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Published on: October 23, 2020
Development and validation of risk prediction model for recurrent cardiovascular events among Chinese: the
Yekai Zhou1, Celia Jiaxi Lin2, Qiuyan Yu3
1Department of Computer Science, The University of Hong Kong, Rm 301 Chow Yei Ching Building, Pokfulam Road, Pokfulam, Hong Kong Special Administrative Region, 999077, China.
Insights
A new machine learning model, Personalized Cardiovascular Disease risk Assessment for Chinese (P-CARDIAC), was developed to predict recurrent cardiovascular disease events in Chinese patients. P-CARDIAC demonstrated superior performance compared to existing risk scores.
Area of Science:
- Cardiology
- Medical Informatics
- Machine Learning
Background:
- Cardiovascular disease (CVD) is a major global health concern, particularly in developing nations.
- Accurate prediction of recurrent CVD events is crucial for effective patient management and healthcare resource allocation.
Purpose of the Study:
- To develop and validate a novel machine learning-based risk prediction model for recurrent cardiovascular events in Chinese patients with established CVD.
- To assess the performance of the developed model against existing risk stratification tools.
Main Methods:
- Utilized three cohorts of Chinese patients with established CVD, including 48,799 in the derivation and 119,672 and 140,533 in validation cohorts.
- Employed multivariate imputation and XGBoost for model development, incorporating 125 risk variables and CVD-related drug interactions.
- Validated the model against Thrombolysis in Myocardial Infarction Risk Score for Secondary Prevention (TRS-2°P) and Secondary Manifestations of ARTerial disease (SMART2) using 1000 bootstrap replicates.
Main Results:
- The P-CARDIAC model achieved a C statistic of 0.69 in the derivation cohort, indicating satisfying discrimination and calibration.
- Internal validation confirmed good performance with a C statistic over 0.6.
- P-CARDIAC outperformed both TRS-2°P and SMART2 in predicting recurrent CVD events.
Conclusions:
- The P-CARDIAC model effectively identifies unique risk patterns in Chinese patients with established CVD.
- This novel tool has the potential for broad application in preventing recurrent CVD events and mitigating associated healthcare burdens.
Aims:
Cardiovascular disease (CVD) is a leading cause of mortality, especially in developing countries. This study aimed to develop and validate a CVD risk prediction model, Personalized CARdiovascular DIsease risk Assessment for Chinese (P-CARDIAC), for recurrent cardiovascular events using machine learning technique.
Methods And Results:
Three cohorts of Chinese patients with established CVD were included if they had used any of the public healthcare services provided by the Hong Kong Hospital Authority (HA) since 2004 and categorized by their geographical locations. The 10-year CVD outcome was a composite of diagnostic or procedure codes with specific International Classification of Diseases, Ninth Revision, Clinical Modification. Multivariate imputation with chained equations and XGBoost were applied for the model development. The comparison with Thrombolysis in Myocardial Infarction Risk Score for Secondary Prevention (TRS-2°P) and Secondary Manifestations of ARTerial disease (SMART2) used the validation cohorts with 1000 bootstrap replicates. A total of 48 799, 119 672 and 140 533 patients were included in the derivation and validation cohorts, respectively. A list of 125 risk variables were used to make predictions on CVD risk, of which 8 classes of CVD-related drugs were considered interactive covariates. Model performance in the derivation cohort showed satisfying discrimination and calibration with a C statistic of 0.69. Internal validation showed good discrimination and calibration performance with C statistic over 0.6. The P-CARDIAC also showed better performance than TRS-2°P and SMART2.
Conclusion:
Compared with other risk scores, the P-CARDIAC enables to identify unique patterns of Chinese patients with established CVD. We anticipate that the P-CARDIAC can be applied in various settings to prevent recurrent CVD events, thus reducing the related healthcare burden.
