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A clinical model to identify patients with high-risk coronary artery disease
Yelin Yang1, Li Chen1, Yeung Yam1
1Department of Medicine (Cardiology), University of Ottawa Heart Institute, Ottawa, Canada.
Insights
A new clinical model identifies patients with high-risk coronary artery disease (CAD) using nine variables. This scoring system aids in determining optimal treatment strategies for patients with suspected CAD.
Area of Science:
- Cardiology
- Medical Diagnostics
- Clinical Risk Stratification
Background:
- Current clinical models inadequately identify patients with high-risk coronary artery anatomy.
- Accurate pre-test probability estimation for obstructive coronary artery disease (CAD) remains a challenge.
Purpose of the Study:
- To develop and validate a clinical model for identifying patients with and without high-risk coronary artery disease (CAD).
- To improve the accuracy of pre-test probability assessment for high-risk coronary anatomy.
Main Methods:
- Retrospective analysis of a multinational coronary computed tomographic angiography (CTA) cohort (n=27,125).
- Definition of high-risk anatomy: left main stenosis ≥50%, 3-vessel disease with stenosis ≥70%, or 2-vessel disease involving proximal LAD.
- Model derivation in 24,251 patients and validation in 7,333 non-overlapping patients.
Main Results:
- A 9-variable risk score (age, sex, diabetes, hypertension, smoking, hyperlipidemia, family history, peripheral vascular disease, chest pain) was developed.
- Model demonstrated robust performance: AUC 0.76 (derivation) and 0.71 (validation).
- High-risk category (≥18 points) showed high specificity (99.3%) and identified 16.7% prevalence of high-risk CAD in validation.
Conclusions:
- A novel clinical scoring system effectively identifies patients with high and low pre-test probability of high-risk CAD.
- This tool can guide decisions for medical therapy versus invasive strategies.
- Improved risk stratification may lead to more personalized and effective patient management.
Objectives:
This study sought to develop a clinical model that identifies patients with and without high-risk coronary artery disease (CAD).
Background:
Although current clinical models help to estimate a patient's pre-test probability of obstructive CAD, they do not accurately identify those patients with and without high-risk coronary anatomy.
Methods:
Retrospective analysis of a prospectively collected multinational coronary computed tomographic angiography (CTA) cohort was conducted. High-risk anatomy was defined as left main diameter stenosis ≥50%, 3-vessel disease with diameter stenosis ≥70%, or 2-vessel disease involving the proximal left anterior descending artery. Using a cohort of 27,125, patients with a history of CAD, cardiac transplantation, and congenital heart disease were excluded. The model was derived from 24,251 consecutive patients in the derivation cohort and an additional 7,333 nonoverlapping patients in the validation cohort.
Results:
The risk score consisted of 9 variables: age, sex, diabetes, hypertension, current smoking, hyperlipidemia, family history of CAD, history of peripheral vascular disease, and chest pain symptoms. Patients were divided into 3 risk categories: low (≤7 points), intermediate (8 to 17 points) and high (≥18 points). The model was statistically robust with area under the curve of 0.76 (95% confidence interval [CI]: 0.75 to 0.78) in the derivation cohort and 0.71 (95% CI: 0.69 to 0.74) in the validation cohort. Patients who scored ≤7 points had a low negative likelihood ratio (<0.1), whereas patients who scored ≥18 points had a high specificity of 99.3% and a positive likelihood ratio (8.48). In the validation group, the prevalence of high-risk CAD was 1% in patients with ≤7 points and 16.7% in those with ≥18 points.
Conclusions:
We propose a scoring system, based on clinical variables, that can be used to identify patients at high and low pre-test probability of having high-risk CAD. Identification of these populations may detect those who may benefit from a trial of medical therapy and those who may benefit most from an invasive strategy.
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