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Updated: Mar 7, 2026

Microfluidics in Assessing Platelet Function
Published on: November 8, 2024
Platelet turnover predicts outcome after coronary intervention
Matthias K Freynhofer1, Liana Iliev, Veronika Bruno
1Matthias K. Freynhofer, MD, 3rd Department of Medicine, Cardiology, Wilhelminen Hospital, Montleartstraße 37, A-1160, Vienna, Austria, Tel.: +43 1 49150 2301, Fax: +43 1 49150 2309,
Insights
Elevated platelet turnover, indicated by reticulated platelet fraction (RPF), predicts major adverse cardiovascular events (MACE) and bleeding in patients undergoing percutaneous coronary intervention (PCI). RPF is a valuable tool for cardiovascular risk prediction in these patients.
Area of Science:
- Cardiology
- Hematology
- Biomarkers
Background:
- High platelet reactivity after percutaneous coronary intervention (PCI) is linked to major adverse cardiovascular events (MACE).
- Elevated platelet turnover is a known contributor to increased platelet reactivity.
Purpose of the Study:
- To evaluate the prognostic significance of platelet turnover and function for MACE after PCI with stent implantation.
- To determine if reticulated platelet fraction (RPF) and mean platelet volume (MPV) predict MACE.
Main Methods:
- A prospective observational study included 486 patients post-PCI on dual antiplatelet therapy (aspirin and clopidogrel).
- Platelet turnover was assessed using MPV and RPF.
- Platelet function was measured by multiple electrode aggregometry (MEA) and vasodilator-stimulated phosphoprotein-phosphorylation (VASP-P) assay.
- Major adverse cardiovascular events (MACE) and in-hospital bleeding were recorded at six-month follow-up.
Main Results:
- RPF and MPV were univariable predictors of MACE.
- RPF emerged as the sole platelet variable independently associated with MACE.
- A predictive model for MACE included troponin I, RPF, C-reactive protein (CRP), and a history of myocardial infarction.
- RPF was also independently associated with in-hospital bleeding events.
Conclusions:
- Reticulated platelet fraction (RPF) serves as an independent predictor of MACE and bleeding in patients on dual antiplatelet therapy post-PCI.
- RPF can be easily quantified via routine blood counts, suggesting its potential utility in cardiovascular risk stratification.
Abstract:
Elevated platelet turnover contributes to high platelet reactivity. High platelet reactivity after percutaneous coronary intervention (PCI) is associated with major adverse cardiovascular events (MACE). The purpose of this study was to determine the prognostic value of platelet turnover and function with regard to MACE after PCI with stent implantation. In this prospective observational study, 486 consecutive patients after PCI on aspirin and clopidogrel were included to determine platelet turnover (mean platelet volume (MPV), reticulated platelet fraction (RPF)) and platelet function (multiple electrode aggregometry (MEA), vasodilator-stimulated phosphoprotein-phosphorylation (VASP-P) assay). At six-months follow-up, MACE occurred in 10.7 % of patients. RPF (odds ratio [OR]=1.173 (95% confidence interval [CI 95 %] 1.040-1.324), p=0.009) and MPV (OR=1.459 (CI 95 % 1.059-2.008), p=0.021) were univariable predictors of MACE, whereas VASP-P (OR=1.016 (CI 95 % 1.000-1.032), p=0.052) and MEA (OR=0.999 (CI 95 % 0.980-1.017), p=0.895) failed to predict MACE. RPF remained the only platelet variable independently associated with MACE. The best model to predict MACE included: troponin I (OR=1.007 (CI 95 % 1.002-1.012), p=0.009), RPF (OR=1.136 (CI 95 % 1.001-1.288), p=0.048), CRP (OR=1.008 (CI 95 % 1.001-1.014), p=0.023) and history of myocardial infarction (OR=2.039 (CI 95 % 1.093-3.806), p=0.025). RPF (OR=1.211 (CI 95 % 1.042-1.406), p=0.012) was also independently associated with in-hospital bleedings. In conclusion, RPF as index of platelet turnover is an independent predictor of MACE and bleeding events in PCI patients on dual antiplatelet therapy. Since RPF can reliably be quantified along with routine haemograms, RPF might easily be applied in the setting of cardiovascular risk prediction.
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