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Cardiac biomarkers are critical in diagnosing, prognosing, and managing cardiovascular diseases. Routine measurement of specific biomarkers such as B-type natriuretic peptide (BNP), C-reactive protein (CRP), and homocysteine (Hcy) is common practice in clinical settings to evaluate heart function and predict cardiovascular events.
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Cardiac biomarkers are enzymes, proteins, and hormones released into the blood when cardiac cells are injured. They are powerful tools for triaging.
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Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
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Related Experiment Video

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Acute Coronary Syndrome Subphenotypes Based on Repeated Biomarker Measurements in Relation to Long-Term Mortality

Marie de Bakker1, Niels T B Scholte1, Rohit M Oemrawsingh2

  • 1Department of Cardiology Erasmus MC, University Medical Center Rotterdam Rotterdam The Netherlands.

Journal of the American Heart Association
|January 12, 2024
PubMed
Summary

Identifying post-acute coronary syndrome (ACS) subphenotypes using repeated biomarker measurements reveals distinct long-term mortality risks. Persistently elevated biomarkers indicate the worst outcomes, regardless of recurrent ACS.

Keywords:
acute coronary syndromecardiovascular biomarkersdeathphenotypesrepeated measurements

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Area of Science:

  • Cardiology
  • Biomarker Research
  • Clinical Trials

Background:

  • Post-acute coronary syndrome (ACS) patient outcomes vary.
  • Identifying distinct patient subphenotypes is crucial for risk stratification.
  • Repeated biomarker measurements may offer insights into these subphenotypes.

Purpose of the Study:

  • To identify subphenotypes of patients post-ACS using longitudinal biomarker data.
  • To investigate the association between these subphenotypes and long-term mortality risk.

Main Methods:

  • Observational study (BIOMArCS) with high-frequency blood sampling for 1 year in ACS patients.
  • Cluster analysis of repeated measurements of cardiac troponin T, NT-proBNP, hs-CRP, and GDF-15.
  • Accelerated failure time models used to evaluate all-cause mortality over a median of 9.1 years.

Main Results:

  • Three distinct biomarker-based subphenotypes were identified: low/stable, decreasing, and persistently elevated concentrations.
  • Persistently elevated biomarker concentrations (cluster 3) were associated with the highest long-term mortality risk.
  • Patients with persistently elevated biomarkers had similar poor outcomes to those with recurrent ACS in the first year.

Conclusions:

  • Subphenotypes of post-ACS patients with varying long-term mortality risks can be identified using repeated cardiovascular biomarker measurements.
  • Persistently elevated biomarker levels are a strong indicator of adverse long-term outcomes, irrespective of early recurrent ACS.