[Multifactorial Prognostication of the Development of Stent Thrombosis in Patients with Acute Coronary Syndrome after Percutaneous Coronary Intervention on the background of Dual Antiplatelet Therapy].

Autor:	Korotaeva ES; Privolzhsky Research Medical University., Koroleva LY; Privolzhsky Research Medical University., Nosov VP; Privolzhsky Research Medical University., Kovaleva GV; Nizhny Novgorod Regional Clinical Hospital named after N. A. Semashko., Kuzmenko EA; Nizhny Novgorod Regional Clinical Hospital named after N. A. Semashko.
Jazyk:	ruština
Zdroj:	Kardiologiia [Kardiologiia] 2019 Dec 11; Vol. 59 (11), pp. 5-13. Date of Electronic Publication: 2019 Dec 11.
DOI:	10.18087/cardio.2019.11.n343
Abstrakt:	Aim: to identify predictors of stent thrombosis in patients with acute coronary syndrome (ACS) after percutaneous coronary intervention (PCI) for 12 months by creating a miathematical logistic regression model to optimize rehabilitation, secondary prevention of ischemic events in the first year after ACS, as well as a personalized approach to treatment. Materials and Methods: The analysis used data from the hospital register, which contained information on all PCIs, performed in the Semashko hospital between September 2016 and August 2018 (2378 patients). For this study we selected a sample of 183 ACS patients (146 men and 37 women) after PCI: 25 with definite stent thrombosis confirmed by repeated coronary angiography (CAG) (the main study group), and 158 without developing definite stent thrombosis (the comparison group) according to the observation for 12 months. All patients during hospitalization and 1 year after discharge received standard medical therapy ACS, according to international recommendations. Laboratory tests, electrocardiography (ECG), echocardiography, 24-hour ECG monitoring were performed for in patients. For determining predictors of the development of stent thrombosis we performed a logistic regression analysis. Results: A mathematical model of multifactorial prognostication of stent thrombosis in patients with ACS after PCI was created. The model included the following predictors: Killip class >II; life-threatening paroxysmal tachyarrhythmias (atrial fibrillation and/or ventricular fibrillation) as ACS complication of; left ventricular ejection fraction ≤45%; CA dissection; CAG confirmed CA thrombosis before PCI. Conclusion: The proposed model in patients with ACS allows us to estimate the risk of stent thrombosis after PCI, as well as to improve the accuracy of the event prediction. The model is easy to use, can be applied by practicing cardiologists during hospitalization. This model allows us to personalize secondary prevention in the first year after ACS, and thus help to reduce cardiovascular mortality, incidence of recurrent myocardial infarctions, unstable angina, and emergency revascularization.
Databáze:	MEDLINE
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