardiovascular Disease.

medRxiv : the preprint server for health sciences·2026
Same journal

MOSAIC: Methylation-Oriented Site Analysis and Information Classifier for Robust Epigenomic Classification of Acute Leukemia in Clinical Cohorts with Variable Tumor Purity.

medRxiv : the preprint server for health sciences·2026
Same journal

Risk beliefs, intensive digital information and demand for a new preventative health product in public clinics: Evidence from an experiment in Zimbabwe.

medRxiv : the preprint server for health sciences·2026
Same journal

Development of an automated, imaging-based preoperative screening model for early identification of malnutrition in an abdominal surgery cohort.

medRxiv : the preprint server for health sciences·2026
Same journal

A Pilot Project Leveraging Large Language Models for Automated Screening and Variable Extraction in Observational Studies.

medRxiv : the preprint server for health sciences·2026
See all related articles

Related Experiment Video

Updated: May 23, 2025

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.0K

Development and Validation of Machine Learning Models for Adverse Events after Cardiac Surgery.

Qingchu Jin1,2, Saeed Amal1,3,2, Jaime B Rabb4

  • 1Roux Institute at Northeastern University, Portland ME, USA.

Medrxiv : the Preprint Server for Health Sciences
|March 10, 2025
PubMed
Summary
This summary is machine-generated.

A new machine learning model, Roux-MMC, accurately predicts adverse events after cardiac surgery, outperforming the existing STS risk model. This advanced model offers broader applicability to all cardiac surgery patients, improving patient care and outcomes.

More Related Videos

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.6K
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.2K

Related Experiment Videos

Last Updated: May 23, 2025

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.0K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.6K
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.2K

Area of Science:

  • Cardiovascular Surgery
  • Medical Informatics
  • Machine Learning in Healthcare

Background:

  • Early recognition of adverse events post-cardiac surgery is critical for effective treatment.
  • The current Society of Thoracic Surgery (STS) risk model has limitations in predicting adverse events and its applicability is restricted to less than 80% of cardiac surgeries.

Purpose of the Study:

  • To develop and validate machine learning (ML) models for predicting postoperative outcomes in cardiac surgery patients.
  • To compare the performance of the developed ML model against the established STS risk model.

Main Methods:

  • Machine learning models, termed the Roux-MMC model, were developed using a retrospective cohort (n=9,841) from the STS Adult Cardiac Surgery Database (ACSD) (2012-2021).
  • The Roux-MMC model was further validated on a prospective cohort (n=2,305) (2022-2024).
  • Model performance was evaluated by comparing the area under the receiver-operating curve (AUROC) for predicting eight key postoperative outcomes against the STS model.

Main Results:

  • The Roux-MMC model demonstrated superior performance compared to the STS model across all eight predicted postoperative outcomes in the prospective cohort.
  • Specific AUROC values for the Roux-MMC model in the prospective cohort ranged from 0.818 for short length of stay (SLOS) to 0.911 for prolonged ventilation.
  • The Roux-MMC model demonstrated wider applicability, covering all cardiac surgery patients, unlike the STS model which applied to only 65-77% of patients.

Conclusions:

  • The developed Roux-MMC machine learning model effectively predicts eight adverse postoperative outcomes in cardiac surgery patients.
  • The Roux-MMC model significantly outperforms the STS model and is applicable to all cardiac surgery patients.
  • Given its development on the STS ACSD, the Roux-MMC model has the potential for widespread implementation in hospitals nationwide.