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Published on: January 28, 2020
Biomarker-Based Risk Model to Predict Cardiovascular Mortality in Patients With Stable Coronary Disease
Daniel Lindholm1, Johan Lindbäck2, Paul W Armstrong3
1Department of Medical Sciences, Cardiology, Uppsala University, Uppsala, Sweden; Uppsala Clinical Research Center, Uppsala University, Uppsala, Sweden.
Insights
A new ABC-CHD model accurately predicts cardiovascular death in stable coronary heart disease (CHD) patients. It uses readily available biomarkers like NT-proBNP and hs-cTnT, along with clinical factors, to guide patient management.
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.
Background:
Currently, there is no generally accepted model to predict outcomes in stable coronary heart disease (CHD).
Objectives:
This study evaluated and compared the prognostic value of biomarkers and clinical variables to develop a biomarker-based prediction model in patients with stable CHD.
Methods:
In a prospective, randomized trial cohort of 13,164 patients with stable CHD, we analyzed several candidate biomarkers and clinical variables and used multivariable Cox regression to develop a clinical prediction model based on the most important markers. The primary outcome was cardiovascular (CV) death, but model performance was also explored for other key outcomes. It was internally bootstrap validated, and externally validated in 1,547 patients in another study.
Results:
During a median follow-up of 3.7 years, there were 591 cases of CV death. The 3 most important biomarkers were N-terminal pro-B-type natriuretic peptide (NT-proBNP), high-sensitivity cardiac troponin T (hs-cTnT), and low-density lipoprotein cholesterol, where NT-proBNP and hs-cTnT had greater prognostic value than any other biomarker or clinical variable. The final prediction model included age (A), biomarkers (B) (NT-proBNP, hs-cTnT, and low-density lipoprotein cholesterol), and clinical variables (C) (smoking, diabetes mellitus, and peripheral arterial disease). This "ABC-CHD" model had high discriminatory ability for CV death (c-index 0.81 in derivation cohort, 0.78 in validation cohort), with adequate calibration in both cohorts.
Conclusions:
This model provided a robust tool for the prediction of CV death in patients with stable CHD. As it is based on a small number of readily available biomarkers and clinical factors, it can be widely employed to complement clinical assessment and guide management based on CV risk. (The Stabilization of Atherosclerotic Plaque by Initiation of Darapladib Therapy Trial [STABILITY]; NCT00799903).
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