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Risk prediction score for clinical outcome in atrial fibrillation and stable coronary artery disease
Masanobu Ishii1, Koichi Kaikita2, Satoshi Yasuda3
1Department of Cardiovascular Medicine, Graduate School of Medical Sciences, Kumamoto University, Kumamoto, Japan.
Insights
A new risk score accurately predicts adverse events in patients with atrial fibrillation (AF) and stable coronary artery disease (CAD). This tool aids in managing thrombosis and bleeding risks for better patient outcomes.
Area of Science:
- Cardiology
- Medical Informatics
- Clinical Prediction Models
Background:
- Patients with atrial fibrillation (AF) and stable coronary artery disease (CAD) face high thrombosis risk.
- Antithrombotic therapy increases bleeding risk, necessitating careful risk stratification.
- Current risk assessment tools may not fully capture the complexity of adverse events in this population.
Purpose of the Study:
- To develop and validate a machine-learning-based risk score for predicting net adverse clinical events (NACE) in patients with AF and stable CAD.
- To identify key predictors of NACE using advanced algorithms.
- To create an accessible integer-based score for clinical application.
Main Methods:
- Utilized data from 2215 patients from the "Atrial Fibrillation and Ischaemic Events With Rivaroxaban in Patients With Stable Coronary Artery Disease" trial.
- Employed random survival forest (RSF) and Cox regression models for risk score development.
- Validated the model using a separate cohort and assessed discrimination and calibration.
Main Results:
- An integer-based risk score was developed using variables like age, sex, BMI, blood pressure, and medical history.
- The risk score effectively classified patients into low, intermediate, and high NACE risk groups.
- The model demonstrated acceptable discrimination (AUC 0.70/0.66) and calibration in validation cohorts.
Conclusions:
- The developed risk score is a valuable tool for predicting NACE in patients with AF and stable CAD.
- This model can assist clinicians in optimizing antithrombotic therapy and managing patient risk.
- Further implementation can improve clinical decision-making and patient care.
Objective:
Antithrombotic therapy is essential for patients with atrial fibrillation (AF) and stable coronary artery disease (CAD) because of the high risk of thrombosis, whereas a combination of antiplatelets and anticoagulants is associated with a high risk of bleeding. We sought to develop and validate a machine-learning-based model to predict future adverse events.
Methods:
Data from 2215 patients with AF and stable CAD enrolled in the Atrial Fibrillation and Ischaemic Events With Rivaroxaban in Patients With Stable Coronary Artery Disease trial were randomly assigned to the development and validation cohorts. Using the random survival forest (RSF) and Cox regression models, risk scores were developed for net adverse clinical events (NACE) defined as all-cause death, myocardial infarction, stroke or major bleeding.
Results:
Using variables selected by the Boruta algorithm, RSF and Cox models demonstrated acceptable discrimination and calibration in the validation cohort. Using the variables weighted by HR (age, sex, body mass index, systolic blood pressure, alcohol consumption, creatinine clearance, heart failure, diabetes, antiplatelet use and AF type), an integer-based risk score for NACE was developed and classified patients into three risk groups: low (0-4 points), intermediate (5-8) and high (≥9). In both cohorts, the integer-based risk score performed well, with acceptable discrimination (area under the curve 0.70 and 0.66, respectively) and calibration (p>0.40 for both). Decision curve analysis showed the superior net benefits of the risk score.
Conclusions:
This risk score can predict the risk of NACE in patients with AF and stable CAD.
Trial Registration Numbers:
UMIN000016612, NCT02642419.
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