Related Experiment Video
Updated: Jan 12, 2026
Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
Published on: May 15, 2020
Development and Validation of the CR-DECIDE Models to Predict Major Adverse Cardiovascular Events and Health Status
Ricky D Turgeon1,2, May K Lee2, Rubee Dev3
1Faculty of Pharmaceutical Sciences, University of British Columbia, Vancouver, British Columbia, Canada.
Insights
New clinical prediction models can help personalize treatment for stable coronary artery disease (CAD) by forecasting major adverse cardiovascular events (MACEs) and health status, aiding shared decision-making.
Area of Science:
- Cardiology
- Clinical Prediction Modeling
- Public Health
Background:
- Stable coronary artery disease (CAD) management guidelines advocate for individualized patient care.
- Shared decision-making is crucial for tailoring treatments to patient needs.
- Existing tools may not fully support personalized care in stable CAD.
Purpose of the Study:
- To develop and validate clinical prediction models for major adverse cardiovascular events (MACEs) and health status in stable CAD patients.
- To support individualized treatment decisions and shared decision-making processes.
- To provide clinicians with tools for better patient risk stratification and outcome prediction.
Main Methods:
- Development and internal validation of models using large Canadian CAD registries (British Columbia, Alberta).
- External validation in participants from the ISCHEMIA trial.
- Outcomes assessed: MACE (death, myocardial infarction, stroke) within 3 years; angina-free status and physical functioning at 1 year.
Main Results:
- Models demonstrated moderate predictive ability for MACEs (C-statistic 0.68) and health status (C-statistics 0.67-0.78).
- Significant improvements in angina-free status (41% to 64.5%) and physical functioning (21% to 72%) were observed at 1 year.
- External validation showed modest reduction in discrimination but retained positive net benefit for the MACE model.
Conclusions:
- The CR-DECIDE models show moderate accuracy in predicting MACEs and health status for stable CAD patients.
- These models warrant further evaluation for their clinical utility at the point of care.
- The findings support the potential for improved individualized care and shared decision-making in stable CAD management.
Background:
Guidelines emphasize individualized care in the management of stable coronary artery disease (CAD). We aimed to develop and validate clinical prediction models for major adverse cardiovascular events (MACEs) and health status among patients with stable CAD to support individualized, shared decision-making.
Methods:
For model development and internal validation, we used registries of outpatients with obstructive CAD on coronary angiography in British Columbia (2004-2015) and Alberta (2004-2020). Models were externally validated in ISCHEMIA trial participants with obstructive CAD on coronary computed tomography angiography. Outcomes included MACE (death, myocardial infarction, or stroke) within 3 years, angina-free status, and good-to-excellent physical functioning at 1 year, based on the Seattle Angina Questionnaire.
Results:
Median age was of study patients was 66-67 years, and 77% were male in both the MACE (n = 34,990) and health status (n = 13,312) model development cohorts. MACEs occurred in 9% (2026 patients) at 3 years. A 14-variable model had a C statistic of 0.68, calibration slope of 0.98, and positive net benefit in decision-curve analysis. At baseline, 41% were angina-free and 21% had good-to-excellent physical functioning, which increased to 64.5% and 72% at 1 year, respectively. C statistics for the angina-free and physical functioning models were 0.67 and 0.78, respectively, and calibration slopes were 0.98-0.99. In external validation, discrimination was modestly reduced and all models slightly underpredicted their respective outcomes, yet the MACE model retained positive net benefit.
Conclusions:
The CR-DECIDE models had moderate ability to predict MACEs and health status in patients with stable CAD and warrant further assessment of their impact at the point of care.
Related Concept Videos
01:30Acute Coronary Syndrome III: Diagnostic Studies
01:25Imaging Studies for Cardiovascular System VI: Calcium -Scoring CT
01:30Coronary Artery Disease I: Introduction
01:27Coronary Artery Disease V: Interprofessional Care
01:26Coronary Artery Disease IV: Preventive Measures
01:26Coronary Artery Disease II: Pathophysiology