Abstract
To develop a risk stratification model based on complete blood count (CBC) components in patients with acute coronary syndrome (ACS) using a classification and regression tree (CART) method. CBC variables and the Global Registry of Acute Coronary Events (GRACE) scores were determined in 2,693 patients with ACS. The CART analysis was performed to classify patients into different homogeneous risk groups and to determine predictors for major adverse cardiovascular events (MACEs) at 1-year follow-up. The CART algorithm identified the white blood cell count, hemoglobin, and mean platelet volume levels as the best combination to predict MACE risk. Patients were stratified into three categories with MACE rates ranging from 3.0% to 29.8%. Kaplan-Meier analysis demonstrated MACE risk increased with the ascending order of the CART risk categories. Multivariate Cox regression analysis showed that the CART risk categories independently predicted MACE risk. The predictive accuracy of the CART risk categories was tested by measuring discrimination and graphically assessing the calibration. Furthermore, the combined use of the CART risk categories and GRACE scores yielded a more accurate predictive value for MACEs. Patients with ACS can be readily stratified into distinct prognostic categories using the CART risk stratification tool on the basis of CBC components.
Highlights
Acute coronary syndrome (ACS) includes a heterogeneous spectrum of diseases in terms of pathophysiological mechanisms, clinical presentation, and prognosis[1,2]
Of the 2,693 patients with acute coronary syndrome (ACS), 240 (8.9%, 95% CI 7.8-10.0%) had experienced major adverse cardiovascular events (MACEs) at 1-year follow-up
Based on the number of positive biomarkers in the Classification and regression tree (CART) analysis, patients were stratified into three risk groups: 1) low-risk with 0 positive biomarker (WBC count
Summary
Acute coronary syndrome (ACS) includes a heterogeneous spectrum of diseases in terms of pathophysiological mechanisms, clinical presentation, and prognosis[1,2]. Recent studies reported that combining hematological indices with the GRACE score facilitated better prediction of future cardiovascular events in patients with ACS as compared to the use of the GRACE score alone[5,6,7]. Each individual CBC component might provide modest predictive ability, a risk stratification model, derived from combining variables in the CBC, could have synergistic advantages. The CBC score, based on beta coefficients from a logistic regression model, was a powerful predictor of poor outcomes in patients with suspected cardiovascular disease, suggesting that combined use of CBC components can provide valuable additional risk information to clinicians. CART analysis was performed to identify key CBC components and develop a risk stratification model. The incremental prognostic value of combining the CART risk model with the GRACE score was determined
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