Risk stratification based on components of the complete blood count in patients with acute coronary syndrome: A
Xiaowei Niu1, Guoyong Liu2, Lichao Huo1
1The First School of Clinical Medicine, Lanzhou University, Lanzhou, Gansu, China.
Insights
A new risk model using complete blood count (CBC) components accurately stratifies patients with acute coronary syndrome (ACS). This tool predicts major adverse cardiovascular events (MACEs), aiding clinical decision-making.
Area of Science:
- Cardiology
- Biomedical Informatics
- Hematology
Background:
- Acute coronary syndrome (ACS) poses significant cardiovascular risk.
- Accurate risk stratification is crucial for managing ACS patients.
- Existing risk models may benefit from incorporating readily available biomarkers.
Purpose of the Study:
- To develop and validate a risk stratification model for ACS patients using complete blood count (CBC) parameters.
- To identify key CBC components predictive of major adverse cardiovascular events (MACEs).
- To compare the model's performance with the Global Registry of Acute Coronary Events (GRACE) score.
Main Methods:
- Classification and Regression Tree (CART) analysis applied to CBC data from 2,693 ACS patients.
- Identification of predictors for 1-year MACEs.
- Kaplan-Meier analysis and multivariate Cox regression for risk assessment.
- Evaluation of model discrimination and calibration.
Main Results:
- CART analysis identified white blood cell count, hemoglobin, and mean platelet volume as key predictors of MACEs.
- A three-category risk stratification model was developed with MACE rates from 3.0% to 29.8%.
- The CART-derived risk categories independently predicted MACE risk and improved prediction when combined with GRACE scores.
Conclusions:
- CBC components can be effectively used to stratify ACS patients into distinct prognostic categories.
- The CART-based risk stratification tool offers a practical approach for predicting MACEs in ACS.
- This model provides valuable prognostic information, complementing existing risk scores like GRACE.
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.
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