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Echocardiography-based machine learning model for atrial fibrillation risk assessment in hypertension
Zheng Tang1, Xinyue Li1, Xin Yi1
1Department of Cardiovascular Medicine, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China; Chongqing Cardiac Arrhythmias Therapeutic Service Center, Chongqing, China; Chongqing Key Lab of Arrhythmia, Chongqing, China; Cardiovascular Neuromodulation Research and Treatment Center, Chongqing, China.
Insights
A new machine learning model integrating echocardiographic and demographic data accurately predicts atrial fibrillation (AF) risk in hypertensive patients. This advanced tool surpasses traditional scores, improving cardiovascular complication prevention.
Area of Science:
- Cardiology and Artificial Intelligence
- Clinical Risk Prediction Modeling
- Hypertension and Cardiovascular Disease Research
Background:
- Early identification of atrial fibrillation (AF) is crucial for preventing cardiovascular complications.
- Existing risk scores like CHA₂DS₂-VASc have limitations in assessing cardiovascular remodeling.
- Hypertension, cardiovascular remodeling, and AF are interconnected, suggesting integrated parameters may enhance risk assessment.
Purpose of the Study:
- To develop and validate a machine learning (ML) model for AF risk assessment in hypertensive individuals.
- To integrate echocardiographic and demographic parameters for improved predictive accuracy.
- To compare the ML model's performance against established risk scores.
Main Methods:
- A large cohort of 48,571 hypertensive patients from eight hospitals was analyzed.
- An XGBoost model was developed using echocardiographic and demographic data, optimized via 10-fold cross-validation.
- Model performance was evaluated using Area Under the Receiver Operating Characteristic Curve (AUC), calibration, and clinical utility across internal and temporal test sets.
Main Results:
- The XGBoost model demonstrated high predictive accuracy with AUCs of 0.875 (internal/early temporal) and 0.886 (late temporal).
- The model significantly outperformed C₂HEST and CHA₂DS₂-VASc scores.
- The developed model maintained excellent calibration and clinical utility.
Conclusions:
- An XGBoost-based ML model integrating echocardiographic and demographic parameters provides a superior tool for AF risk stratification in hypertension.
- This model offers a reliable and accurate method for identifying high-risk patients, potentially improving clinical management and outcomes.
- The findings support the integration of advanced analytics and comprehensive patient data for enhanced cardiovascular risk assessment.
Background:
Early identification of atrial fibrillation (AF) allows for timely interventions to reduce cardiovascular complications. Risk scores including C2HEST and CHA2DS2-VASc have limitations in capturing cardiovascular remodeling. Given the association among hypertension, cardiovascular remodeling, and AF, integrating echocardiographic and demographic parameters may improve AF risk assessment in hypertension.
Objective:
We aimed to develop and validate a machine learning model integrating echocardiographic and demographic parameters to assess AF risk in hypertension.
Methods:
This study included 48,571 hypertensive patients from 8 hospitals. AF was diagnosed based on electrocardiograms or medical records. Patients from The Second Affiliated Hospital of Chongqing Medical University (n = 40,811) were randomly divided into training and internal test sets (7:3), whereas patients from other hospitals (n = 7760) were split into early (2014-2019; n = 3648) and late temporal test sets (2020-2021; n = 4112). 8 machine learning models integrating echocardiographic and demographic parameters were developed and optimized via 10-fold cross-validation in the training set. The best-performing model was selected in the internal test set and further evaluated across all test sets using area under the receiver operating characteristic curve, calibration, and clinical utility.
Results:
An extreme gradient boosting-based model achieved an area under the receiver operating characteristic curve of 0.875 in both internal test (95% confidence interval [CI], 0.861-0.888) and early temporal test sets (95% CI, 0.850-0.899) and 0.886 (95% CI, 0.865-0.904) in the late temporal test set, significantly outperforming C2HEST and CHA2DS2-VASc scores while maintaining calibration and clinical utility.
Conclusion:
An extreme gradient boosting-based model integrating echocardiographic and demographic parameters outperformed existing scores, offering a reliable tool for AF risk assessment in hypertension.
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