Objectives: High platelet reactivity (HPR) was studied in patients presenting with ST-segment elevation myocardial infarction (STEMI) due to stent thrombosis (ST) undergoing immediate percutaneous coronary intervention (PCI).
Background: HPR on P2Y inhibitors (HPR-ADP) is frequently observed in stable patients who have experienced ST. The HPR rates in patients presenting with ST for immediate PCI are unknown.
Methods: Consecutive patients presenting with definite ST were included in a multicenter ST registry. Platelet reactivity was measured before immediate PCI with the VerifyNow P2Y or Aspirin assay.
Results: Platelet reactivity was measured in 129 ST patients presenting with STEMI undergoing immediate PCI. HPR-ADP was observed in 76% of the patients, and HPR on aspirin (HPR-AA) was observed in 13% of the patients. HPR rates were similar in patients who were on maintenance P2Y inhibitor or aspirin since stent placement versus those without these medications. In addition, HPR-ADP was similar in patients loaded with a P2Y inhibitor shortly before immediate PCI versus those who were not. In contrast, HPR-AA trended to be lower in patients loaded with aspirin as compared with those not loaded.
Conclusions: Approximately 3 out of 4 ST patients with STEMI undergoing immediate PCI had HPR-ADP, and 13% had HPR-AA. Whether patients were on maintenance antiplatelet therapy while developing ST or loaded with P2Y inhibitors shortly before undergoing immediate PCI had no influence on the HPR rates. This raises concerns that the majority of patients with ST have suboptimal platelet inhibition undergoing immediate PCI.
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http://dx.doi.org/10.1016/j.jcin.2017.09.019 | DOI Listing |
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Faculty of Medical and Health Sciences, Tel Aviv University, Tel Aviv 6997801, Israel.
In this study, we introduce a novel approach that integrates interpretability techniques from both traditional machine learning (ML) and deep neural networks (DNN) to quantify feature importance using global and local interpretation methods. Our method bridges the gap between interpretable ML models and powerful deep learning (DL) architectures, providing comprehensive insights into the key drivers behind model predictions, especially in detecting outliers within medical data. We applied this method to analyze COVID-19 pandemic data from 2020, yielding intriguing insights.
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