Антикоагулянтная терапия при фибрилляции предсердий на фоне острого коронарного синдрома в реальной клинической практике по данным тотального регистра острого коронарного синдрома по Краснодарскому краю Ключевые слова: антитромбоцитарные препараты, геморрагические осложнения, оральные антикоагулянты, острый коронарный синдром, регистр, тромбоэмболические осложнения, фибрилляция предсердий

Background Myocardial infarction (MI) without obstructive lesion of coronary arteries (MINOCA) has incidence to 14%. Despite its high prevalence, MINOCA is paid not enough attention, therefore some patients can not receive appropriate treatment. Aim Evaluating the long-term results of clinical observation in patients with MINOCA in comparison with patients with obstructive damage and the subsequent revascularization of myocardium. Material On the basis of Scientific Research Institute RCH – 1 the multicenter cohort observation was organized. Patients from the register CROCS (the register of acute coronary syndrome in Krasnodar Region) with the diagnosis MI were divided into two groups: MINOCA including patients without obstructive damage of coronary arteries and MINOCA including patients with obstructive injury and the subsequent stenting a heart attack connected artery. All patients in 12 months would have control assessment during which they had objective survey; electrocardiography; daily monitoring; test of six-minute walking, echocardiography. Results In the MINOCA group in 12 months after the acute coronary event in 21.5% cases there were tension stenocardia, in 16.2% – a painless form of myocardial ischemia was revealed, in a group of patients with MINOCA stenocardia of tension was revealed at 12.5% and 8.4% – with painless ischemia. The group of patients with MINOCA is characterized by more reliable decrease in both parameters of heart rate variability, and turbulence of heart rate. MINOCA is followed by statistically significant increase in risk of ACS development and death within 12 months. All indicators had reliable difference. Conclusion Patients with MINOCA are a special group, their differential characteristics demand definite diagnostic efforts.

With the development of atrial fibrillation (AF), patients with acute coronary syndrome (ACS) are characterized by a twofold increase in the 30-day mortality compared with patients with sinus rhythm. In this regard, there is great interest in developing models of risk stratification to identify adverse outcomes in these patients with a view to more careful monitoring of patients in this group.Material and methods. For the construction of predictive models, a statistical method was used for the classification trees and, the procedure for neural networks implemented in the STATISTICA package. For the construction of prognostic models, a sample was used, consisting of 201 patients with and without fatal outcome; condition of each patient was described by 42 quantitative and qualitative clinical indices. Each patient belonged to one of 3 groups according to the type of AF: new-onset AF in ACS patient, paroxysmal AF, documented in an anamnesis before the episode of ACS and the constant or persistent form of AF.Results. To determine predictors of models predicting the possible fatal outcome of a patient, the Spearman correlation coefficient was used. Examination of the correlations for each of the 3 groups separately allowed to reveal clinical indicators for each group – predictors of predictive models with predominantly moderate correlations to the categorical variable “lethal outcome”. After analyzing the prognostic ability of the developed models, a software module was created in the Microsoft Visual C # 2015 programming environment to determine lethal outcome possibility in patients with ACS in the presence of AF using classification trees and neural networks.Conclusion. It is shown that for patients with ACS in the presence of AF, it is possible to construct mathematically based prognostic models that can reliably predict the lethal outcome possibility in patients based on actual values of clinical indices. In this case, clinical indicators can be both quantitative and qualitative (categorical), breaking patients into certain categories. Similar applications, unlike risk scales, are mathematically justified and can form the basis of systems for supporting decision-making.

Aim: to elucidate risk factors of development of atrial fibrillation (AF) in patients with acute coronary syndrome (ACS), and to assess of patient’s adherence to oral anticoagulant therapy (OAT) during 12 months after ACS episode according to the data of the Total ACS Registry for the Krasnodar Territory.Materials and methods. In this retrospective analysis we used Registry data on patients with ACS and concomitant AF, consecutively admitted to cardiological departments of the S.V. Ochapovsky Territorial Clinical Hospital from 20/11/2015 to 20/02/18. Number of patients in the analyzed group was 201 (52 with AF which first appeared in connection with the index ACS). Survivors after hospital discharge were contacted by telephone and at planned visits. The analysis included assessment of rates of the following outcomes: inhospital death, hemorrhagic and thromboembolic complications, prognostic efficacy of the CRISADE and HAS BLED scores, and expediency of prescription to patients with ACS and concomitant first AF episode of prolonged OAT after hospital discharge.Results. Demographic and anamnestic data of patients with the first AF attack at the background of ACS were like those of patients with other types of AF. This group of patients was characterized by more severe course of the disease, but this produced no impact on inhospital mortality and rate of complications, as well as on mortality for 12 months after hospital discharge.Conclusion. The results of this analysis are important for understanding distinctive characteristics of patients with AF first developed during ACS.

Background. According to the literature data, acute coronary syndrome (ACS) in 2-20 % of cases is combined with atrial fibrillation (AF). According to the current guidelines of the European Society of Cardiology (ESC), patients with coexisting AF and ACS should receive dual antiplatelet therapy for the prevention of recurrent cardiovascular events and anticoagulant therapy for the prevention of thromboembolic complications. However, this combination is fraught with the development of hemorrhagic syndrome.Aim. To develop a model and software module for predicting possible bleeding in patients with ACS combined with AF taking three-component antithrombotic therapy.Materials and Methods. To build prognostic models for the development of hemorrhagic syndrome, a statistical method was used for classification trees and the neural network procedure implemented in the STATISTICA package. To build prognostic models, a sample was used consisting of 201 patients with a combination of ACS and AF with and without fatal outcome, the state of which was described by 42 quantitative and qualitative clinical indicators. The control group included 205 patients with ACS and intact sinus rhythm.Results. To identify predictors of predictive models of the possible development of hemorrhagic syndrome in patients with triple antithrombotic therapy, the Spearman correlation coefficient was used. The study of correlations allowed to reveal clinical indicators – predictors of prognostic models. After analyzing the predictive ability of the developed models, a software module was created in the Microsoft Visual C # 2015 programming environment that allows determining the possibility of hemorrhagic syndrome in patients with a combination of ACS and AF using classification trees and neural networks.Сonclusion. A classification model and a software module were developed to predict possible bleeding in patients taking three-component antithrombotic therapy. Models contain both quantitative and qualitative (categorical) clinical indicators. The current level of development of data analysis technologies opens up broad horizons for medicine in solving problems on real medical data, without translating them into scoring risk scales, including prediction of the diagnosis of the disease, stage of the disease, treatment outcome, possible complications, etc. High reliability of such systems can be provided by large volumes of medical data accumulated on servers.