Biomarker-Based Risk Model to Predict Cardiovascular Mortality in Patients With Stable Coronary Disease.

Area of Science:

• Cardiology
• Biomarker Research
• Clinical Prediction Modeling

Background:

• No universally accepted model exists for predicting outcomes in stable coronary heart disease (CHD).
• Accurate risk stratification is crucial for managing patients with stable CHD.

Purpose of the Study:

• To evaluate and compare the prognostic value of biomarkers and clinical variables in stable CHD.
• To develop a robust biomarker-based prediction model for cardiovascular (CV) death in stable CHD patients.

Main Methods:

• Prospective analysis of 13,164 patients with stable CHD from the STABILITY trial.
• Multivariable Cox regression used to develop a prediction model incorporating age, biomarkers (NT-proBNP, hs-cTnT, LDL cholesterol), and clinical factors (smoking, diabetes, PAD).
• Internal bootstrap validation and external validation in 1,547 patients.

Main Results:

• N-terminal pro-B-type natriuretic peptide (NT-proBNP) and high-sensitivity cardiac troponin T (hs-cTnT) demonstrated superior prognostic value.
• The developed 'ABC-CHD' model showed high discriminatory ability for CV death (c-index 0.81 derivation, 0.78 validation).
• The model exhibited adequate calibration in both derivation and validation cohorts.

Conclusions:

• The ABC-CHD model offers a reliable tool for predicting CV death in stable CHD.
• Its basis on accessible biomarkers and clinical factors facilitates widespread clinical application.
• The model can enhance clinical assessment and inform cardiovascular risk management strategies.