The Number of Microbubbles Generated During Cardiopulmonary Bypass Can Be Estimated Using Machine Learning From Suction Flow Rate, Venous Reservoir Level, Perfusion Flow Rate, Hematocrit Level, and Blood Temperature

Overview

Journal IEEE Open J Eng Med Biol

Publisher IEEE

Specialty Biomedical Engineering

Date 2024 Mar 15

PMID 38487096

Authors

Satoshi Miyamoto

Zu Soh

Shigeyuki Okahara

Akira Furui

Taiichi Takasaki

Keijiro Katayama

Shinya Takahashi

Toshio Tsuji

Affiliations

Soon will be listed here.

Abstract

Goal: Microbubbles (MBs) are known to occur within the circuits of cardiopulmonary bypass (CPB) systems, and higher-order dysfunction after cardiac surgery may be caused by MBs as well as atheroma dispersal associated with cannula insertion. As complete MB elimination is not possible, monitoring MB count rates is critical. We propose an online detection system with a neural network-based model to estimate MB count rate using five parameters: suction flow rate, venous reservoir level, perfusion flow rate, hematocrit level, and blood temperature.

Methods: Perfusion experiments were performed using an actual CPB circuit, and MB count rates were measured using the five varying parameters.

Results: Bland-Altman analysis indicated a high estimation accuracy ( > 0.95, < 0.001) with no significant systematic error. In clinical practice, although the inclusion of clinical procedures slightly decreased the estimation accuracy, a high coefficient of determination for 30 clinical cases ( = 0.8576) was achieved between measured and estimated MB count rates.

Conclusions: Our results highlight the potential of this system to improve patient outcomes and reduce MB-associated complication risk.

References

Doganci S, Gunaydin S, Kocak O, Yilmaz S, Demirkilic U . Impact of the intensity of microemboli on neurocognitive outcome following cardiopulmonary bypass. Perfusion. 2013; 28(3):256-62. DOI: 10.1177/0267659112470693. View

Kaza A, Cope J, Fiser S, Long S, Kern J, Kron I . Elimination of fat microemboli during cardiopulmonary bypass. Ann Thorac Surg. 2003; 75(2):555-9; discussion 559. DOI: 10.1016/s0003-4975(02)04540-x. View

Dinh D, Lee G, Billah B, Smith J, Shardey G, Reid C . Trends in coronary artery bypass graft surgery in Victoria, 2001-2006: findings from the Australasian Society of Cardiac and Thoracic Surgeons database project. Med J Aust. 2008; 188(4):214-7. DOI: 10.5694/j.1326-5377.2008.tb01587.x. View

Abu-Omar Y, Balacumaraswami L, Pigott D, Matthews P, Taggart D . Solid and gaseous cerebral microembolization during off-pump, on-pump, and open cardiac surgery procedures. J Thorac Cardiovasc Surg. 2004; 127(6):1759-65. DOI: 10.1016/j.jtcvs.2003.09.048. View

Miyamoto S, Soh Z, Okahara S, Furui A, Takasaki T, Katayama K . Neural network-based modeling of the number of microbubbles generated with four circulation factors in cardiopulmonary bypass. Sci Rep. 2021; 11(1):549. PMC: 7804121. DOI: 10.1038/s41598-020-80810-3. View

Segers T, Stehouwer M, De Somer F, de Mol B, Versluis M . Optical verification and in-vitro characterization of two commercially available acoustic bubble counters for cardiopulmonary bypass systems. Perfusion. 2017; 33(1):16-24. PMC: 5734373. DOI: 10.1177/0267659117722595. View

Hammon Jr J, Stump D, Kon N, CORDELL A, HUDSPETH A, Oaks T . Risk factors and solutions for the development of neurobehavioral changes after coronary artery bypass grafting. Ann Thorac Surg. 1997; 63(6):1613-8. DOI: 10.1016/s0003-4975(97)00261-0. View

Newman M, Mathew J, Grocott H, Mackensen G, Monk T, Welsh-Bohmer K . Central nervous system injury associated with cardiac surgery. Lancet. 2006; 368(9536):694-703. DOI: 10.1016/S0140-6736(06)69254-4. View

Groom R, Quinn R, Lennon P, Donegan D, Braxton J, Kramer R . Detection and elimination of microemboli related to cardiopulmonary bypass. Circ Cardiovasc Qual Outcomes. 2009; 2(3):191-8. DOI: 10.1161/CIRCOUTCOMES.108.803163. View

10.

Rubens F, Boodhwani M, Mesana T, Wozny D, Wells G, Nathan H . The cardiotomy trial: a randomized, double-blind study to assess the effect of processing of shed blood during cardiopulmonary bypass on transfusion and neurocognitive function. Circulation. 2007; 116(11 Suppl):I89-97. DOI: 10.1161/CIRCULATIONAHA.106.678987. View

11.

Altmann A, Tolosi L, Sander O, Lengauer T . Permutation importance: a corrected feature importance measure. Bioinformatics. 2010; 26(10):1340-7. DOI: 10.1093/bioinformatics/btq134. View

12.

Chang H, Weber M, Thomson J, Martin R . Hydrodynamic features of pulmonary air embolism: a model study. J Appl Physiol Respir Environ Exerc Physiol. 1981; 51(4):1002-8. DOI: 10.1152/jappl.1981.51.4.1002. View

13.

Wagner S, Rode C, Wojke R, Canaud B . Observation of microbubbles during standard dialysis treatments. Clin Kidney J. 2015; 8(4):400-4. PMC: 4515906. DOI: 10.1093/ckj/sfv051. View

14.

Okahara S, Tsuji T, Ninomiya S, Miyamoto S, Takahashi H, Soh Z . Hydrodynamic characteristics of a membrane oxygenator: modeling of pressure-flow characteristics and their influence on apparent viscosity. Perfusion. 2014; 30(6):478-83. DOI: 10.1177/0267659114562101. View

15.

Souders J, Doshier J, Polissar N, Hlastala M . Spatial distribution of venous gas emboli in the lungs. J Appl Physiol (1985). 1999; 87(5):1937-47. DOI: 10.1152/jappl.1999.87.5.1937. View

16.

Stride E, Saffari N . Microbubble ultrasound contrast agents: a review. Proc Inst Mech Eng H. 2004; 217(6):429-47. DOI: 10.1243/09544110360729072. View

17.

Bronson S, Riley J, Blessing J, Ereth M, Dearani J . Prescriptive patient extracorporeal circuit and oxygenator sizing reduces hemodilution and allogeneic blood product transfusion during adult cardiac surgery. J Extra Corpor Technol. 2013; 45(3):167-72. PMC: 4557535. View

18.

Myers G, Voorhees C, Haynes R, Eke B . Post-arterial filter gaseous microemboli activity of five integral cardiotomy reservoirs during venting: an in vitro study. J Extra Corpor Technol. 2009; 41(1):20-7. PMC: 4680219. View

19.

Bird J, de Ruiter R, Courbin L, Stone H . Daughter bubble cascades produced by folding of ruptured thin films. Nature. 2010; 465(7299):759-62. DOI: 10.1038/nature09069. View

20.

Miyamoto S, Soh Z, Okahara S, Furui A, Takasaki T, Katayama K . Neural Network-based Estimation of Microbubbles Generated in Cardiopulmonary Bypass Circuit: A Clinical Application Study. Annu Int Conf IEEE Eng Med Biol Soc. 2022; 2022:617-620. DOI: 10.1109/EMBC48229.2022.9871662. View