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Published on: August 9, 2024
Predictive Model for High-Risk Coronary Artery Disease
James J Jang1, Manjushri Bhapkar2, Adrian Coles2
1San Jose Medical Center, Kaiser Permanente, CA (J.J.J.).
Insights
Identifying high-risk coronary artery disease (CAD) is crucial. New predictive models using clinical variables effectively identify patients with high-risk CAD, outperforming traditional risk assessments.
Area of Science:
- Cardiology
- Medical Imaging
- Predictive Analytics
Background:
- Identifying patients with high-risk coronary artery disease (CAD) presents a clinical challenge.
- High-risk CAD is defined by significant stenosis in major coronary arteries.
Purpose of the Study:
- To develop and validate predictive models for high-risk CAD using pretest clinical variables.
- To compare the performance of these new models against traditional risk assessment tools.
Main Methods:
- Utilized the PROMISE cohort (n=4589) randomized to coronary computed tomographic angiography.
- Developed two predictive models for high-risk CAD (≥50% or ≥70% stenosis) using stepwise logistic regression.
- Assessed model discrimination and calibration, comparing them to the Pooled Cohort Equation and Diamond-Forrester risk scores.
Main Results:
- High-risk CAD was present in 2.4% to 6.6% of patients.
- Both models demonstrated good discrimination (bias-corrected C statistic=0.73).
- Predictive variables included family history, age, sex, kidney function, diabetes, blood pressure, and angina. Models outperformed traditional assessments.
Conclusions:
- A significant proportion of stable, symptomatic patients have high-risk CAD.
- Simple combinations of pretest clinical variables offer improved prediction of high-risk CAD.
- These models provide a better risk stratification tool than existing methods.
Background:
Patients with high-risk coronary artery disease (CAD) may be difficult to identify.
Methods:
Using the PROMISE (Prospective Multicenter Imaging Study for Evaluation of Chest Pain) cohort randomized to coronary computed tomographic angiography (n=4589), 2 predictive models were developed for high-risk CAD, defined as left main stenosis (≥50% stenosis) or either (1) ≥50% stenosis [50] or (2) ≥70% stenosis [70] of 3 vessels or 2-vessel CAD involving the proximal left anterior descending artery. Pretest predictors were examined using stepwise logistic regression and assessed for discrimination and calibration.
Results:
High-risk CAD was identified in 6.6% [50] and 2.4% [70] of patients. Models developed to predict high-risk CAD discriminated well: [50], bias-corrected C statistic=0.73 (95% CI, 0.71-0.76); [70], bias-corrected C statistic=0.73 (95% CI, 0.68-0.77). Variables predictive of CAD in both models included family history of premature CAD, age, male sex, lower glomerular filtration rate, diabetes mellitus, elevated systolic blood pressure, and angina. Additionally, smoking history was predictive of [50] CAD and sedentary lifestyle of [70] CAD. Both models characterized high-risk CAD better than the Pooled Cohort Equation (area under the curve=0.70 and 0.71 for [50] and [70], respectively) and Diamond-Forrester risk scores (area under the curve=0.68 and 0.71, respectively). Both [50] and [70] CAD was associated with more frequent invasive interventions and adverse events than non-high-risk CAD (all P<0.0001).
Conclusions:
In contemporary practice, 2.4% to 6.6% of stable, symptomatic patients requiring noninvasive testing have high-risk CAD. A simple combination of pretest clinical variables improves prediction of high-risk CAD over traditional risk assessments.
Clinical Trial Registration:
URL: https://www.clinicaltrials.gov . Unique identifier: NCT01174550.
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