| Title |
Cardiovascular mechanics in the early stages of pulmonary hypertension: a computational study
|
|---|---|
| Published in |
Biomechanics and Modeling in Mechanobiology, July 2017
|
| DOI | 10.1007/s10237-017-0940-4 |
| Pubmed ID | |
| Authors |
Sebastián Acosta, Charles Puelz, Béatrice Rivière, Daniel J. Penny, Ken M. Brady, Craig G. Rusin |
| Abstract |
We formulate and study a new mathematical model of pulmonary hypertension. Based on principles of fluid and elastic dynamics, we introduce a model that quantifies the stiffening of pulmonary vasculature (arteries and arterioles) to reproduce the hemodynamics of the pulmonary system, including physiologically consistent dependence between compliance and resistance. This pulmonary model is embedded in a closed-loop network of the major vessels in the body, approximated as one-dimensional elastic tubes, and zero-dimensional models for the heart and other organs. Increasingly severe pulmonary hypertension is modeled in the context of two extreme scenarios: (1) no cardiac compensation and (2) compensation to achieve constant cardiac output. Simulations from the computational model are used to estimate cardiac workload, as well as pressure and flow traces at several locations. We also quantify the sensitivity of several diagnostic indicators to the progression of pulmonary arterial stiffening. Simulation results indicate that pulmonary pulse pressure, pulmonary vascular compliance, pulmonary RC time, luminal distensibility of the pulmonary artery, and pulmonary vascular impedance are much better suited to detect the early stages of pulmonary hypertension than mean pulmonary arterial pressure and pulmonary vascular resistance, which are conventionally employed as diagnostic indicators for this disease. |
X Demographics
The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 67% |
| Unknown | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 47 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 47 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 8 | 17% |
| Researcher | 7 | 15% |
| Other | 4 | 9% |
| Student > Master | 4 | 9% |
| Student > Bachelor | 3 | 6% |
| Other | 10 | 21% |
| Unknown | 11 | 23% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 16 | 34% |
| Medicine and Dentistry | 8 | 17% |
| Nursing and Health Professions | 2 | 4% |
| Agricultural and Biological Sciences | 2 | 4% |
| Mathematics | 2 | 4% |
| Other | 4 | 9% |
| Unknown | 13 | 28% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 19 June 2018.
All research outputs
#14,615,513
of 23,849,058 outputs
Outputs from Biomechanics and Modeling in Mechanobiology
#196
of 486 outputs
Outputs of similar age
#171,202
of 316,146 outputs
Outputs of similar age from Biomechanics and Modeling in Mechanobiology
#9
of 12 outputs
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So far Altmetric has tracked 486 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.2. This one is in the 38th percentile – i.e., 38% of its peers scored the same or lower than it.
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