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Published on: January 28, 2020
Machine learning-selected inflammation biomarkers for stable coronary artery disease with intermediate coronary
Qiong Xu1, Shoupeng Duan2, Shuo Liu3
1Department of Cardiology, The First Affiliated Hospital of Bengbu Medical University, Bengbu, Anhui, China.
Insights
Machine learning identified key factors from electronic health records to predict long-term outcomes in stable coronary artery disease (SCAD) patients. This tool aids in identifying high-risk individuals with intermediate coronary lesions.
Area of Science:
- Cardiology
- Machine Learning in Healthcare
- Biomarker Discovery
Background:
- Stable coronary artery disease (SCAD) can unpredictably progress to unstable states.
- Identifying patients at risk for adverse events is crucial for effective management.
- Intermediate coronary lesions pose a challenge in risk stratification.
Purpose of the Study:
- To develop a predictive model for long-term prognosis in SCAD patients using electronic health record (EHR) data.
- To identify key predictive features for major adverse cardiovascular events (MACEs).
- To validate the model's performance in an external cohort.
Main Methods:
- Utilized LASSO regression and Boruta algorithm to identify predictive features from EHR data.
- Developed a predictive model using multivariate Cox regression analysis.
- Included training (n=403) and external validation (n=247) cohorts from January 2018 to December 2020.
Main Results:
- A four-variable model (platelet-to-lymphocyte ratio, diabetes mellitus, lipoprotein(a), mean platelet width) predicted MACEs with AUCs ranging from 0.658 to 0.743.
- The model demonstrated moderate discriminative ability in both development and validation cohorts.
- Calibration and clinical utility were confirmed via decision curve analysis.
Conclusions:
- A validated nomogram incorporating inflammation biomarkers and EHR data can identify high-risk SCAD patients.
- The model shows moderate discriminative power for predicting poor outcomes in patients with intermediate coronary stenosis.
- This approach aids in personalized risk assessment for SCAD management.
Background:
Stable coronary artery disease (SCAD) generally exhibits prolonged periods of stability. However, this condition can unpredictably progress into an unstable state, representing a complex pathological process involving multiple contributing factors. Thus, we aimed to utilize machine-learning techniques to identify predictive features from electronic health record (EHR) data for forecasting the long-term prognosis of patients with SCAD and intermediate coronary lesions.
Methods:
Patients were divided into a training cohort (n = 403) and an external validation cohort (n = 247) according to their hospital of origin during the period from January 2018 to December 2020. Predictive features were determined using LASSO regression analysis and boruta algorithm, followed by multivariate Cox regression analysis for model construction.
Results:
The developed predictive model comprised four clinical variables: platelet-to-lymphocyte ratio, diabetes mellitus, lipoprotein(a), and mean platelet width. The area under the curve for predicting major adverse cardiovascular events (MACEs) within 2-, 3- and 4-year in the development cohort was 0.692 (95%CI:0.59-0.793), 0.709 (95%CI:0.625-0.792) and 0.743 (95%CI:0.672-0.813), respectively, while that in the external validation cohort was 0.658 (95%CI 0.542-0.773), 0.681 (95%CI:0.579-0.782) and 0.723 (95%CI: 0.635-0.811), respectively. Additionally, the developed predictive model was calibrated by analyzing the correlation between expected and observed MACEs in the development and external validation cohorts. Lastly, the clinical value of the developed predictive model was confirmed via decision curve analysis.
Conclusion:
Our validated nomogram was based on inflammation biomarkers and EHR data, demonstrating moderate discriminative ability to detect individuals at high risk of poor outcome among patients with SCAD and angiographically intermediate coronary stenosis.
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