Introduction: The objective of the study was to evaluate the capability of magnetocardiography (MCG) in distinguishing obstructive coronary heart disease (OCHD) from coronary microvascular dysfunction (CMVD). Methods: Patients with chest pain suspected of having CAD were consecutively enrolled in according to the inclusion and exclusion criteria. All patients underwent coronary angiography and coronary blood flow analysis, and were divided into OCHD group (n = 447) and CMVD group (n = 332) based on the results of imaging analysis. All patients also underwent MCG examination before or after coronary angiology in 48 h, the MCG parameters were analyzed by an independent third party. Univariate and multivariate logistic regression analyses were performed to identify significant clinical and MCG parameters between groups. Diagnostic models were constructed using selected MCG parameters alone or in combination with clinical indicators. Performance was assessed using receiver operating characteristic curves, accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Clinical utility and model performance were further validated via calibration curves and decision curve analysis. Results: In OCHD group, the prevalence of type 2 diabetes mellitus (35.79% vs. 25.30%, p = 0.022) and interventricular septal thickness (mm) (11.00 10.25–12.00 vs. 11.00 10.00–11.00, p < 0.001) was significantly higher than those in the CMVD group, while high-density lipoprotein cholesterol (mmol/L) was significantly lower (1.01 0.85–1.16 vs. 1.06 0.93–1.22, p < 0.001). Seven MCG parameters showed significant intergroup differences: QR-enav (10.08 9.02–11.22 vs. 9.36 8.38–10.41, p < 0.001), RS-enav (10.29 9.32–11.33 vs. 9.66 8.71–10.73, p < 0.001), TT-dicar (0.04 0.02–0.14 vs. 0.02 0.01–0.09, p < 0.001), TT-dicd (41.40 25.75–77.55 vs. 34.40 20.83–62.90, p < 0.001), TT-didav (60.70 55.50–66.40 vs. 63.80 58.02–67.80, p < 0.001), TT-didmse (3.70 2.10–6.35 vs. 2.90 1.70–4.77, p < 0.001), and TT-enav (10.44 9.20–11.57 vs. 9.96 8.79–11.01, p < 0.001). RS-enav achieved the highest AUC (0.615), with sensitivity, specificity, PPV, and NPV of 48.77%, 69.21%, 70.10%, and 47.72%, respectively. TT-didmse exhibited specificity of 73.51% and PPV of 69.41%, while TT-enav showed specificity of 60.26% and PPV of 67.21%. TT-dicd demonstrated the highest diagnostic sensitivity (78.30%) in distinguishing OCHD patients from CMVD. The integrated model combining seven MCG parameters yielded an AUC of 0.681 (95% CI: 0.642–0.720), which improved to 0.713 (95% CI: 0.674–0.751) in the combined model. Calibration curves confirmed high agreement between nomogram predictions and observed outcomes, and decision curve analysis indicated better net benefit for the combined model. Conclusion: MCG parameters, either alone or in combination with clinical indicators, demonstrate feasibility in distinguishing OCHD from CMVD. As a novel noninvasive diagnostic tool, MCG holds potential value in discriminating between OCHD and CMVD.
Bao et al. (Fri,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: