Prediction of high on-treatment platelet reactivity in clopidogrel-treated patients with acute coronary syndromes. (1st August 2017)

• Record Type: Journal Article
• Authors: Podda, G.M.; Grossi, E.; Palmerini, T.; Buscema, M.; Femia, E.A.; Della Riva, D.; de Servi, S.; Calabrò, P.; Piscione, F.; Maffeo, D.; Toso, A.; Palmieri, C.; De Carlo, M.; Capodanno, D.; Genereux, P.; Cattaneo, M.
• Is Part Of: International journal of cardiology. Volume 240(2017)
• Page Start: 60
• Page End: 65
• Publication Date: 2017-08-01
• Subjects: Clopidogrel -- Antiplatelet therapy -- Platelet reactivity -- VASP -- Artificial neural networks; Cardiology -- Periodicals; Electronic journals; 616.12
• DOI: 10.1016/j.ijcard.2017.03.074
• Languages: English
• ISSNs: 0167-5273

Abstract

Abstract: Background: About 40% of clopidogrel-treated patients display high platelet reactivity (HPR). Alternative treatments of HPR patients, identified by platelet function tests, failed to improve their clinical outcomes in large randomized clinical trials. A more appealing alternative would be to identify HPR patients a priori, based on the presence/absence of demographic, clinical and genetic factors that affect PR. Due to the complexity and multiplicity of these factors, traditional statistical methods (TSMs) fail to identify a priori HPR patients accurately. The objective was to test whether Artificial Neural Networks (ANNs) or other Machine Learning Systems (MLSs), which use algorithms to extract model-like 'structure' information from a given set of data, accurately predict platelet reactivity (PR) in clopidogrel-treated patients. Methods: A complete set of fifty-nine demographic, clinical, genetic data was available of 603 patients with acute coronary syndromes enrolled in the prospective GEPRESS study, which showed that HPR after 1 month of clopidogrel treatment independently predicted adverse cardiovascular events in patients with Syntax Score > 14. Data were analysed by MLSs and TSMs. ANNs identified more variables associated PR at 1 month, compared to TSMs. Results: ANNs overall accuracy in predicting PR, although superior to other MLSs was 63% (95% CI 59–66). PR phenotype changed in both directions in 35% of patients across the 3 time points tested (before PCI, at hospital discharge and at 1 month). Conclusions: Despite their ability to analyse very complex non-linear phenomena, ANNs or MLS were unable to predict PR accurately, likely because PR is a highly unstable phenotype.