To investigate the application value of a comprehensive model integrating non-invasive myocardial work (MW) parameters and multi-dimensional cardiac parameters in risk prediction and early screening of coronary heart disease (CHD) , a prospective study was conducted involving 141 patients with CHD. Patients were divided based on coronary angiography (CAG) results: the CHD group (n=84, coronary stenosis ≥50%) and the non-CHD group (n=57, coronary stenosis <50%). Parameters showing intergroup differences were identified by univariate analysis. Independent risk factors for CHD were determined using multivariate binary logistic regression, which were then used to construct a multi-dimensional combined prediction model. Predictive performance was evaluated using receiver operating characteristic (ROC) curves, area under the curve (AUC), sensitivity, specificity, and optimal cut-off values. The DeLong test was used to compare AUC differences between the combined model and single parameters. Univariate analysis showed that the CHD group had significantly lower levels of global constructive work (GCW), global work efficiency (GWE), global work index (GWI), global longitudinal strain (GLS), left ventricular ejection fraction (LVEF), and high-density lipoprotein cholesterol (HDL-C), and significantly higher levels of global wasted work (GWW), epicardial adipose tissue (EAT), fasting blood glucose (FBG), triglycerides (TG), triglyceride-glucose (TyG) index, Gensini score, metabolic syndrome (Mets), and smoking history compared to the non-CHD group (all P<0.05). Multivariate logistic regression confirmed that GCW, GWE, EAT, and Mets were significantly associated with the occurrence of CHD. ROC curve analysis revealed that among individual parameters, EAT had the best predictive ability (AUC=0.760). The combined model incorporating the six above-mentioned parameters achieved an AUC of 0.940 (95% CI: 0.897–0.982), with a sensitivity of 89.3% and specificity of 89.5%, outperforming all single parameters, with an AUC significantly higher than that of EAT.

Cong Luo, Jiaoquan Wei. The value of non-invasive myocardial work parameters combined with cardiac multi-dimensional parameters in predicting the risk of coronary heart disease. Frontiers in Medical Science Research (2026), Vol. 8, Issue 4: 28-35. https://doi.org/10.25236/FMSR.2026.080404.

[1] Borén J, Chapman M J, Krauss R M, et al. Low-density lipoproteins cause atherosclerotic cardiovascular disease: pathophysiological, genetic, and therapeutic insights: a consensus statement from the European Atherosclerosis Society Consensus Panel[J]. Eur Heart J, 2020,41(24):2313-2330.

[2] Lubrano V, Balzan S. Status of biomarkers for the identification of stable or vulnerable plaques in atherosclerosis[J]. Clin Sci (Lond), 2021,135(16):1981-1997.

[3] Roth G A, Mensah G A, Johnson C O, et al. Global Burden of Cardiovascular Diseases and Risk Factors, 1990-2019: Update From the GBD 2019 Study[J]. J Am Coll Cardiol, 2020,76(25):2982-3021.

[4] Virani S S, Alonso A, Aparicio H J, et al. Heart Disease and Stroke Statistics-2021 Update: A Report From the American Heart Association[J]. Circulation, 2021,143(8):e254-e743.

[5] Smiseth O A, Donal E, Penicka M, et al. How to measure left ventricular myocardial work by pressure-strain loops[J]. Eur Heart J Cardiovasc Imaging, 2021,22(3):259-261.

[6] Lin J, Wu W, Gao L, et al. Global Myocardial Work Combined with Treadmill Exercise Stress to Detect Significant Coronary Artery Disease[J]. J Am Soc Echocardiogr, 2022,35(3):247-257.

[7] Edwards N F A, Scalia G M, Shiino K, et al. Global Myocardial Work Is Superior to Global Longitudinal Strain to Predict Significant Coronary Artery Disease in Patients With Normal Left Ventricular Function and Wall Motion[J]. J Am Soc Echocardiogr, 2019,32(8):947-957.

[8] Li M, Wang Y, Li L, et al. Global myocardial work in coronary artery disease patients without regional wall motion abnormality: Correlation with Gensini-score[J]. Clin Cardiol, 2024, 47(2):e24193.

[9] Zhao Y, He F, Guo W, et al. The clinical value of noninvasive left ventricular myocardial work in the diagnosis of myocardial ischemia in coronary heart disease: a comparative study with coronary flow reserve fraction[J]. Int J Cardiovasc Imaging, 2024,40(10):2167-2179.

[10] Iacobellis G. Local and systemic effects of the multifaceted epicardial adipose tissue depot[J]. Nat Rev Endocrinol, 2015,11(6):363-371.

[11] Packer M. Epicardial Adipose Tissue May Mediate Deleterious Effects of Obesity and Inflammation on the Myocardium[J]. J Am Coll Cardiol, 2018,71(20):2360-2372.

[12] Parisi V, Petraglia L, Formisano R, et al. Validation of the echocardiographic assessment of epicardial adipose tissue thickness at the Rindfleisch fold for the prediction of coronary artery disease[J]. Nutr Metab Cardiovasc Dis, 2020,30(1):99-105.

[13] Hirode G, Wong R J. Trends in the Prevalence of Metabolic Syndrome in the United States, 2011-2016[J]. JAMA, 2020,323(24):2526-2528.

[14] X Q, G A, Y C, et al. Associations of TyG index with coronary heart disease risk and coronary artery sclerosis severity in OSA[J]. Diabetology & metabolic syndrome, 2024,16(1):301.

[15] Vrints C, Andreotti F, Koskinas K C, et al. 2024 ESC Guidelines for the management of chronic coronary syndromes[J]. Eur Heart J, 2024,45(36):3415-3537.

[16] Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990-2021: a systematic analysis for the Global Burden of Disease Study 2021[J]. Lancet, 2024,403(10440):2100-2132.

[17] E M, ER B T, KA E K, et al. Accuracy of 2-dimensional speckle tracking echocardiography in diagnosis of coronary artery stenosis in stable angina pectoris[J]. Acta cardiologica, 2024,79(10):1111-1118.

[18] Russell K, Eriksen M, Aaberge L, et al. A novel clinical method for quantification of regional left ventricular pressure-strain loop area: a non-invasive index of myocardial work[J]. Eur Heart J, 2012, 33(6):724-733.

[19] Zhang J, Liu Y, Deng Y, et al. Non-invasive Global and Regional Myocardial Work Predicts High-Risk Stable Coronary Artery Disease Patients With Normal Segmental Wall Motion and Left Ventricular Function[J]. Front Cardiovasc Med, 2021,8:711547.

[20] Sabatino J, De Rosa S, Leo I, et al. Prediction of Significant Coronary Artery Disease Through Advanced Echocardiography: Role of Non-invasive Myocardial Work[J]. Front Cardiovasc Med, 2021,8:719603.

[21] Zhao Y, He F, Guo W, et al. The clinical value of noninvasive left ventricular myocardial work in the diagnosis of myocardial ischemia in coronary heart disease: a comparative study with coronary flow reserve fraction[J]. Int J Cardiovasc Imaging, 2024,40(10):2167-2179.

[22] Iacobellis G, Barbaro G. The double role of epicardial adipose tissue as pro- and anti-inflammatory organ[J]. Horm Metab Res, 2008,40(7):442-445.

[23] Mahdavi-Roshan M, Mozafarihashjin M, Shoaibinobarian N, et al. Evaluating the use of novel atherogenicity indices and insulin resistance surrogate markers in predicting the risk of coronary artery disease: a case‒control investigation with comparison to traditional biomarkers[J]. Lipids Health Dis, 2022,21(1):126.

[24] Cai G, Shi G, Xue S, et al. The atherogenic index of plasma is a strong and independent predictor for coronary artery disease in the Chinese Han population[J]. Medicine (Baltimore), 2017,96(37):e8058.