120017
论文已发表
注册即可获取德孚的最新动态
IF 收录期刊
- 3.6 Breast Cancer (Dove Med Press)
- 4.3 Clin Epidemiol
- 2.6 Cancer Manag Res
- 3.2 Infect Drug Resist
- 4.1 Clin Interv Aging
- 6.1 Drug Des Dev Ther
- 4.1 Int J Chronic Obstr
- 8.7 Int J Nanomed
- 2.5 Int J Women's Health
- 3.2 Neuropsych Dis Treat
- 2.4 OncoTargets Ther
- 2.6 Patient Prefer Adher
- 2.6 Ther Clin Risk Manag
- 3.1 J Pain Res
- 3.5 Diabet Metab Synd Ob
- 4.5 Psychol Res Behav Ma
- 3.4 Nat Sci Sleep
- 2.4 Pharmgenomics Pers Med
- 2.6 Risk Manag Healthc Policy
- 4.6 J Inflamm Res
- 2.3 Int J Gen Med
- 3.9 J Hepatocell Carcinoma
- 3.3 J Asthma Allergy
- 2.5 Clin Cosmet Investig Dermatol
- 3.0 J Multidiscip Healthc
已发表论文
预测价值的 FDAPR指数用于缺血性卒中患者中的冠心病：一项机器学习研究基于一项多层感知机
Authors Lai W, Zhao J, Du J, Zhang C
Received 29 October 2025
Accepted for publication 28 January 2026
Published 5 February 2026 Volume 2026:19 575324
DOI https://doi.org/10.2147/IJGM.S575324
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Dr Redoy Ranjan
Wei Lai,1 Jinbo Zhao,2 Juan Du,1 Changjiang Zhang1
1Department of Cardiology, Minda Hospital of Hubei Minzu University, Enshi, Hubei, People’s Republic of China; 2Cardiovascular Disease Center, Central Hospital of Tujia and Miao Autonomous Prefecture, Hubei University of Medicine, Enshi, Hubei, People’s Republic of China
Correspondence: Juan Du, Email 18671573125@163.com Changjiang Zhang, Email zcj2008@163.com
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.
Keywords: FDAPR index, ischemic stroke, coronary artery disease, multilayer perceptron, prediction model
1Department of Cardiology, Minda Hospital of Hubei Minzu University, Enshi, Hubei, People’s Republic of China; 2Cardiovascular Disease Center, Central Hospital of Tujia and Miao Autonomous Prefecture, Hubei University of Medicine, Enshi, Hubei, People’s Republic of China
Correspondence: Juan Du, Email 18671573125@163.com Changjiang Zhang, Email zcj2008@163.com
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.
Keywords: FDAPR index, ischemic stroke, coronary artery disease, multilayer perceptron, prediction model