Prediction of high on-treatment platelet reactivity in clopidogrel-treated patients with acute coronary syndromes.
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 1month 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 1month, 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 1month).


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.
Unità di Medicina III, ASST Santi Paolo e Carlo, Dipartimento di Scienze della Salute, Università degli Studi di Milano, Milano, Italy.
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Podda
Centro Diagnostico Italiano, Milano, Italy.
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Grossi
Dipartimento Cardiovascolare, Policlinico S. Orsola, Bologna, Italy.
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Palmerini
Semeion Research Centre, Roma, Italy.
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Buscema
Unità di Medicina III, ASST Santi Paolo e Carlo, Dipartimento di Scienze della Salute, Università degli Studi di Milano, Milano, Italy.
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Femia
Dipartimento Cardiovascolare, Policlinico S. Orsola, Bologna, Italy.
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Della Riva
Unità Coronarica IRCCS Policlinico San Matteo, Pavia, Italy.
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de Servi
Divisione di Cardiologia, Seconda Università di Napoli, Napoli, Italy.
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Calabrò
Dipartimento di Medicina e Chirurgia, Schola Medica Salernitana, Università di Salerno, Salerno, Italy.
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Piscione
Unità di Cardiologia, Servizio di Emodinamica, Istituto Ospedaliero Fondazione Poliambulanza, Brescia, Italy.
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Maffeo
Divisione di Cardiologia, Ospedale Santo Stefano, Prato, Italy.
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Toso
Ospedale del Cuore, Fondazione Toscana Gabriele Monasterio, Massa, Italy.
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Palmieri
Dipartimento Cardiotoracico e Vascolare, Azienda Ospedaliero-Universitaria Pisana, Pisa, Italy.
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De Carlo
Ospedale Ferrarotto, Università di Catania, Catania, Italy.
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Capodanno
The Cardiovascular Research Foundation, New York, NY, USA.
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Genereux
Unità di Medicina III, ASST Santi Paolo e Carlo, Dipartimento di Scienze della Salute, Università degli Studi di Milano, Milano, Italy. Electronic address: marco.cattaneo@unimi.it.
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Cattaneo
D054058:Acute Coronary Syndrome / Q000097:blood / Q000188:drug therapy* / Q000235:genetics*; D000368:Aged; D000077144:Clopidogrel; D005260:Female; D053263:Gene Regulatory Networks / Q000187:drug effects / Q000502:physiology; D006801:Humans; D000069550:Machine Learning; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D015539:Platelet Activation / Q000187:drug effects* / Q000502:physiology; D010975:Platelet Aggregation Inhibitors / Q000494:pharmacology / Q000627:therapeutic use; D011237:Predictive Value of Tests; D011446:Prospective Studies; D013988:Ticlopidine / Q000031:analogs & derivatives* / Q000494:pharmacology / Q000627:therapeutic use; D016896:Treatment Outcome
D010975:Platelet Aggregation Inhibitors; D000077144:Clopidogrel; D013988:Ticlopidine
Antiplatelet therapy; Artificial neural networks; Clopidogrel; Platelet reactivity; VASP