Психогенная провокация сердечно-сосудистых заболеваний (ССЗ) стала предметом исследования кардиологов, психиатров, психологов и представителей других специальностей с середины XX века после появления работ W. Cannon [1], впервые описавшего гуморально-гормональные изменения при различных эмоциональных состояниях (страхе, гневе и т.д.), и H. Selye [2], сформулировавшего концепцию «стресса» (стресс-синдрома). Психогенно провоцированные ССЗ возникают в связи как с тяжелым неспецифическим стрессом (катастрофы и катаклизмы), так и с психотравмирующими ситуациями высокой личностной значимости. Последние варьируют от условно-патогенных обстоятельств обыденной жизни до стрессогенных в истинном смысле-смерть супруга или

Aims: To investigate the potential of a signal processed by smartphone-case based on single lead electrocardiogram (ECG) for left ventricular diastolic dysfunction (LVDD) determination as a screening method. Methods and Results: We included 446 subjects for sample learning and 259 patients for sample test aged 39 to 74 years for testing with 2D-echocardiography, tissue Doppler imaging and ECG using a smartphone-case based single lead ECG monitor for the assessment of LVDD. Spectral analysis of ECG signals (spECG) has been used in combination with advanced signal processing and artificial intelligence methods. Wavelengths slope, time intervals between waves, amplitudes at different points of the ECG complexes, energy of the ECG signal and asymmetry indices were analyzed. The QTc interval indicated significant diastolic dysfunction with a sensitivity of 78% and a specificity of 65%, a Tpeak parameter >590 ms with 63% and 58%, a T value off >695 ms with 63% and 74%, and QRSfi > 674 ms with 74% and 57%, respectively. A combination of the threshold values from all 4 parameters increased sensitivity to 86% and specificity to 70%, respectively (OR 11.7 [2.7-50.9], P < .001). Algorithm approbation have shown: Sensitivity—95.6%, Specificity—97.7%, Diagnostic accuracy—96.5% and Repeatability—98.8%. Conclusion: Our results indicate a great potential of a smartphone-case based on single lead ECG as novel screening tool for LVDD if spECG is used in combination with advanced signal processing and machine learning technologies.

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The article provides an overview of foreign and national studies of socio-demographic factors of patients' commitment to treatment in atrial fibrillation and other chronic diseases. Low adherence to treatment with long-term therapy is the main reason for the decline in treatment effectiveness. Socio-demographic predictors of commitment to treatment, such as gender, age, race, marital status, education and income, are considered. Analysis of the results of studies shows the ambiguity and frequently contradictory connection of these factors with the commitment to treatment. At the same time, adherence to treatment is studied in connection with individual-personal factors - personality traits, level of emotional intelligence, self-efficacy, motivational features. It is suggested that individual-personal and general socio-demographic factors interact and have an indirect effect on adherence to treatment in long-term therapy.

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