Leveraging device-arterial coupling to determine cardiac and vascular state
DOI:
https://doi.org/10.61841/0bfwxg35Keywords:
Cardiac output, vascular resistance, mechanical circulatory support, cardiogenic shock, vascularcouplingAbstract
In this paper, limitations in available diagnostic metrics restrict the efficacy of managing therapies for cardiogenic shock. Cardiovascular state is inferred through measurement of pulmonary capillary wedge pressure and reliance on linear approximations between pressure and flow to estimate peripheral vascular resistance. Mechanical circulatory support devices residing within the left ventricle and aorta provide an opportunity for both determining cardiac and vascular state and offering therapeutic benefit. We leverage the controllable mode of operation and transvalvular position of an indwelling percutaneous ventricular assist device to assess vascular and, in turn, cardiac state through the effects of device-arterial coupling across different levels of device support. Methods: Vascular state is determined by measuring changes in the pressure waveforms induced through intentional variation in the device-generated blood flow. We evaluate this impact by applying a lumped parameter model to quantify state-specific vascular resistance and compliance and calculate beat-to-beat stroke volume and cardiac output in both animal models and retrospective patient data without external calibration.Result
Vascular state was accurately predicted in patients and animals in both baseline and experimental conditions. In the animal, stroke volume was predicted within a total RMS error of 3.71 mL (n = 482). Conclusion: We demonstrate that device-arterial coupling is a powerful tool for evaluating patients and state specific parameters of cardiovascular function. Significance: These insights may yield improved clinical care and support the development of next-generation mechanical circulatory support devices that determine and operate in tandem with the supported organ.
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References
1. Subha PP, Pillai Geethakumari SM, Athira M, Nujum ZT, Pattern and risk factors of stroke in the young
among stroke patients admitted in medical college hospital, Thiruvananthapuram., Ann indian Acad
Neurol 2015;18:20-3.
2. Barry L. Zaret, M.D., Marvin Moser, M.D., Lawrence S. Cohen, Chapter 18 Stroke—Lawrence M. Brass,
M.D.
3. MacMahon S., Rodgers A. The epidemiological association between blood pressure and stroke:
implications for primary and secondary prevention. Hypertens Res. 1994;17(suppl 1):S23-S32.
4. Shinton R, Beevers G. Meta-analysis of the relation between cigarette smoking and stroke. BMJ
5. Benjamin EJ, Levy D, Vaziri SM, D’Agostino RB, Belanger AJ, Wolf
6. PA. Independent risk factors for atrial fibrillation in population-based cohort: the Framingham
HeartStudy. JAMA. 1994;271:840-844
7. Saangyong Uhmn, Dong-Hoi Kim, Jin Kim, Sung Won Cho, Jae YounCheong, "Chronic Hepatitis
Classification Using SNP Data and
8. Mining Techniques", Frontiers in the Convergence of Bioscience and
9. Information Technologies, FBIT 2007, pp. 81-86, 11-13 Oct. 2007B.
10. Smith, “An approach to graphs of linear forms (unpublished workstyle),” unpublished.
11. S. Bhatia, P. Prakash and G.N. Pillai, SVM-based Decision SupportSystem for Heart Disease
Classification with Integer-coded Genetic
12. Algorithm to select critical features, Proceedings of the WorldCongress on Engineering and Computer
Science, San Francisco, USA, pp. 34-38, 2008.
13. Yanwei Xing, Jie Wang and Zhihong Zhao Yonghong Gao, 2007: Combination data mining methods with
new medical data to predict outcome of Coronary Heart Disease” ConvergenceInformation
Technology, 2007. International Conference November 2007, pp. 868–872.
14. Jianxin Chen, Guangcheng Xi, Yanwei Xing, Jing Chen, and Jie Wang (2007), “Predicting Syndrome by
NEI Specifications: A Comparison ofFive Data Mining Algorithms in Coronary Heart Disease” Life
SystemModeling and Simulation Lecture Notes in Computer Science, pp. 129-135
15. Alexopoulos, E., Dounias, G.D., and Vemmos, K. (1999). "Medicaldiagnosis of stroke using inductive
machine learning". In Proceedingsof Workshop on Machine Learning in Medical Applications,
AdvanceCourse in Artificial Intelligence-ACAI99, Chania, Greece, 20-23.
16. C. Cortes and V. Vapnik, “Support-vector networks,” Machinelearning, vol. 20, no. 3, pp. 273–297, 1995.
17. Sandercock, Peter; Niewada, Maciej; Czlonkowska, Anna. (2011).
18. International Stroke Trial database (version 2), University of Edinburgh. Department of Clinical Neurosciences.
19. Jeena R S, Dr Sukesh Kumar A, ’Stroke Prediction using SVM , Proceedings of the International Conf. on Control, Instrumentation, Communication and Computational Technologies (ICCICCT-2016), Tamil Nadu
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