Deep phenotyping of cardiac function in heart transplant patients using\n cardiovascular systems models Article Swipe
Amanda L. Colunga
,
K. Kim
,
N. Payton Woodall
,
Todd Dardas
,
John H. Gennari
,
Mette S. Olufsen
,
Brian E. Carlson
·
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1812.11857
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1812.11857
Heart transplant patients are followed with periodic right heart\ncatheterizations (RHCs) to identify post-transplant complications and guide\ntreatment. Post-transplant positive outcomes are associated with a steady\nreduction of right ventricular and pulmonary arterial pressures, toward normal\nlevels of right-side pressure (about 20mmHg) measured by RHC. This study shows\nmore information about patient progression is obtained by combining standard\nRHC measures with mechanistic computational cardiovascular systems models. This\nstudy shows: to understand how cardiovascular system models can be used to\nrepresent a patient's cardiovascular state, and to use these models to track\npost-transplant recovery and outcome. To obtain reliable parameter estimates\ncomparable within and across datasets, we use sensitivity analysis, parameter\nsubset selection, and optimization to determine patient specific mechanistic\nparameter that can be reliably extracted from the RHC data. Patient-specific\nmodels are identified for ten patients from their first post-transplant RHC and\nlongitudinal analysis is done for five patients. Results of sensitivity\nanalysis and subset selection show we can reliably estimate seven\nnon-measurable quantities including ventricular diastolic relaxation, systemic\nresistance, pulmonary venous elastance, pulmonary resistance, pulmonary\narterial elastance, pulmonary valve resistance and systemic arterial elastance.\nChanges in parameters and predicted cardiovascular function post-transplant are\nused to evaluate cardiovascular state during recovery in five patients. Of\nthese five patients, only one patient showed inconsistent trends during\nrecovery in ventricular pressure-volume relationships and power output. At a\nfour-year recovery time point this patient exhibited biventricular failure\nalong with graft dysfunction while the remaining four exhibited no\ncardiovascular complications.\n
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Metadata
- Type
- preprint
- Landing Page
- http://arxiv.org/abs/1812.11857
- https://arxiv.org/pdf/1812.11857
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4327673962
All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W4327673962Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.1812.11857Digital Object Identifier
- Title
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Deep phenotyping of cardiac function in heart transplant patients using\n cardiovascular systems modelsWork title
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preprintOpenAlex work type
- Publication year
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2018Year of publication
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2018-12-26Full publication date if available
- Authors
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Amanda L. Colunga, K. Kim, N. Payton Woodall, Todd Dardas, John H. Gennari, Mette S. Olufsen, Brian E. CarlsonList of authors in order
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https://arxiv.org/abs/1812.11857Publisher landing page
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https://arxiv.org/pdf/1812.11857Direct link to full text PDF
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/1812.11857Direct OA link when available
- Concepts
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Elastance, Medicine, Cardiology, Heart transplantation, Internal medicine, Pulmonary hypertension, Heart failure, Diastole, Vascular resistance, Blood pressure, Pulmonary function testing, Transplantation, LungTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.understand | 63 |
| abstract_inverted_index.This\nstudy | 60 |
| abstract_inverted_index.dysfunction | 213 |
| abstract_inverted_index.information | 44 |
| abstract_inverted_index.mechanistic | 55 |
| abstract_inverted_index.progression | 47 |
| abstract_inverted_index.relaxation, | 151 |
| abstract_inverted_index.resistance, | 157 |
| abstract_inverted_index.sensitivity | 97 |
| abstract_inverted_index.shows\nmore | 43 |
| abstract_inverted_index.ventricular | 26, 149, 195 |
| abstract_inverted_index.a\nfour-year | 202 |
| abstract_inverted_index.inconsistent | 191 |
| abstract_inverted_index.optimization | 102 |
| abstract_inverted_index.biventricular | 209 |
| abstract_inverted_index.complications | 13 |
| abstract_inverted_index.computational | 56 |
| abstract_inverted_index.relationships | 197 |
| abstract_inverted_index.standard\nRHC | 52 |
| abstract_inverted_index.to\nrepresent | 71 |
| abstract_inverted_index.cardiovascular | 57, 65, 74, 171, 177 |
| abstract_inverted_index.failure\nalong | 210 |
| abstract_inverted_index.normal\nlevels | 32 |
| abstract_inverted_index.Post-transplant | 16 |
| abstract_inverted_index.post-transplant | 12, 126, 173 |
| abstract_inverted_index.pressure-volume | 196 |
| abstract_inverted_index.complications.\n | 220 |
| abstract_inverted_index.during\nrecovery | 193 |
| abstract_inverted_index.and\nlongitudinal | 128 |
| abstract_inverted_index.guide\ntreatment. | 15 |
| abstract_inverted_index.parameter\nsubset | 99 |
| abstract_inverted_index.steady\nreduction | 23 |
| abstract_inverted_index.no\ncardiovascular | 219 |
| abstract_inverted_index.elastance.\nChanges | 166 |
| abstract_inverted_index.pulmonary\narterial | 158 |
| abstract_inverted_index.estimates\ncomparable | 90 |
| abstract_inverted_index.sensitivity\nanalysis | 137 |
| abstract_inverted_index.seven\nnon-measurable | 146 |
| abstract_inverted_index.systemic\nresistance, | 152 |
| abstract_inverted_index.mechanistic\nparameter | 107 |
| abstract_inverted_index.track\npost-transplant | 82 |
| abstract_inverted_index.heart\ncatheterizations | 8 |
| abstract_inverted_index.Patient-specific\nmodels | 117 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 7 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.8100000023841858 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile.value | 0.45221737 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |