Konjestif Kalp Yetmezliği ve Atriyal Fibrilasyon Hastalarında Kalp Atım Hızı Değişkenliği Parametrelerinin Değerlendirilmesi.

Öz

Giriş ve Amaç: Konjestif kalp yetmezliği (KKY) ve atriyal fibrilasyon (AF) hastalarında ani kardiyak ölüm, bu hastalardaki ölümlerin önemli bir kısmını oluşturur. Ani ölüm riskini tahmin etmek için birçok parametre kullanılmaktadır. Ancak kalp atım hızı değişkenliği (KHD), yüksek tahmin gücü, düşük maliyet ve girişimsel olmayan bir ölçüm yöntemi olarak öne çıkmaktadır. Bazı KHD parametreleri, KKY hastalarında bağımsız prognostik bilgi sağlamıştır. Biz de bu çalışmada normal sinüs ritme sahip bireylerde, konjestif kalp yetmezliği (KKY) ve AF hastalarında KHD parametrelerini karşılaştırarak, bu hastalarda hangi parametreleri kullanmanın daha doğru olacağını göstermeyi amaçladık. Gereç ve Yöntemler: Çalışmamızda, Physionet Elektrokardiyografi (EKG) veritabanları kullanıldı. Kayıtlar üç grupta incelendi: Normal Sinüs Ritmi (NSR, n=18), Konjestif Kalp Yetersizliği (KKY, n=30) ve Atriyal Fibrilasyon (AF, n=30). KHD analizleri ile zaman-tabanlı, frekans-tabanlı ve doğrusal olmayan parametreler elde edildi. Tüm gruplar arasında doğrusal olmayan parametrelerin varyasyonlarını test etmek için parametrik olmayan bağımsız örnekler Kruskal Wallis testi, Dunn düzeltmesi ile birlikte kullanıldı. İstatistiksel anlamlılık düzeyi p < 0,05 olarak kabul edildi. Bulgular: Zaman-tabanlı parametreler incelendiğinde, AF grubunda ortalama kalp hızı, Atımlar arasındaki mesafenin standart sapması (SDNN), bu standart sapmanın karekök ortalaması (RMSSD)'nın NSR ve KKY gruplarına göre yüksek olduğu görüldü. Stres endeksi ise AF grubunda daha düşüktü. Frekans-tabanlı parametrelerde ise NSR grubunda çok düşük frekans (VLF)'ın diğer gruplara göre yüksek olduğu, sempatovagal dengenin ise KKY ve AF gruplarında, NSR grubuna göre arttığı görüldü. Doğrusal olmayan analizlerde DFAα1’in KKY ve AF gruplarında NSR grubuna göre arttığı gözlemlendi. Sonuç: Bulgularımız, doğrusal KHD parametrelerinin AF hastalarında, daha az güvenilir sonuçlar gösterdiğini ortaya koymaktadır. Bu bulgular, KHD parametrelerinin sinüs ritme sahip bireylerde risk sınıflandırması için önemli bir araç olabileceğini ancak, sinüs ritme sahip olmayan bireylerde daha fazla araştırma ve özellikle doğrusal olmayan analiz yöntemlerinin geliştirilmesi gerektiğini göstermektedir.

Anahtar Kelimeler

• kalp atım hızı değişkenliği
• atriyal fibrilasyon
• konjestif kalp yetmezliği
• doğrusal olmayan analizler
• sinüs ritim
• kalp hızı

Evaluation of Heart Rate Variability Parameters in Congestive Heart Failure and Atrial Fibrillation Patients.

Öz

Objective: Sudden cardiac death constitutes a significant portion of mortality in patients with congestive heart failure (KKY) and atrial fibrillation (AF). Numerous parameters are utilized to predict the risk of sudden death. However, heart rate variability (KHD) stands out as a method with high predictive power, low cost, and non-invasive measurement for estimating this risk. Certain KHD parameters have provided independent prognostic information in KKY patients. In this study, we aimed to compare HRV parameters in individuals with normal sinus rhythm, congestive heart failure (CHF), and atrial fibrillation (AF) patients to demonstrate which parameters would be more accurate to use in these patients. Results: It was observed that in the AF group, the average heart rate, standard deviation of the NN intervals (SDNN), and the square root of the mean squared differences of successive NN intervals (RMSSD) were higher compared to the NSR and KKY groups. However, the stress index was lower in the AF group. In terms of frequency-domain parameters, very low frequency (VLF) was found to be higher in the NSR group compared to the other groups, while the sympathovagal balance increased in the KKY and AF groups compared to the NSR group. In nonlinear analyses, DFAα1 was observed to be higher in the KKY and AF groups compared to the NSR group. Conclusion: Our findings suggest that linear KHD parameters yield less reliable results in AF patients. These findings indicate that KHD parameters could be an important tool for risk classification in individuals with sinus rhythm; however, further research, particularly the development of nonlinear analysis methods, is needed for individuals without sinus rhythm.

Anahtar Kelimeler

• heart rate
• heart rate variability
• atrial fibrillation
• congestive hearf failure
• nonlinear analyses
• sinus ritm

Kaynakça

1. 1. A. Hjalmarson et al., “Effects of controlled-release metoprolol on total mortality, hospitalizations, and well-being in patients with heart failure: the Metoprolol CR/XL Randomized Intervention Trial in congestive heart failure (MERIT-HF). MERIT-HF Study Group,” JAMA, vol. 283, no. 10, pp. 1295–1302, Mar. 2000, doi: 10.1001/jama.283.10.1295.
2. 2. L. Y. Chen, D. G. Benditt, and A. Alonso, “Atrial Fibrillation and Its Association With Sudden Cardiac Death,” Circ. J., vol. 78, no. 11, pp. 2588–2593, 2014, doi: 10.1253/circj.CJ-14-0814.
3. 3. M. N. Jarczok et al., “Heart rate variability in the prediction of mortality: A systematic review and meta-analysis of healthy and patient populations,” Neurosci. Biobehav. Rev., vol. 143, p. 104907, Dec. 2022, doi: 10.1016/j.neubiorev.2022.104907.
4. 4. J. Nolan et al., “Prospective study of heart rate variability and mortality in chronic heart failure: results of the United Kingdom heart failure evaluation and assessment of risk trial (UK-heart),” Circulation, vol. 98, no. 15, pp. 1510–1516, Oct. 1998, doi: 10.1161/01.cir.98.15.1510.
5. 5. M. T. La Rovere et al., “Short-term heart rate variability strongly predicts sudden cardiac death in chronic heart failure patients,” Circulation, vol. 107, no. 4, pp. 565–570, Feb. 2003, doi: 10.1161/01.cir.0000047275.25795.17.
6. 6. M. Malik et al., “Heart rate variability. Standards of measurement, physiological interpretation, and clinical use. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology.,” Eur. Heart J., vol. 17, no. 3, pp. 354–81, Mar. 1996.
7. 7. J. T. Bigger, P. Albrecht, R. C. Steinman, L. M. Rolnitzky, J. L. Fleiss, and R. J. Cohen, “Comparison of time- and frequency domain-based measures of cardiac parasympathetic activity in Holter recordings after myocardial infarction,” Am. J. Cardiol., 1989, doi: 10.1016/0002-9149(89)90436-0.
8. 8. F. Shaffer and J. P. Ginsberg, “An Overview of Heart Rate Variability Metrics and Norms,” Front. Public Health, vol. 5, pp. 258–258, 2017, doi: 10.3389/fpubh.2017.00258.

9. 9. G. D. Pinna et al., “Heart rate variability measures: a fresh look at reliability,” Clin. Sci., vol. 113, no. 3, pp. 131–140, Jul. 2007, doi: 10.1042/CS20070055.
10. 10. W.-H. Lin, D. Wu, C. Li, H. Zhang, and Y.-T. Zhang, “Comparison of Heart Rate Variability from PPG with That from ECG,” in The International Conference on Health Informatics, Y.-T. Zhang, Ed., in IFMBE Proceedings. Cham: Springer International Publishing, 2014, pp. 213–215. doi: 10.1007/978-3-319-03005-0_54.
11. 11. H. Kazdağlı, H. F. Özel, M. Özbek, Ş. Alpay, and M. Alenbey, “Classical heart rate variability and nonlinear heart rate analysis in mice under Napentobarbital and ketamine/xylazine anesthesia,” Turk. J. Med. Sci., vol. 52, no. 3, pp. 858–869, Jun. 2022, doi: 10.55730/1300-0144.5383.
12. 12. H. F. Ozel and H. Kazdagli, “A simple approach to determine loss of physiological complexity in heart rate series,” Biomed. Phys. Eng. Express, vol. 9, no. 4, p. 045015, May 2023, doi: 10.1088/2057-1976/acd254.
13. 13. S. Guzzetti et al., “Different spectral components of 24 h heart rate variability are related to different modes of death in chronic heart failure,” Eur. Heart J., vol. 26, no. 4, pp. 357–362, Feb. 2005, doi: 10.1093/eurheartj/ehi067.
14. 14. Developed with the special contribution of the European Heart Rhythm Association (EHRA) et al., “Guidelines for the management of atrial fibrillation: The Task Force for the Management of Atrial Fibrillation of the European Society of Cardiology (ESC),” Eur. Heart J., vol. 31, no. 19, pp. 2369–2429, Oct. 2010, doi: 10.1093/eurheartj/ehq278.
15. 15. L. Salahuddin, M. G. Jeong, and D. Kim, “Ultra Short Term Analysis of Heart Rate Variability using Normal Sinus Rhythm and Atrial Fibrillation ECG Data,” in 2007 9th International Conference on e-Health Networking, Application and Services, Jun. 2007, pp. 240–243. doi: 10.1109/HEALTH.2007.381639.
16. 16. A. L. Goldberger et al., “PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals,” Circulation, vol. 101, no. 23, pp. E215-220, Jun. 2000, doi: 10.1161/01.cir.101.23.e215.
17. 17. M. P. Tarvainen, J.-P. P. Niskanen, J. A. Lipponen, P. O. Ranta-aho, and P. A. Karjalainen, “Kubios HRV - Heart rate variability analysis software,” Comput. Methods Programs Biomed., vol. 113, no. 1, pp. 210–220, Jan. 2014, doi: 10.1016/j.cmpb.2013.07.024.
18. 18. F. Shaffer, R. McCraty, and C. L. Zerr, “A healthy heart is not a metronome: an integrative review of the heart’s anatomy and heart rate variability,” Front. Psychol., 2014, doi: 10.3389/fpsyg.2014.01040.
19. 19. R. E. Kleiger, J. P. Miller, J. T. Bigger, and A. J. Moss, “Decreased heart rate variability and its association with increased mortality after acute myocardial infarction,” Am. J. Cardiol., 1987, doi: 10.1016/0002-9149(87)90795-8.
20. 20. “Relationship between heart rate and outcomes in patients in sinus rhythm or atrial fibrillation with heart failure and reduced ejection fraction - Docherty - 2020 - European Journal of Heart Failure - Wiley Online Library.” https://onlinelibrary.wiley.com/doi/full/10.1002/ejhf.1682 (accessed Sep. 20, 2023).
21. 21. K. Umetani, D. H. Singer, R. McCraty, and M. Atkinson, “Twenty-four hour time domain heart rate variability and heart rate: relations to age and gender over nine decades.,” J. Am. Coll. Cardiol., vol. 31, no. 3, pp. 593–601, Mar. 1998.
22. 22. R. M. Baevsky and A. P. Berseneva, “Methodical recommendations use kardivar system for determination of the stress level and estimation of the body adaptability standards of measurements and physiological interpretation,” 2008.
23. 23. R. Maestri et al., “Reliability of heart rate variability measurements in patients with a history of myocardial infarction,” Clin. Sci., vol. 118, no. 3, pp. 195–201, Feb. 2010, doi: 10.1042/CS20090183.
24. 24. S. Vikman, T. H. Mäkikallio, S. Yli-Mäyry, M. Nurmi, K. E. J. Airaksinen, and H. V. Huikuri, “Heart rate variability and recurrence of atrial fibrillation after electrical cardioversion,” Ann. Med., vol. 35, no. 1, pp. 36–42, 2003, doi: 10.1080/07853890310004110.
25. 25. M. P. van den Berg, J. Haaksma, J. Brouwer, R. G. Tieleman, G. Mulder, and H. J. G. M. Crijns, “Heart Rate Variability in Patients With Atrial Fibrillation Is Related to Vagal Tone,” Circulation, vol. 96, no. 4, pp. 1209–1216, Aug. 1997, doi: 10.1161/01.CIR.96.4.1209.
26. 26. S. H. Kim et al., “Higher heart rate variability as a predictor of atrial fibrillation in patients with hypertension,” Sci. Rep., vol. 12, no. 1, Art. no. 1, Mar. 2022, doi: 10.1038/s41598-022-07783-3.
27. 27. U. R. Acharya, K. P. Joseph, N. Kannathal, C. M. Lim, and J. S. Suri, “Heart rate variability: a review,” Med. Biol. Eng. Comput., vol. 44, no. 12, pp. 1031–1051, 2006.
28. 28. R. Lampert et al., “Decreased heart rate variability is associated with higher levels of inflammation in middle-aged men,” Am. Heart J., vol. 156, no. 4, p. 759.e1-759.e7, 2008, doi: 10.1016/j.ahj.2008.07.009.
29. 29. P. Ponikowski et al., “Detection and significance of a discrete very low frequency rhythm in RR interval variability in chronic congestive heart failure,” Am. J. Cardiol., vol. 77, no. 15, pp. 1320–1326, 1996.
30. 30. H. Schmidt et al., “Autonomic dysfunction predicts mortality in patients with multiple organ dysfunction syndrome of different age groups,” Crit. Care Med., vol. 33, no. 9, pp. 1994–2002, 2005.
31. 31. M. Hadase et al., “Very Low Frequency Power of Heart Rate Variability is a Powerful Predictor of Clinical Prognosis in Patients with Congestive Heart Failure,” Circ. J., 2004, doi: 10.1253/circj.68.343.
32. 32. M. Candemir, B. Sezenöz, and M. Özdemir, “Predictors of Paroxysmal Atrial Fibrillation: Heart Rate Variability and Heart Rate Turbulence,” Kafkas J. Med. Sci., vol. 12, no. 1, pp. 65–70, 2022, doi: 10.5505/kjms.2022.65902.
33. 33. I. Szollosi, H. Krum, D. Kaye, and M. T. Naughton, “Sleep Apnea in Heart Failure Increases Heart Rate Variability and Sympathetic Dominance,” Sleep, vol. 30, no. 11, pp. 1509–1514, Nov. 2007, doi: 10.1093/sleep/30.11.1509.
34. 34. F. Shaffer, R. McCraty, and C. L. Zerr, “A healthy heart is not a metronome: an integrative review of the heart’s anatomy and heart rate variability,” Front. Psychol., 2014, doi: 10.3389/fpsyg.2014.01040.
35. 35. C. K. Peng, S. Havlin, H. E. Stanley, and A. L. Goldberger, “Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series,” Chaos, 1995, doi: 10.1063/1.166141.
36. 36. A. Mizobuchi, K. Osawa, M. Tanaka, A. Yumoto, H. Saito, and S. Fuke, “Detrended fluctuation analysis can detect the impairment of heart rate regulation in patients with heart failure with preserved ejection fraction,” J. Cardiol., vol. 77, no. 1, pp. 72–78, Jan. 2021, doi: 10.1016/j.jjcc.2020.07.027.
37. 37. “Detrended fluctuation analysis can detect the impairment of heart rate regulation in patients with heart failure with preserved ejection fraction - ScienceDirect.” https://www.sciencedirect.com/science/article/pii/S0914508720302744 (accessed Sep. 22, 2023).
38. 38. Regis Nunes Vargas, A. C. P. Veiga, and R. R. Linhares, “Atrial fibrillation detection by DFA and SDCST methods,” Model Assist. Stat. Appl., vol. 16, no. 3, pp. 189–196, Jan. 2021, doi: 10.3233/MAS-210532.
39. 39. D. Delignières and V. Marmelat, “Fractal fluctuations and complexity: Current debates and future challenges,” Crit. Rev. Biomed. Eng., vol. 40, no. 6, pp. 485–500, 2012, doi: 10.1615/CritRevBiomedEng.2013006727.