ArticleExperimental and therapeutic medicine2026
A diagnostic and classification model integrating cTnI, CK-MB, MYO and NT-proBNP for cardiovascular diseases.
Article in Experimental and therapeutic medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Cardiovascular diseases (CVD) are among the leading causes of mortality worldwide and accurate diagnosis is important for treatment and prognosis. The present study intended to develop a model for diagnosing and classifying cardiovascular diseases to improve early diagnosis precision. The present retrospective study analyzed 116 cases of coronary artery disease (CAD), 26 cases of structural heart disease (SHD), and 58 cases of arrhythmias, along with 200 healthy controls. The present study analyzed the differences in demographic characteristics, clinical symptoms, cardiovascular biomarker levels and electrocardiogram results across these four groups. Multivariate logistic regression and random forests were used to identify significant factors in cardiovascular disease. A diagnostic model was constructed based on the selected factors, and its ability to diagnose cardiovascular diseases and differentiate between CAD, SHD and arrhythmias was evaluated. Patients with CVD showed significant differences compared with the healthy control group in demographic characteristics, including age, sex and family history. Different types of patients with CVD showed varying degrees of symptoms such as pain, dyspnea, and dizziness and there are significant differences in the levels of cardiovascular biomarkers. Cardiac Troponin I, creatine kinase MB, myoglobin and N-terminal pro B-type natriuretic peptide were identified as significant predictors of CVD. The diagnostic model for CVD achieved an Area Under the Curve (AUC) of 0.719, outperforming the use of individual biomarkers. The model differentiated arrhythmias from SHD with an AUC of 0.771 and SHD from CAD with an AUC of 0.715. The diagnostic model offers improved predictive ability for early cardiovascular disease diagnosis compared with standard methods and demonstrates clear advantages when distinguishing between CAD, SHD, and arrhythmias, supporting improved clinical decision-making.
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