Modelling and control of a failing heart managed by a left ventricular assist device

• Autorzy: Son Jeongeun , Du Dongping , Du Yuncheng

Identyfikatory

DOI

10.1016/j.bbe.2020.01.014

• Języki publikacji: EN

Abstrakty

EN

Left ventricular assist device (LVAD) recently has been used in advanced heart failure (HF), which supports a failing heart to meet blood circulation demand of the body. However, the pumping power of LVADs is typically set as a constant and cannot be freely adjusted to incorporate blood need from resting or mild exercise such as walking stairs. To promote the adoption of LVADs in clinical use as a long-term treatment option, a feedback controller is needed to regulate automatically the pumping power to support a time-varying blood demand, according to different physical activities. However, the tuning of pumping power induces suction, which will collapse the heart and cause sudden death. It is essential to consider suction when developing control strategy to adjust the pumping power. Further, hemodynamic of a failing heart exhibits variability, due to patient-to-patient heterogeneity and inherent stochastic nature of the heart. Such variability poses challenges for controller design. In this work, we develop a feedback controller to adjust the pumping power of an LVAD without inducing suction, while incorporating variability in hemodynamic. To efficiently quantify variability, the generalized polynomial chaos (gPC) theory is used to design a robust self-tuning controller. The efficiency of our control algorithm is illustrated with three case scenarios, each representing a specific change in physical activity of HF patients.

Słowa kluczowe

EN

self-tuning controller; cardiovascular modelling; left ventricular assist device control; uncertainty quantification

PL

regulator samonastrajalny; układ sercowo-naczyniowy; urządzenie wspomagania lewej komory; kwantyfikacja niepewności

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2020
• Tom: Vol. 40, no. 1
• Strony: 559--573
• Opis fizyczny: Bibliogr. 48 poz., rys., tab., wykr.

Twórcy

autor

Son Jeongeun

• Department of Chemical & Biomolecular Engineering, Clarkson University, Potsdam, NY, USA

autor

Du Dongping

• Department of Industrial, Manufacturing & Systems Engineering, Texas Tech University, Lubbock, TX, USA

autor

Du Yuncheng

• Department of Chemical & Biomolecular Engineering, Clarkson University, Potsdam, NY, USA

Bibliografia

• [1] Mancini D, Colombo PC. Left ventricular assist devices: a rapidly evolving alternative to transplant. J Am Coll Cardiol 2015;65(23):2542–55.
• [2] Simaan MA, Ferreira A, Chen S, Antaki JF, Galati DG. A dynamical state space representation and performance analysis of a feedback-controlled rotary left ventricular assist device. IEEE Trans Control Syst Technol 2009;17(1):15–28.
• [3] Shi Y, Korakianitis T, Bowles C. Numerical simulation of cardiovascular dynamics with different types of VAD assistance. J Biomech 2007;40(13):2919–33.
• [4] Wu Y. Design and testing of a physiologic control system for an artificial heart pump. [Ph.D. dissertation] Mech. Aerosp. Eng.. University of Virginia; 2004.
• [5] Slaughter MS, Pagani FD, Rogers JG, Miller LW, Sun B, Russell SD, et al. Clinical management of continuous-flow left ventricular assist devices in advanced heart failure. J Heart Lung Transplant 2010;29(4):S1–39.
• [6] Wang Y, Koenig SC, Slaughter MS, Giridharan GA. Rotary blood pump control strategy for preventing left ventricular suction. ASAIO J 2015;61(1):21–30.
• [7] Ohuchi K, Kikugawa D, Takahashi K, Uemura M, Nakamura M, Murakami T, et al. Control strategy for rotary blood pumps. Artif Organs 2001;25(5):366–70.
• [8] Petrou A, Kanakis M, Boës S, Pergantis P, Meboldt M, Daners MS. Viscosity prediction in a physiologically controlled ventricular assist device. IEEE Trans Biomed Eng 2018;65 (10):2355–64.
• [9] Choi S, Antaki JE, Boston R, Thomas D. A sensorless approach to control of a turbodynamic left ventricular assist system. IEEE Trans Control Syst Technol 2001;9 (3):473–82.
• [10] Choi S, Boston JR, Antaki JF. Hemodynamic controller for left ventricular assist device based on pulsatility ratio. Artif Organs 2007;31(2):114–25.
• [11] Fu M, Xu L. Computer simulation of sensorless fuzzy control of a rotary blood pump to assure normal physiology. ASAIO J 2000;46(3):273–8.
• [12] Martina J, de Jonge N, Rutten M, Kirkels JH, Klöpping C, Rodermans B, et al. Exercise hemodynamics during extended continuous flow left ventricular assist device support: the response of systemic cardiovascular parameters and pump performance. Artif Organs 2013;37(9):754–62.
• [13] AlOmari AH, Savkin AV, Stevens M, Mason DG, Timms DL, Salamonsen RF, et al. Developments in control systems for rotary left ventricular assist devices for heart failure patients: a review. Physiol Meas 2012;34(1):R1–27.
• [14] Ferreira A. A rule-based controller based on suction detection for rotary blood pumps. [Ph.D. dissertation] Dept. Elect. Comput. Eng.. University of Pittsburgh; 2007.
• [15] Stevens MC, Wilson S, Bradley A, Fraser J, Timms D. Physiological control of dual rotary pumps as a biventricular assist device using a master/slave approach. Artif Organs 2014;38(9):766–74.
• [16] Son J, Du D, Du Y. Stochastic modeling and control of circulatory system with a left ventricular assist device. 2019 American Control Conference (ACC). IEEE; 2019. p. 5408–13.
• [17] Eck VG, Feinberg J, Langtangen HP, Hellevik LR. Stochastic sensitivity analysis for timing and amplitude of pressure waves in the arterial system. Int J Numer Meth Biomed Eng 2015;31(4):e02711.
• [18] Huberts W, Donders WP, Delhaas T, van de Vosse FN. Applicability of the polynomial chaos expansion method for personalization of a cardiovascular pulse wave propagation model. Int J Numer Meth Biomed Eng 2014;30 (12):1679–704.
• [19] Quicken S, Donders WP, van Disseldorp EMJ, Gashi K, Mees BME, van de Vosse FN, et al. Application of an adaptive polynomial chaos expansion on computationally expensive three-dimensional cardiovascular models for uncertainty quantification and sensitivity analysis. J Biomech Eng 2016;138(12):121010.
• [20] Hu Z, Du D, Du Y. Generalized polynomial chaos-based uncertainty quantification and propagation in multi-scale modeling of cardiac electrophysiology. Comput Biol Med 2018;102:57–74.
• [21] Du Y, Budman H, Duever TA. Comparison of stochastic fault detection and classification algorithms for nonlinear chemical processes. Comput Chem Eng 2017;106:57–70.
• [22] Xiu D. Numerical methods for stochastic computations: a spectral method approach. Princeton, NJ, USA: Princeton University Press; 2010.
• [23] Avanzolini G, Barbini P, Cappello A, Cevenini G. CADCS simulation of the closed-loop cardiovascular system. Int J Biomed Comput 1988;22(1):39–49.
• [24] Fernandez de Canete J, Del Saz-Orozco P, Moreno-Boza D, Duran-Venegas E. Object-oriented modeling and simulation of the closed loop cardiovascular system by using SIMSCAPE. Comput Biol Med 2013;43(4):323–33.
• [25] Shi Y, Korakianitis T. Numerical simulation of cardiovascular dynamics with left heart failure and in-series pulsatile ventricular assist device. Artif Organs 2006;30(12):929–48.
• [26] Korakianitis T, Shi Y. A concentrated parameter model for the human cardiovascular system including heart valve dynamics and atrioventricular interaction. Med Eng Phys 2006;28(7):613–28.
• [27] Korakianitis T, Shi Y. Numerical simulation of cardiovascular dynamics with healthy and diseased heart valves. J Biomech 2006;39(11):1964–82.
• [28] Suga H, Sagawa K, Shoukas AA. Load independence of the instantaneous pressure-volume ratio of the canine left ventricle and effects of epinephrine and heart rate on the ratio. Circ Res 1973;32(3):314–22.
• [29] Suga H, Sagawa K. Instantaneous pressure-volume relationships and their ratio in the excised, supported canine left ventricle. Circ Res 1974;35(1):117–26.
• [30] Vandenberghe S, Segers P, Steendijk P, Meyns B, Dion RAE, Antaki JF, et al. Modeling ventricular function during cardiac assist: does time-varying elastance work? ASAIO J 2006;52(1):4–8.
• [31] Stergiopulos N, Meister JJ, Westerhof N. Determinants of stroke volume and systolic and diastolic aortic pressure. Amer J Physiol 1996;270(6):H2050–9.
• [32] Choi S, Boston JR, Thomas D, Antaki JF. Modeling and identification of an axial flow blood pump. Proceedings of the 1997 American Control Conference (Cat. No.97CH36041). IEEE; 1997. p. 3714–5.
• [33] Xiu D, Karniadakis GE. The Wiener–Askey polynomial chaos for stochastic differential equations. SIAM J Sci Comput 2002;24(2):619–44.
• [34] Moazami N, Fukamachi K, Kobayashi M, Smedira NG, Hoercher KJ, Massiello A, et al. Axial and centrifugal continuous-flow rotary pumps: a translation from pump mechanics to clinical practice. J Heart Lung Transplant 2013;32(1):1–11.
• [35] Schwarz KQ, Parikh SS, Chen X, Farrar DJ, Steinmetz S, Ramamurthi S, et al. Non-invasive flow measurement of a rotary pump ventricular assist device using quantitative contrast echocardiography. J Am Soc Echocardiogr 2010;23 (3):324–9.
• [36] Trinquero P, Pirotte A, Gallagher LP, Iwaki KM, Beach C, Wilcox JE. Left ventricular assist device management in the emergency department. West J Emerg Med 2018;19 (5):834–41.
• [37] Ferreira A, Boston JR, Antaki JF. A control system for rotary blood pumps based on suction detection. IEEE Trans Biomed Eng 2009;56(3):656–65.
• [38] Gwak KW. Application of extremum seeking control to turbodynamic blood pumps. ASAIO J 2007;53(4):403–9.
• [39] Konishi H, Antaki JF, Amin DV, Boston JR, Kerrigan JP, Mandarino WA, et al. Controller for an axial flow blood pump. Artif Organs 1996;20(6):618–20.
• [40] Shi Y, Brown AG, Lawford PV, Arndt A, Nuesser P, Hose DR. Computational modelling and evaluation of cardiovascular response under pulsatile impeller pump support. Interface Focus 2011;1(3):320–37.
• [41] Bartoli CR, Dowling RD. The future of adult cardiac assist devices: novel systems and mechanical circulatory support strategies. Cardiol Clin 2011;29(4):559–82.
• [42] Son J, Du Y. Model-based stochastic fault detection and diagnosis of lithium-ion batteries. Processes 2019;7(1):38.
• [43] Du Y, Duever TA, Budman H. Fault detection and diagnosis with parametric uncertainty using generalized polynomial chaos. Comput Chem Eng 2015;76:63–75.
• [44] Saini V, Samra T. Persistent postoperative hypercyanotic spells in an adult with surgically untreated tetralogy of fallot: use of ketamine infusion. J Anaesthesiol Clin Pharmacol 2017;33(3):412–3.
• [45] Melo J, Peters JI. Low systemic vascular resistance: differential diagnosis and outcome. Crit Care 1999;3(3):71–7.
• [46] Jorde UP, Uriel N, Nahumi N, Bejar D, Gonzalez-Costello J, Thomas SS, et al. Prevalence, significance, and management of aortic insufficiency in continuous flow left ventricular assist device recipients. Circ Heart Fail 2014;7 (2):310–9.
• [47] Reddy YNV, Melenovsky V, Redfield MM, Nishimura RA, Borlaug BA. High-output heart failure: a 15-year experience. J Am Coll Cardiol 2016;68(5):473–82.
• [48] Wong M, Toh L, Wilson A, Rowley K, Karschimkus C, Prior D, et al. Reduced arterial elasticity in rheumatoid arthritis and the relationship to vascular disease risk factors and inflammation. Arthritis Rheum 2003;48(1):81–9.

Uwagi

EN

This paper was partially presented at the 2019 American Control Conference (ACC), Philadelphia, PA, USA, July 10–12, 2019.

PL

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).