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Predictive Value of the FDAPR Index for Ischemic Stroke in Patients with Coronary Heart Disease: A Machine Learning
Wei Lai1, Jinbo Zhao2, Juan Du1
1Department of Cardiology, Minda Hospital of Hubei Minzu University, Enshi, Hubei, People's Republic of China.
Insights
The Fibrinogen-D-dimer to Albumin-Platelet Ratio (FDAPR) is linked to a higher risk of ischemic stroke in coronary artery disease (CAD) patients. This ratio can aid in predicting stroke risk, especially when combined with other factors like age and potassium levels.
Area of Science:
- Cardiology
- Neurology
- Biomarker Research
Background:
- Ischemic stroke is a significant complication for patients with coronary artery disease (CAD).
- Early prediction of stroke risk in CAD patients remains a clinical challenge.
Purpose of the Study:
- To investigate the association between the Fibrinogen-D-dimer to Albumin-Platelet Ratio (FDAPR) and ischemic stroke risk in CAD patients.
- To develop a machine learning model for predicting stroke risk using FDAPR and other clinical variables.
Main Methods:
- Retrospective analysis of 1844 CAD patients undergoing coronary angiography.
- Utilized Boruta feature selection and multivariable logistic regression to identify stroke predictors.
- Developed a multilayer perceptron (MLP) model, evaluated with ROC curves, AUC, and SHAP analysis.
Main Results:
- Higher FDAPR levels were observed in patients who experienced ischemic stroke compared to those who did not.
- Age, FDAPR, serum potassium (K), and total bilirubin (TBIL) were identified as independent stroke risk factors.
- The MLP model achieved an AUC of 0.71; SHAP analysis confirmed FDAPR's positive association with stroke risk, with synergistic interactions with age, K, and TBIL.
Conclusions:
- The Fibrinogen-D-dimer to Albumin-Platelet Ratio (FDAPR) is significantly associated with increased ischemic stroke risk in patients with coronary artery disease.
- FDAPR serves as a potential biomarker for ischemic stroke prediction in CAD patients, particularly when analyzed alongside other clinical parameters.
Background:
Ischemic stroke is a common and severe complication in patients with coronary artery disease (CAD), yet its early prediction remains challenging. This study aimed to investigate the association between the Fibrinogen-D-dimer to Albumin-Platelet Ratio (FDAPR) and the risk of ischemic stroke in CAD patients, and to develop a multilayer perceptron (MLP)-based machine learning model for stroke risk prediction.
Methods:
Patients with confirmed CAD who underwent their first coronary angiography in two hospitals were retrospectively enrolled. The Boruta feature selection algorithm and multivariable logistic regression were used to identify independent predictors of ischemic stroke, which were subsequently incorporated into the MLP model. Model performance was evaluated using receiver operating characteristic (ROC) curves, area under the curve (AUC), and calibration plots. The Shapley Additive Explanations (SHAP) method was applied to quantify the contribution of each variable to model output, and two-dimensional contour plots were generated to explore the interaction effects between FDAPR and other key variables.
Results:
A total of 1844 CAD patients were included in the analysis. Compared with the non-stroke group, patients with ischemic stroke were older, had higher FDAPR levels, and lower serum potassium concentrations. Boruta and multivariable logistic regression identified age, FDAPR, serum potassium (K), and total bilirubin (TBIL) as independent risk factors for stroke. The MLP model achieved an AUC of 0.71 in both the training and test datasets. SHAP analysis demonstrated that elevated FDAPR levels were positively associated with stroke risk. Contour plots further revealed synergistic interactions between FDAPR and age, potassium, and TBIL.
Conclusion:
FDAPR is significantly associated with ischemic stroke risk among patients with coronary artery disease.
