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The incremental value of angiographic features for predicting recurrent cardiovascular events: Insights from the Duke
Michael G Nanna1, Eric D Peterson2, Karen Chiswell3
1Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC, USA; Duke University Medical Center, Department of Medicine, Durham, NC, USA.
Insights
Identifying patients at high risk for recurrent cardiovascular events is difficult. Angiographic features offer minimal added value for predicting cardiovascular disease events beyond clinical factors.
Area of Science:
- Cardiology
- Medical Imaging
- Public Health
Background:
- Accurate risk stratification for recurrent cardiovascular disease (CVD) events is crucial.
- Clinical characteristics alone may not fully identify high-risk patient subgroups.
- Coronary angiography is a key diagnostic tool in managing coronary artery disease (CAD).
Purpose of the Study:
- To evaluate the incremental value of coronary angiographic features in predicting secondary cardiovascular events.
- To compare the predictive performance of clinical factors versus clinical factors plus angiographic data.
Main Methods:
- Retrospective analysis of 3366 patients with significant coronary artery disease (CAD) from the Duke Databank for Cardiovascular Disease.
- Development of multivariable models using clinical variables and subsequently adding angiographic data.
- Comparison of model discrimination for predicting major adverse cardiovascular events (MACE) over 3 years.
Main Results:
- 19.2% of patients experienced a MACE within 3 years.
- A clinical model predicted events with a c-statistic of 0.716.
- Adding angiographic features yielded a minimal improvement in discrimination (c-statistic=0.724, delta=0.008).
Conclusions:
- Coronary angiographic features provide limited incremental predictive value for secondary CVD risk beyond established clinical factors.
- The added benefit of incorporating detailed angiographic information into risk prediction models is marginal.
- Clinical assessment remains the primary driver for risk stratification in CAD patients.
Background And Aims:
Identifying patient subgroups with cardiovascular disease (CVD) at highest risk for recurrent events remains challenging. Angiographic features may provide incremental value in risk prediction beyond clinical characteristics.
Methods:
We included all cardiac catheterization patients from the Duke Databank for Cardiovascular Disease with significant coronary artery disease (CAD; 07/01/2007-12/31/2012) and an outpatient follow-up visit with a primary care physician or cardiologist in the same health system within 3 months post-catheterization. Follow-up occurred for 3 years for the primary major adverse cardiovascular event endpoint (time to all-cause death, myocardial infarction [MI], or stroke). A multivariable model to predict recurrent events was developed based on clinical variables only, then adding angiographic variables from the catheterization. Next, we compared discrimination of clinical vs. clinical plus angiographic risk prediction models.
Results:
Among 3366 patients with angiographically-defined CAD, 633 (19.2%) experienced cardiovascular events (death, MI, or stroke) within 3 years. A multivariable model including 18 baseline clinical factors and initial revascularization had modest ability to predict future atherosclerotic cardiovascular disease events (c-statistic = 0.716). Among angiographic predictors, number of diseased vessels, left main stenosis, left anterior descending stenosis, and the Duke CAD Index had the highest value for secondary risk prediction; however, the clinical plus angiographic model only slightly improved discrimination (c-statistic = 0.724; delta 0.008). The net benefit for angiographic features was also small, with a relative integrated discrimination improvement of 0.05 (95% confidence interval: 0.03-0.08).
Conclusions:
The inclusion of coronary angiographic features added little incremental value in secondary risk prediction beyond clinical characteristics.
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