Analysis of heart rate variability as a predictor of mortality in cardiovascular patients of intensive care unit

• Autorzy: Moridani M. K. , Setarehdan S. K. , Nasrabadi A. M. , Hajinasrollah E.

Identyfikatory

DOI

10.1016/j.bbe.2015.05.004

• Języki publikacji: EN

Abstrakty

EN

Objective: Dynamic changes of heart rate variability (HRV) reflect autonomic dysfunction in cardiac disease. Some studies suggest the role of HRV in predicting intensive care unit (ICU) mortality. The main object of this study was analyzing the HRV to design an algorithm to predict mortality risk. Methods: We evaluated 80 cardiovascular ICU patients (45 males and 45 females), ranging from 45 to 70 years. Common time and frequency domain analysis, non-linear Poincaré plot and recurrence quantification analysis (RQA) were used to study the HRV in two episodes. The episodes include 8–4 h before death, and 4 h before death to death. Independent sample t-test was used as statistical analysis. Results: Statistical analysis indicates that frequency domain and Poincaré parameters such as LF/HF and SD2/SD1 show changes in transition to death episode (p < 0.05). Moreover, L_mean, v_max and RT measures showed meaningful changes (p < 0.01) in closer segments to the death. Conclusions: Analysis of physiological variables shows that there are significant differences in RQA measures in episodes close to death. These changes can be interpreted as more stability and determinism behavior of HRV in episodes close to death. RQA parameters can be used together with HRV parameters for description and prediction of mortality risk in ICU patients.

Słowa kluczowe

EN

mortality; intensive care unit; heart rate variability; linear analysis; nonlinear Poincaré analysis; recurrence quantification analysis

PL

śmiertelność; oddział intensywnej terapii; zmienność rytmu zatokowego; analiza liniowa; analiza ilościowa

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2015
• Tom: Vol. 35, no. 4
• Strony: 217--226
• Opis fizyczny: Bibliogr. 51 poz., tab., wykr.

Twórcy

autor

Moridani M. K.

• Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, No. 29, Floor 4, Farjam St., Tehran-Pars, Tehran 1653989618, Iran

autor

Setarehdan S. K.

• Control and Intelligent Processing Centre of Excellence, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran

autor

Nasrabadi A. M.

• Department of Biomedical Engineering, Shahed University, Tehran, Iran

autor

Hajinasrollah E.

• Loghman Medical Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran

Bibliografia

• [1] Marshall JC, Aarts MA. From Celsus to Galen to bone: the illnesses, syndromes, and diseases of acute inflammation. In: Vincent J-L, editor. Yearbook of intensive care and emergency medicine. Berlin, Germany: Springer; 2001. p. 3–12.
• [2] Kennedy H. Heart rate variability—a potential, noninvasive prognostic index in the critically ill patient. Crit Care Med 1998;26:213–4.
• [3] Seely AJ, Christou NV. Multiple organ dysfunction syndrome: exploring the paradigm of complex nonlinear systems. Crit Care Med 2000;28:2193–200.
• [4] Clermont G, Angus DC. Towards understanding pathophysiology in critical care: the human body as a complex system. In: Vincent J-L, editor. Yearbook of intensive care and emergency medicine. Berlin, Germany: Springer; 2004. p. 13–22.
• [5] Sprung CL, Cohen SL, Sjokvist P, Baras M, Bulow HH, Hovilehto S, et al. End-of-life practices in European intensive care units: the Ethicus study. JAMA 2003;290: 790–7.
• [6] Ferrand E, Robert R, Ingrand P, Lemaire F, French LATAREA Group. Withholding and withdrawal of life support in intensive-care units in France: a prospective survey. Lancet 2001;357:9–14.
• [7] Esteban A, Gordo F, Solsona JF, Alía I, Caballero J, Bouza C, et al. Withdrawing and withholding life support in the intensive care unit: a Spanish prospective multi-centre observational study. Intensive Care Med 2001;27:1744–9.
• [8] McLean RF, Tarshis J, Mazer CD, Szalai JP. Death in two Canadian intensive care units: institutional difference and changes over time. Crit Care Med 2000;28:100–3.
• [9] Wunsch H, Harrison DA, Harvey S, Rowan K. End-of-life decisions: a cohort study of the withdrawal of all active treatment in intensive care units in the United Kingdom. Intensive Care Med 2005;31:823–31.
• [10] Yazigi A, Riachi M, Dabbar G. Withholding and withdrawal of life sustaining treatment in a Lebanese intensive care unit: a prospective observational study. Intensive Care Med 2005;31:562–7.
• [11] Buckley TA, Joynt GM, Tan PY, Cheng CA, Yap FH. Limitation of life support: frequency and practice in a Hong Kong intensive care unit. Crit Care Med 2004;32:415–20.
• [12] Azoulay E, Metnitz B, Sprung CL, Timsit JF, Lemaire F, Bauer P, et al. SAPS 3 investigators: end-of-life practices in 282 intensive care units: data from the SAPS 3 database. Intensive Care Med 2009;35:623–30.
• [13] Sprung CL, Maia P, Bulow HH, Ricou B, Armaganidis A, Baras M, et al. The importance of religious affiliation and culture on end-of-life decisions in European intensive care units. Intensive Care Med 2007;33:1732–9.
• [14] Moselli NM, Debernardi F, Piovano F. Forgoing life sustaining treatments: differences and similarities between North America and Europe. Acta Anaesthesiol Scand 2006;50:1177–86.
• [15] Ramon J, Fierens D, Guiza F, Meyfroidt G, Blockeel H, Bruynooghe M, et al. Mining data from intensive care patients. Adv Eng Inform 2007;21:243–56.
• [16] Silva A, Cortez P, Santos MF, Gomes L, Neves J. Mortality assessment in intensive care units via adverse events using artificial neural networks. Artif Intell Med 2006;36:223–34.
• [17] Rosenberg AL. Recent innovations in intensive care unit risk-prediction models. Curr Opin Crit Care 2002;8:321–30.
• [18] Knaus WA. APACHE 1978–2001: the development of a quality assurance system based on prognosis: milestones and personal reflections. Arch Surg 2002;137:37–41.
• [19] Knaus WA, Wagner DP, Draper EA, Zimmerman JE, Bergner M, Bastos PG, et al. The APACHE III prognostic system. Risk prediction of hospital mortality for critically ill hospitalized adults. Chest 1991;100:1619–36.
• [20] Le Gall JR, Loirat P, Alperovitch A, Glaser P, Granthil C, Mathieu D, et al. A simplified acute physiology score for ICU patients. Crit Care Med 1984;12:975–7.
• [21] Le Gall JR, Lemeshow S, Saulnier F. A new Simplified Acute Physiology Score (SAPS II) based on a European/North American multicenter study. JAMA 1993;270:2957–63.
• [22] Morik K, Imhoff M, Brockhausen P, Joachims T, Gather U. Knowledge discovery and knowledge validation in intensive care. Artif Intell Med 2000;19:225–49.
• [23] Moser SA, Jones WT, Brossette SE. Application of data mining to intensive care unit microbiologic data. Emerg Infect Dis 1999;5:454–7.
• [24] Ganzert S, Guttmann J, Kersting K, Kuhlen R, Putensen C, Sydow M, et al. Analysis of respiratory pressure volume curves in intensive care medicine using inductive machine learning. Artif Intell Med 2002;26:69–86.
• [25] Lucas P. Bayesian analysis, pattern analysis, and data mining in health care. Curr Opin Crit Care 2004;10:399–403.
• [26] Kreke JE, Schaefer AJ, Roberts MS. Simulation and critical care modeling. Curr Opin Crit Care 2004;10:395–8.
• [27] Kong L, Milbrandt EB, Weissfeld LA. Advances in statistical methodology and their application in critical care. Curr Opin Crit Care 2004;10:391–4.
• [28] Sierra B, Serrano N, Larranaga P, Plasencia EJ, Inza I, Jimenez JJ, et al. Using Bayesian networks in the construction of a bi-level multi-classifier. A case study using intensive care unit patients data. Artif Intell Med 2001;22:233–48.
• [29] Garrard GS, Kontoyannis DA, Piepoli M. Spectral analysis of heart rate variability in sepsis syndrome. Clin Auton Res 1993;3:5–13.
• [30] Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. Standards of measurement, physiological interpretation and clinical use. Circulation 1996;93:1043–65.
• [31] Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. Standards of measurement, physiological interpretation and clinical use. Eur Heart J 1996;75:354–81.
• [32] Akselroad S, Gordon D, Madwed JB, Snidman NC, Shannon DC, Cohen RJ. Power spectrum analysis of heart rate fluctuation: a quantitative probe of beat-to-beat cardiovascular control. Science 1981;213:220–2.
• [33] Tsuji H, Venditti FJ, Manders ES, Evans JC, Larson MG, Feldman CL, et al. Reduced heart rate variability and mortality risk in an elderly cohort: the Framingham Heart Study. Circulation 1994;90:878–83.
• [34] Son Youn-Jung, Kim Hong-Gee, Kim Eung-Hee, Choi Sangsup, Lee Soo-Kyoung. Application of support vector machine for prediction of medication adherence in heart failure patients. Healthc Inform Res 2010;16(December (4)):253–9.
• [35] Hamadene W, Peyoride L, Seidiri H. Interpretation of RQA variables: Application to the prediction of epileptic seizures. Proceedings of 8th International Conference on Signal Processing in Beijing, vol. 4; 2006. p. 16–20.
• [36] http://www.uptodate.com.
• [37] Xing Quansheng, Wu Qin. Non-sinus rhythm after heart surgery: permanent or not? A simple method can tell. Asian Cardiovasc Thoracic Ann 2012;20(4):495–6.
• [38] Feng WEN, Fang-tian HE. An efficient method of addressing ectopic beats: new insight into data preprocessing of heart rate variability. J Zhejiang Univ-Sci B (Biomed Biotechnol) 2011;12(12):976–82.
• [39] MIMIC II Database. http://physionet.org/physiobank/database/mimic2db/.
• [40] Mazzeo AT, La Monaca E, Di Leo R, Vita G, Santamaria LB. Heart rate variability: a diagnostic and prognostic tool in anesthesia and intensive care. Acta Anaesthesiol Scand 2011;55:797–811.
• [41] Brennan M, Palaniswami M, Kamen P. Do existing measures of poincare plot geometry reflect nonlinear features of heart rate variability. IEEE Trans Biomed Eng 2001;48:1342–7.
• [42] Marwan N. Encounters with neighbours: current developments of concepts based on recurrence plots and their applications, Potsdam, 2003. 331 s Dizertačni prace. University of Potsdam, 2003. Dostupne z http://www. recurrenceplot.tk/furtherreading.php. ISBN 3000123474, 9783000123474.
• [43] Kennel MB, Brown R, Abarbanel HD. Determining embedding dimension for phase-space reconstruction using a geometrical construction. Phys Rev A 1992;45: 3403–11.
• [44] Fraser AM, Swinney HL. Independent coordinates for strange attractors from mutual information. Phys Rev A 1986;33:1134–40.
• [45] Zbilut JP, Webber CL. Embeddings and delays as derived from quantification of recurrence plots. Phys Lett A 1992;171:199–203.
• [46] Marwan N, Romano MC, Thiel M, Kurths J. Recurrence plots for the analysis of complex systems. Phys Rep 2007;438:237–329.
• [47] Matassini L, Kantz H, Hołyst J, Hegger R. Optimizing of recurrence plots for noise reduction. Phys Rev E Stat Nonlinear Soft Matter Phys 2002;65:021102.
• [48] Thiel M, Romano MC, Kurths J, Meucci R, Allaria E, Arecchi FT. Influence of observational noise on the recurrence quantification analysis. Physica D 2002;171: 138–512.
• [49] Eckmann JP, Kamphorst SO, Ruelle D. Recurrence plots of dynamical systems. Europhys Lett 1987;4:973–7.
• [50] Tsuji H, Venditti Jr FJ, Manders ES, Evans JC, Larson MG, Feldman CL, et al. Reduced heart rate variability and mortality risk in an elderly cohort: the Framingham Heart Study. Circulation 1994;90:878–83.
• [51] Jacqueline M Dekker, Richard S Crow, Aaron R Folsom, Peter J Hannan, Duanping Liao, Cees A Swenne, et al. Low heart rate variability in a 2-minute rhythm strip predicts risk of coronary heart disease and mortality from several causes. Circulation 2000;102:1239–44.