Key result
Four-biomarker model identifies cardiovascular disease with an AUC of ~0.72, outperforming individual markers.
Why the study?
Accurate diagnosis and classification of cardiovascular diseases are important for treatment and prognosis, but improved early diagnostic precision is needed.
Does a diagnostic model integrating cTnI, CK-MB, MYO, and NT-proBNP improve the early diagnostic precision and classification of cardiovascular diseases in adults?
Cohort (n=400)
No
Does a diagnostic model integrating cTnI, CK-MB, MYO, and NT-proBNP improve the early diagnostic precision and classification of cardiovascular diseases in adults?
Effect estimate: AUC 0.719 (95% CI 0.670-0.769)
A diagnostic model integrating cTnI, CK-MB, MYO, and NT-proBNP demonstrates improved predictive ability for early cardiovascular disease diagnosis and aids in distinguishing between coronary artery disease, structural heart disease, and arrhythmias compared to single biomarkers.
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.
No takes yet. Share an insight, caveat, or question.
Zhang et al. (2026) conducted a cohort in Cardiovascular diseases (Coronary artery disease, Structural heart disease, Arrhythmias) (n=400). Diagnostic model integrating cTnI, CK-MB, MYO and NT-proBNP vs. Individual biomarkers was evaluated on Ability of the diagnostic model to distinguish between patients with cardiovascular disease and healthy controls (Area Under the Curve) (AUC 0.719, 95% CI 0.670-0.769). A diagnostic model integrating cTnI, CK-MB, MYO, and NT-proBNP differentiated cardiovascular disease patients from healthy controls with an AUC of 0.719, outperforming individual biomarkers.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: