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Prediction of post-PCI angina risk using machine learning-based magnetocardiography model
WenLong Wang1, LiNa Wang1, FaMing Ding1
1Department of Cardiology, Binzhou Medical University Hospital, Binzhou, Shandong, China.
Background:
Recurrent angina after percutaneous coronary intervention (PCI) impairs quality of life and poses a clinical challenge. Magnetocardiography (MCG), as a noninvasive tool, its role in predicting symptomatic outcomes post-PCI remains undefined.
Objective:
This study sought to develop and validate a machine learning-based MCG model, both alone and combined with clinical biomarkers, to predict angina after PCI.
Methods:
110 patients with coronary artery disease undergoing successful PCI were included. MCG was performed pre-PCI and within 72 h post-PCI. MCG was performed to derive quantitative scores for all patients both prior to and following percutaneous coronary intervention (PCI). Angina status was assessed within 3 months using the Seattle Angina Questionnaire-Angina Stability (SAQ-AS) and -Angina Frequency (SAQ-AF) domains. Based on SAQ-AS scores, patients were stratified into an AS-negative group (n = 105, mean age 62 ± 8 years, 59% male) and an AS-positive group (n = 5, mean age 65 ± 15 years, 40% male). Similarly, SAQ-AF scores categorized patients into an AF-negative group (n = 101, mean age 63 ± 8 years, 60.4% male) and an AF-positive group (n = 9, mean age 60 ± 12 years, 33.3% male). Serum biomarkers, including low-density lipoprotein cholesterol (LDL-C), cardiac troponin I (cTnI), and N-terminal pro-B-type natriuretic peptide (NT-proBNP), were measured. An MCG score was measured for each patient both pre- and post-PCI. An MCG model and a combined model integrating MCG with biomarkers were developed using multivariable logistic regression. Performance was evaluated by the area under the receiver operating characteristic curve (AUC), sensitivity, and F1-score. Prediction models were visualized using nomograms.
Results:
MCG score decreased from 0.783 pre-PCI to 0.616 post-PCI. The combined model demonstrated superior discriminatory ability for SAQ-AS. For SAQ-AF, the combined model achieved higher sensitivity (0.663 vs. 0.605), F1-score (0.797 vs. 0.754), and AUC (0.813 vs. 0.801) compared to MCG alone. The nomogram provided broader risk stratification (SAQ-AS: 0.7-0.9; SAQ-AF: 0.5-0.9). Calibration was satisfactory (Hosmer-Lemeshow p > 0.05).
Conclusion:
A machine learning-based MCG model effectively predicts post-PCI angina risk. Integrating MCG with clinical biomarkers enhances risk stratification, offering a noninvasive strategy for identifying high-risk patients.
