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Computational Modeling of Pathophysiologic Responses to Exercise in Fontan Patients

Overview of attention for article published in Annals of Biomedical Engineering, September 2014
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Title
Computational Modeling of Pathophysiologic Responses to Exercise in Fontan Patients
Published in
Annals of Biomedical Engineering, September 2014
DOI 10.1007/s10439-014-1131-4
Pubmed ID
Authors

Ethan Kung, James C. Perry, Christopher Davis, Francesco Migliavacca, Giancarlo Pennati, Alessandro Giardini, Tain-Yen Hsia, Alison Marsden

Abstract

Reduced exercise capacity is nearly universal among Fontan patients. Although many factors have emerged as possible contributors, the degree to which each impacts the overall hemodynamics is largely unknown. Computational modeling provides a means to test hypotheses of causes of exercise intolerance via precisely controlled virtual experiments and measurements. We quantified the physiological impacts of commonly encountered, clinically relevant dysfunctions introduced to the exercising Fontan system via a previously developed lumped-parameter model of Fontan exercise. Elevated pulmonary arterial pressure was observed in all cases of dysfunction, correlated with lowered cardiac output (CO), and often mediated by elevated atrial pressure. Pulmonary vascular resistance was not the most significant factor affecting exercise performance as measured by CO. In the absence of other dysfunctions, atrioventricular valve insufficiency alone had significant physiological impact, especially under exercise demands. The impact of isolated dysfunctions can be linearly summed to approximate the combined impact of several dysfunctions occurring in the same system. A single dominant cause of exercise intolerance was not identified, though several hypothesized dysfunctions each led to variable decreases in performance. Computational predictions of performance improvement associated with various interventions should be weighed against procedural risks and potential complications, contributing to improvements in routine patient management protocol.

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The data shown below were compiled from readership statistics for 53 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 2%
United States 1 2%
Unknown 51 96%

Demographic breakdown

Readers by professional status Count As %
Student > Master 9 17%
Researcher 8 15%
Student > Ph. D. Student 8 15%
Other 3 6%
Student > Doctoral Student 2 4%
Other 8 15%
Unknown 15 28%
Readers by discipline Count As %
Engineering 19 36%
Medicine and Dentistry 7 13%
Nursing and Health Professions 3 6%
Unspecified 3 6%
Mathematics 2 4%
Other 6 11%
Unknown 13 25%