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Biomarker-Based Prediction of Recurrent Ischemic Events in ACS
Quick Takes
- This study leverages previously collected samples from 10,713 patients in the PLATO trial and 3,508 patients in the TRACER trial to create a model predictive of CV death and MI at 1 year in patients with ACS who underwent PCI.
- Modeling relied on traditional statistical approaches and a priori selected clinical variables and specific biomarkers.
- The ABC-ACS ischemia model incorporating age, GDF-15, NT-proBNP, extent of coronary disease, vascular disease, Killip class, ACS type, and P2Y12 inhibitor was predictive of CV death and MI at 1 year post-PCI.
Study Questions:
How can we best predict cardiovascular (CV) death or myocardial infarction (MI) in patients with acute coronary syndrome (ACS) who underwent percutaneous coronary intervention (PCI)?
Methods:
This study leveraged previously collected samples from 10,713 patients enrolled in the PLATO (A Comparison of Ticagrelor and Clopidogrel in Patients With Acute Coronary Syndrome) trial and 3,508 patients from the TRACER (Thrombin Receptor Antagonist for Clinical Event Reduction in Acute Coronary Syndrome) trial, in which the following biomarkers were measured: N-terminal pro–B-type natriuretic peptide (NT-proBNP), high-sensitivity troponin T (hs-TnT), C-reactive protein (CRP), growth differentiation factor 15 (GDF-15), and cystatin C. In an attempt to identify the model most strongly predictive of CV death or MI (primary outcome), the authors used a multi-step modeling framework incorporating these biomarkers in addition to clinical variables, using the PLATO data for development of the model and TRACER data for validation.
Results:
There were 632 and 190 episodes of CV death/MI in the development and validation cohorts. The most important predictors of CV death/MI were the biomarkers, GDF-15, and NT-proBNP, which had greater prognostic value than all candidate variables. The final model included eight items: age (A), biomarkers (B) (GDF-15 and NT-proBNP), and clinical variables (C) (extent of coronary artery disease, previous vascular disease, Killip class, ACS type, P2Y12 inhibitor). The model, named ABC-ACS ischemia, was well calibrated and showed good discriminatory ability for 1-year risk of CV death/MI with C-indices of 0.71 and 0.72 in the development and validation cohorts, respectively. Predictive ability for MI alone was weaker (0.65 and 0.68, respectively). The ABC-ACS ischemia model outperformed the GRACE 2.0 risk score in predicting all examined outcomes.
Conclusions:
The ABC-ACS ischemia model incorporating age, GDF-15, NT-proBNP, extent of coronary disease, vascular disease, Killip class, ACS type, and P2Y12 inhibitor was predictive of CV death and MI at 1 year post-PCI.
Perspective:
This study used a traditional approach to derive and validate a risk score for the prediction of outcomes post-PCI in patients with ACS. Among both clinical and biomarker variables, NT-proBNP and GDF-15 were the most important variables in the modeling, and likely drove the advantage of the ABC-ACS ischemia score compared to GRACE 2.0. NT-proBNP levels incorporate data related to myocardial stretch and risk of future heart failure, while GDF-15 is a cytokine related to cellular stress and aging. Neither CRP nor hs-TnT made the cut; likely due to their acutely increased levels during ACS, which may not reflect long-term conditions.
While this study highlights the value of biomarkers for predictive modeling, the clinical implications are not clear. The authors surmise that the score could be used to balance long-term risk of ischemic and bleeding events through assigning duration of antithrombotic therapies, but that will have to be determined prospectively. The biomarkers used in this study were pre-selected; many other biomarkers shown to be predictive of outcomes were not included. A proteomic approach to biomarker assessment and machine learning for modeling, while most costly, may have perhaps yielded even better predictive models. With the expanding accessibility and decreasing costs of proteomics, this may well be possible.
Clinical Topics: Acute Coronary Syndromes, Invasive Cardiovascular Angiography and Intervention, Prevention, Atherosclerotic Disease (CAD/PAD), ACS and Cardiac Biomarkers, Interventions and ACS, Interventions and Coronary Artery Disease
Keywords: Acute Coronary Syndrome, Biomarkers, Clopidogrel, Coronary Artery Disease, C-Reactive Protein, Cystatin C, Growth Differentiation Factor 15, Myocardial Infarction, Myocardial Ischemia, Natriuretic Peptide, Brain, Percutaneous Coronary Intervention, Receptors, Thrombin, Risk Factors, Secondary Prevention, Ticagrelor, Troponin T, Vascular Diseases
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