Characteristics of heart rate variability in patients with acute coronary syndrome without ST segment elevation in comparison with clinical and biochemical parameters

Cover Page

Full Text

Open Access Open Access
Restricted Access Access granted
Restricted Access Subscription or Fee Access

Abstract

BACKGROUND: The search for rational methods of primary, secondary, and tertiary prevention of coronary heart disease. To date, there are several publications on heart rate variability in ischemic heart disease.

AIM: To study the state of the regulatory systems in the organism of patients with acute coronary syndrome without ST segment elevation based on the heart rhythm, and their relationship with the clinical, biochemical and instrumental parameters of the disease.

MATERIALS AND METHODS: The open comparative study included 76 patients (62 men, 14 women) of mean age, 61.0 ± 0.9 years, who were admitted to the Emergency Cardiology Department diagnosed of acute coronary syndrome without ST segment elevation. On admission, cardiointervalometry was performed using Varicard 2.51 apparatus, and a number of clinical and biochemical parameters were evaluated

RESULTS: Multiple correlations of parameters of heart rate variability and clinical, biochemical and instrumental parameters were observed. From this, a cluster analysis of cardiointervalometry was performed, thereby stratifying patients into five clusters. Two extreme variants of dysregulation of the heart rhythm correlated with instrumental and laboratory parameters. A marked increase in the activity of the subcortical nerve centers (maximal increase of the spectral power in the very low frequency range with the underlying reduction of SDNN) in cluster 1 was associated with reduction of the left ventricular ejection fraction: cluster 1–47.0 [40.0; 49.0], cluster 2–60.0 [58.0; 64.0], cluster 3–60.0 [52.5; 64.5] % (the data are presented in the form of median and interquartile range; Me [Q25; Q75], p < 0,05). Cluster 5 showed significant reduction in SDNN (“monotonous rhythm”), combined with increased level of creatine phosphokinase (CPC): cluster 5–446,0 [186.0; 782.0], cluster 4–141.0 [98.0; 204.0] IU/l; Me [Q25; Q75], p < 0.05) and MВ-fraction of creatine phosphokinase; cluster 5–32.0 [15.0; 45.0], 4 cluster 4–12.0 [9.0; 18.0] IU/l; Me [Q25; Q75], p < 0.05).

CONCLUSIONS: In patients with acute coronary syndrome without ST segment elevation, cluster analysis of parameters of heart rate variability identified different peculiarities of regulation of the heart rhythm. Pronounced strain of the regulatory systems of the body was found to be associated with signs of severe pathology: the predominance of VLF (spectral power of the curve enveloping a dynamic range of cardiointervals in the very low frequency range) in spectral analysis with an underlying reduced SDNN is characteristic of patients with a reduced ejection fraction, and a “monotonous rhythm” is characteristic of patients with an increased level of creatine phosphokinase and MB-fraction of creatine phosphokinase.

Full Text

Restricted Access

About the authors

Aleksej A. Nizov

Ryazan State Medical University

Email: a.nizov@rzgmu.ru
ORCID iD: 0000-0001-7531-9102
SPIN-code: 2939-8193
ResearcherId: M-7081-2018

MD, PhD, Professor of the Department of Internal Diseases

Russian Federation, 9, st. High-voltage, Ryazan, 390026

Aleksej I. Girivenko

Ryazan State Medical University

Author for correspondence.
Email: giraly@yandex.ru
ORCID iD: 0000-0002-6882-7501
SPIN-code: 3082-7017

assistant

Russian Federation, 9, st. High-voltage, Ryazan, 390026

Mihail M. Lapkin

Ryazan State Medical University

Email: m.lapkin@rzgmu.ru
ORCID iD: 0000-0003-1826-8307
SPIN-code: 5744-5369
ResearcherId: S-2722-2016

MD, Dr. Sci. (Med.), Professor

Russian Federation, 9, st. High-voltage, Ryazan, 390026

Aleksej V. Borozdin

Ryazan State Medical University

Email: borozdin.a.v@yandex.ru
ORCID iD: 0000-0001-8912-8737
SPIN-code: 4660-2009

MD, Cand. Sci. (Med.)

Russian Federation, 9, st. High-voltage, Ryazan, 390026

Yana A. Belenikina

Ryazan State Medical University

Email: jnb22@rambler.ru
ORCID iD: 0000-0002-7325-5448
SPIN-code: 2937-5048

MD, Cand. Sci. (Med.)

Russian Federation, 9, st. High-voltage, Ryazan, 390026

Ekaterina I. Suchkova

Ryazan State Medical University

Email: katya.suchkova.1990@mail.ru
ORCID iD: 0000-0002-7997-0338
SPIN-code: 7506-6232
ResearcherId: G-7491-2019

MD, Cand. Sci. (Med.), Assistant of the Department of Internal Diseases

 
Russian Federation, 9, st. High-voltage, Ryazan, 390026

Irina V. Bikushova

Ryazan State Medical University

Email: irina-simagina@yandex.ru
ORCID iD: 0000-0002-4152-4885
SPIN-code: 5656-7976

assistant

Russian Federation, 9, st. High-voltage, Ryazan, 390026

References

  1. Vishnevsky A, Andreev E, Timonin S. Mortality from cardiovascular diseases and life expectancy in Russia. Demographic Review. 2016;3(1):6–34. (In Russ).
  2. Boytsov SA, Samorodskaia IV, Nikulina NN, et al. Comparative analysis of mortality from acute forms of ischemic heart disease during a 15-year period in the Russian Federation and the United States and the factors influencing its formation. Terapevticheskii Arkhiv. 2017;89(9):53–9. (In Russ). doi: 10.17116/terarkh201789953-59
  3. Bayevskiy RM, Ivanov GG, Chireykin LV, et al. Analiz variabel’nosti serdechnogo ritma pri ispol’zovanii razlichnykh elektrokardiograficheskikh sistem (metodicheskiye rekomendatsii). Vestnik Aritmologii. 2002;(24):65–87. (In Russ).
  4. Bayevskiy RM, Bayevskiy AR, Lapkin MM, et al. Mediko-fiziologicheskiye aspekty razrabotki apparatno-programmnykh sredstv dlya matematicheskogo analiza ritma serdtsa. I.P. Pavlov Russian Medical Biological Herald. 1996;(1–2):104–13. (In Russ).
  5. Hayano J, Ueda N, Kisohara M, et al. Survival Predictors of Heart Rate Variability After Myocardial Infarction With and Without Low Left Ventricular Ejection Fraction. Frontiers in Neuroscience. 2021;15:610955. doi: 10.3389/fnins.2021.610955
  6. Johnston BW, Barrett-Jolley R, Krige A, et al. Heart rate variability: Measurement and emerging use in critical care medicine. Journal of the Intensive Care Society. 2020;21(2):148–57. doi: 10.1177/175114371 9853744
  7. Fang S-C, Wu Y-L, Tsai P-S. Heart Rate Variability and Risk of All-Cause Death and Cardiovascular Events in Patients With Cardiovascular Disease: A Meta-Analysis of Cohort Studies. Biological Research for Nursing. 2020;22(1):45–56. doi: 10.1177/1099800419877442
  8. Liu Y, Scirica BM, Stultz CM, et al. Beatquency domain and machine learning improve prediction of cardiovascular death after acute coronary syndrome. Scientific Reports. 2016;6:34540. doi: 10.1038/srep34540
  9. Liu X, Xiang L, Tong G. Predictive values of heart rate variability, deceleration and acceleration capacity of heart rate in post-infarction patients with LVEF ≥35. Annals of Noninvasive Electrocardiology. 2020;25(6):e12771. doi: 10.1111/anec.12771
  10. Yuda E, Ueda N, Kisohara M, et al. Redundancy among risk predictors derived from heart rate variability and dynamics: ALLSTAR big data analysis. Annals of Noninvasive Electrocardiology. 2020;26(1):e12790. doi: 10.1111/anec.12790
  11. Lanza GA, Ruscio E, Ingrasciotta G, et al. Relation of vascular dilator function and cardiac autonomic function with coronary angiography findings in patients with non-ST segment elevation acute coronary syndrome. European Heart Journal. Acute Cardiovascular Care. 2021. Vol. 10, № 2. P. 164-169. doi: 10.1177/2048872620918714
  12. Ruda MYa, Averkov OV, Panchenko YeP, et al. Guideline for the management of patients with non-ST-elevation acute coronary syndromes (Part 1). Kardiologicheskij Vestnik. 2017;12(3):3–28. (In Russ).
  13. Ruda MYa, Averkov OV, Panchenko YeP, et al. Guideline for the management of patients with non-ST-elevation acute coronary syndromes (Part 2). Kardiologicheskij Vestnik. 2018;13(1):59–62. (In Russ). doi: 10.17116/cardiobulletin201813159-62
  14. Merkulova MA, Lapkin MM, Zorin RA. The use of cluster analysis and the theory of artificial neural networks to predict the effectiveness of targeted human activity. Nauka Molodykh (Eruditio Juvenium). 2018;6(3):374–82. (In Russ). doi: 10.23888/HMJ201863374-382
  15. Mirkin BG. Metody klaster-analiza dlya podderzhki prinyatiya resheniy: obzor. Moscow: Vysshaya shkola ekonomiki; 2011. (In Russ).
  16. Zorin RA, Lapkin MM, Trutneva EA, et al. Physiological costs can predict effectiveness of cognitive activity. Doctor.Ru. 2012;(10):24–8. (In Russ).
  17. Shvartz VA, Kiselev AR, Karavaev AS, et al. Comparative study of short-term cardiovascular autonomic control in cardiac surgery patients who underwent coronary artery bypass grafting or correction of valvular heart disease. Journal of Cardiovascular and Thoracic Research. 2018;10(1):28–35. doi: 10.15171/jcvtr.2018.05

Supplementary files

Supplementary Files
Action
1. Fig. 1. A dendrogram showing the distribution of patients based on heart rate variability parameters: according to the heart rate variability parameters, the patients form distinct groups (clusters).

Download (71KB)

Copyright (c) 2021 Nizov A.A., Girivenko A.I., Lapkin M.M., Borozdin A.V., Belenikina Y.A., Suchkova E.I., Bikushova I.V.


Media Registry Entry of the Federal Service for Supervision of Communications, Information Technology and Mass Communications (Roskomnadzor) PI No. FS77-76803 dated September 24, 2019.



This website uses cookies

You consent to our cookies if you continue to use our website.

About Cookies