Can time-averaged flow boundary conditions be used to meet the clinical timeline for Fontan surgical planning?
Abstract Cardiovascular simulations have great potential as a clinical tool for planning and evaluating patient-specific treatment strategies for those suffering from congenital heart diseases, specifically Fontan patients. However, several bottlenecks have delayed wider deployment of the simulation...
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| Veröffentlicht in: | Journal of biomechanics 2017-01, Vol.50, p.172-179 |
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| creator | Wei, Zhenglun (Alan) Trusty, Phillip M Tree, Mike Haggerty, Christopher M Tang, Elaine Fogel, Mark Yoganathan, Ajit P |
| description | Abstract Cardiovascular simulations have great potential as a clinical tool for planning and evaluating patient-specific treatment strategies for those suffering from congenital heart diseases, specifically Fontan patients. However, several bottlenecks have delayed wider deployment of the simulations for clinical use; the main obstacle is simulation cost. Currently, time-averaged clinical flow measurements are utilized as numerical boundary conditions (BCs) in order to reduce the computational power and time needed to offer surgical planning within a clinical time frame. Nevertheless, pulsatile blood flow is observed in vivo , and its significant impact on numerical simulations has been demonstrated. Therefore, it is imperative to carry out a comprehensive study analyzing the sensitivity of using time-averaged BCs. In this study, sensitivity is evaluated based on the discrepancies between hemodynamic metrics calculated using time-averaged and pulsatile BCs; smaller discrepancies indicate less sensitivity. The current study incorporates a comparison between 3D patient-specific CFD simulations using both the time-averaged and pulsatile BCs for 101 Fontan patients. The sensitivity analysis involves two clinically important hemodynamic metrics: hepatic flow distribution (HFD) and indexed power loss (iPL). Paired demographic group comparisons revealed that HFD sensitivity is significantly different between single and bilateral superior vena cava cohorts but no other demographic discrepancies were observed for HFD or iPL. Multivariate regression analyses show that the best predictors for sensitivity involve flow pulsatilities, time-averaged flow rates, and geometric characteristics of the Fontan connection. These predictors provide patient-specific guidelines to determine the effectiveness of analyzing patient-specific surgical options with time-averaged BCs within a clinical time frame. |
| doi_str_mv | 10.1016/j.jbiomech.2016.11.025 |
| format | Article |
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However, several bottlenecks have delayed wider deployment of the simulations for clinical use; the main obstacle is simulation cost. Currently, time-averaged clinical flow measurements are utilized as numerical boundary conditions (BCs) in order to reduce the computational power and time needed to offer surgical planning within a clinical time frame. Nevertheless, pulsatile blood flow is observed in vivo , and its significant impact on numerical simulations has been demonstrated. Therefore, it is imperative to carry out a comprehensive study analyzing the sensitivity of using time-averaged BCs. In this study, sensitivity is evaluated based on the discrepancies between hemodynamic metrics calculated using time-averaged and pulsatile BCs; smaller discrepancies indicate less sensitivity. The current study incorporates a comparison between 3D patient-specific CFD simulations using both the time-averaged and pulsatile BCs for 101 Fontan patients. The sensitivity analysis involves two clinically important hemodynamic metrics: hepatic flow distribution (HFD) and indexed power loss (iPL). Paired demographic group comparisons revealed that HFD sensitivity is significantly different between single and bilateral superior vena cava cohorts but no other demographic discrepancies were observed for HFD or iPL. Multivariate regression analyses show that the best predictors for sensitivity involve flow pulsatilities, time-averaged flow rates, and geometric characteristics of the Fontan connection. These predictors provide patient-specific guidelines to determine the effectiveness of analyzing patient-specific surgical options with time-averaged BCs within a clinical time frame.</description><identifier>ISSN: 0021-9290</identifier><identifier>EISSN: 1873-2380</identifier><identifier>DOI: 10.1016/j.jbiomech.2016.11.025</identifier><identifier>PMID: 27855985</identifier><language>eng</language><publisher>United States: Elsevier Ltd</publisher><subject>Adolescent ; Adult ; Anatomy ; Aorta ; Blood flow ; Boundary conditions ; Cardiovascular diseases ; Child ; Child, Preschool ; Children ; Children & youth ; Computational fluid dynamics ; Computer applications ; Computer Simulation ; Conduits ; Congenital defects ; Coronary artery disease ; EKG ; Energy loss ; Female ; Filters ; Flow velocity ; Fluid dynamics ; Fontan Procedure ; Heart Defects, Congenital - physiopathology ; Heart diseases ; Heart surgery ; Hemodynamics ; Hospitals ; Humans ; Hydrodynamics ; Image processing ; Lesions ; Liver - blood supply ; Magnetic resonance imaging ; Male ; Mathematical models ; Models, Cardiovascular ; NMR ; Nuclear magnetic resonance ; Optimization ; Optimization techniques ; Patient-specific ; Patient-Specific Modeling ; Physical Medicine and Rehabilitation ; Power loss ; Resonance ; Return flow ; Segmentation ; Sensitivity ; Simulation ; Surgical planning ; Translation ; Turnover time ; Univentricular ; Ventricle ; Young Adult</subject><ispartof>Journal of biomechanics, 2017-01, Vol.50, p.172-179</ispartof><rights>Elsevier Ltd</rights><rights>2016 Elsevier Ltd</rights><rights>Copyright © 2016 Elsevier Ltd. All rights reserved.</rights><rights>Copyright Elsevier Limited 2017</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c554t-6f56b1def68ca910a8a1995598b9b7d07d0cc94006154e43236efcf2188853ee3</citedby><cites>FETCH-LOGICAL-c554t-6f56b1def68ca910a8a1995598b9b7d07d0cc94006154e43236efcf2188853ee3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.proquest.com/docview/1885095685?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>230,314,780,784,885,3550,27924,27925,45995,64385,64387,64389,72469</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/27855985$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Wei, Zhenglun (Alan)</creatorcontrib><creatorcontrib>Trusty, Phillip M</creatorcontrib><creatorcontrib>Tree, Mike</creatorcontrib><creatorcontrib>Haggerty, Christopher M</creatorcontrib><creatorcontrib>Tang, Elaine</creatorcontrib><creatorcontrib>Fogel, Mark</creatorcontrib><creatorcontrib>Yoganathan, Ajit P</creatorcontrib><title>Can time-averaged flow boundary conditions be used to meet the clinical timeline for Fontan surgical planning?</title><title>Journal of biomechanics</title><addtitle>J Biomech</addtitle><description>Abstract Cardiovascular simulations have great potential as a clinical tool for planning and evaluating patient-specific treatment strategies for those suffering from congenital heart diseases, specifically Fontan patients. However, several bottlenecks have delayed wider deployment of the simulations for clinical use; the main obstacle is simulation cost. Currently, time-averaged clinical flow measurements are utilized as numerical boundary conditions (BCs) in order to reduce the computational power and time needed to offer surgical planning within a clinical time frame. Nevertheless, pulsatile blood flow is observed in vivo , and its significant impact on numerical simulations has been demonstrated. Therefore, it is imperative to carry out a comprehensive study analyzing the sensitivity of using time-averaged BCs. In this study, sensitivity is evaluated based on the discrepancies between hemodynamic metrics calculated using time-averaged and pulsatile BCs; smaller discrepancies indicate less sensitivity. The current study incorporates a comparison between 3D patient-specific CFD simulations using both the time-averaged and pulsatile BCs for 101 Fontan patients. The sensitivity analysis involves two clinically important hemodynamic metrics: hepatic flow distribution (HFD) and indexed power loss (iPL). Paired demographic group comparisons revealed that HFD sensitivity is significantly different between single and bilateral superior vena cava cohorts but no other demographic discrepancies were observed for HFD or iPL. Multivariate regression analyses show that the best predictors for sensitivity involve flow pulsatilities, time-averaged flow rates, and geometric characteristics of the Fontan connection. These predictors provide patient-specific guidelines to determine the effectiveness of analyzing patient-specific surgical options with time-averaged BCs within a clinical time frame.</description><subject>Adolescent</subject><subject>Adult</subject><subject>Anatomy</subject><subject>Aorta</subject><subject>Blood flow</subject><subject>Boundary conditions</subject><subject>Cardiovascular diseases</subject><subject>Child</subject><subject>Child, Preschool</subject><subject>Children</subject><subject>Children & youth</subject><subject>Computational fluid dynamics</subject><subject>Computer applications</subject><subject>Computer Simulation</subject><subject>Conduits</subject><subject>Congenital defects</subject><subject>Coronary artery disease</subject><subject>EKG</subject><subject>Energy loss</subject><subject>Female</subject><subject>Filters</subject><subject>Flow velocity</subject><subject>Fluid dynamics</subject><subject>Fontan Procedure</subject><subject>Heart Defects, Congenital - physiopathology</subject><subject>Heart diseases</subject><subject>Heart surgery</subject><subject>Hemodynamics</subject><subject>Hospitals</subject><subject>Humans</subject><subject>Hydrodynamics</subject><subject>Image processing</subject><subject>Lesions</subject><subject>Liver - blood supply</subject><subject>Magnetic resonance imaging</subject><subject>Male</subject><subject>Mathematical models</subject><subject>Models, Cardiovascular</subject><subject>NMR</subject><subject>Nuclear magnetic resonance</subject><subject>Optimization</subject><subject>Optimization techniques</subject><subject>Patient-specific</subject><subject>Patient-Specific Modeling</subject><subject>Physical Medicine and Rehabilitation</subject><subject>Power loss</subject><subject>Resonance</subject><subject>Return flow</subject><subject>Segmentation</subject><subject>Sensitivity</subject><subject>Simulation</subject><subject>Surgical planning</subject><subject>Translation</subject><subject>Turnover time</subject><subject>Univentricular</subject><subject>Ventricle</subject><subject>Young Adult</subject><issn>0021-9290</issn><issn>1873-2380</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>8G5</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><recordid>eNqFkkFv1DAQhSMEokvhL1SWuHBJ8CRxYl8K1Yq2SJU4AGfLcSa7Dom92Mmi_nuc3W6BXpAs2dY8f57n5yS5AJoBhep9n_WNcSPqbZbHfQaQ0Zw9S1bA6yLNC06fJytKc0hFLuhZ8iqEnlJal7V4mZzlNWdMcLZK7FpZMpkRU7VHrzbYkm5wv0jjZtsqf0-0s62ZjLOBNEjmEAWTIyPiRKYtEj0Ya7QaDoy4RtI5T66dnSI3zH5zKO4GZa2xmw-vkxedGgK-eZjPk-_Xn76tb9O7Lzef11d3qWasnNKqY1UDLXYV10oAVVyBEEvLjWjqlsahtSgprYCVWBZ5UWGnuxw456xALM6TyyN3Nzcjthrt5NUgd96M0ZR0ysh_K9Zs5cbtJQMBImcR8O4B4N3PGcMkRxM0DtEIujlI4CXUoqaMRunbJ9Lezd5Ge1HFGRWs4guwOqq0dyF47B6bASqXSGUvT5HKJVIJIOmhk4u_rTweO2UYBR-PAowPujfoZdAGrcbWeNSTbJ35_x2XTxCnXH_gPYY_fmTIJZVfl4-1_CuoCgBRiOI3HxDMDA</recordid><startdate>20170104</startdate><enddate>20170104</enddate><creator>Wei, Zhenglun (Alan)</creator><creator>Trusty, Phillip M</creator><creator>Tree, Mike</creator><creator>Haggerty, Christopher M</creator><creator>Tang, Elaine</creator><creator>Fogel, Mark</creator><creator>Yoganathan, Ajit P</creator><general>Elsevier Ltd</general><general>Elsevier Limited</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7QP</scope><scope>7TB</scope><scope>7TS</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8FD</scope><scope>8FE</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>8G5</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M2O</scope><scope>M7P</scope><scope>MBDVC</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>Q9U</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>20170104</creationdate><title>Can time-averaged flow boundary conditions be used to meet the clinical timeline for Fontan surgical planning?</title><author>Wei, Zhenglun (Alan) ; Trusty, Phillip M ; Tree, Mike ; Haggerty, Christopher M ; Tang, Elaine ; Fogel, Mark ; Yoganathan, Ajit P</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c554t-6f56b1def68ca910a8a1995598b9b7d07d0cc94006154e43236efcf2188853ee3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Adolescent</topic><topic>Adult</topic><topic>Anatomy</topic><topic>Aorta</topic><topic>Blood flow</topic><topic>Boundary conditions</topic><topic>Cardiovascular diseases</topic><topic>Child</topic><topic>Child, Preschool</topic><topic>Children</topic><topic>Children & youth</topic><topic>Computational fluid dynamics</topic><topic>Computer applications</topic><topic>Computer Simulation</topic><topic>Conduits</topic><topic>Congenital defects</topic><topic>Coronary artery disease</topic><topic>EKG</topic><topic>Energy loss</topic><topic>Female</topic><topic>Filters</topic><topic>Flow velocity</topic><topic>Fluid dynamics</topic><topic>Fontan Procedure</topic><topic>Heart Defects, Congenital - physiopathology</topic><topic>Heart diseases</topic><topic>Heart surgery</topic><topic>Hemodynamics</topic><topic>Hospitals</topic><topic>Humans</topic><topic>Hydrodynamics</topic><topic>Image processing</topic><topic>Lesions</topic><topic>Liver - blood supply</topic><topic>Magnetic resonance imaging</topic><topic>Male</topic><topic>Mathematical models</topic><topic>Models, Cardiovascular</topic><topic>NMR</topic><topic>Nuclear magnetic resonance</topic><topic>Optimization</topic><topic>Optimization techniques</topic><topic>Patient-specific</topic><topic>Patient-Specific Modeling</topic><topic>Physical Medicine and Rehabilitation</topic><topic>Power loss</topic><topic>Resonance</topic><topic>Return flow</topic><topic>Segmentation</topic><topic>Sensitivity</topic><topic>Simulation</topic><topic>Surgical planning</topic><topic>Translation</topic><topic>Turnover time</topic><topic>Univentricular</topic><topic>Ventricle</topic><topic>Young Adult</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wei, Zhenglun (Alan)</creatorcontrib><creatorcontrib>Trusty, Phillip M</creatorcontrib><creatorcontrib>Tree, Mike</creatorcontrib><creatorcontrib>Haggerty, Christopher M</creatorcontrib><creatorcontrib>Tang, Elaine</creatorcontrib><creatorcontrib>Fogel, Mark</creatorcontrib><creatorcontrib>Yoganathan, Ajit P</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Calcium & Calcified Tissue Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Physical Education Index</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Research Library (Alumni Edition)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>Research Library Prep</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>ProQuest Biological Science Collection</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Research Library</collection><collection>Biological Science Database</collection><collection>Research Library (Corporate)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>ProQuest Central Basic</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Journal of biomechanics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wei, Zhenglun (Alan)</au><au>Trusty, Phillip M</au><au>Tree, Mike</au><au>Haggerty, Christopher M</au><au>Tang, Elaine</au><au>Fogel, Mark</au><au>Yoganathan, Ajit P</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Can time-averaged flow boundary conditions be used to meet the clinical timeline for Fontan surgical planning?</atitle><jtitle>Journal of biomechanics</jtitle><addtitle>J Biomech</addtitle><date>2017-01-04</date><risdate>2017</risdate><volume>50</volume><spage>172</spage><epage>179</epage><pages>172-179</pages><issn>0021-9290</issn><eissn>1873-2380</eissn><abstract>Abstract Cardiovascular simulations have great potential as a clinical tool for planning and evaluating patient-specific treatment strategies for those suffering from congenital heart diseases, specifically Fontan patients. However, several bottlenecks have delayed wider deployment of the simulations for clinical use; the main obstacle is simulation cost. Currently, time-averaged clinical flow measurements are utilized as numerical boundary conditions (BCs) in order to reduce the computational power and time needed to offer surgical planning within a clinical time frame. Nevertheless, pulsatile blood flow is observed in vivo , and its significant impact on numerical simulations has been demonstrated. Therefore, it is imperative to carry out a comprehensive study analyzing the sensitivity of using time-averaged BCs. In this study, sensitivity is evaluated based on the discrepancies between hemodynamic metrics calculated using time-averaged and pulsatile BCs; smaller discrepancies indicate less sensitivity. The current study incorporates a comparison between 3D patient-specific CFD simulations using both the time-averaged and pulsatile BCs for 101 Fontan patients. The sensitivity analysis involves two clinically important hemodynamic metrics: hepatic flow distribution (HFD) and indexed power loss (iPL). Paired demographic group comparisons revealed that HFD sensitivity is significantly different between single and bilateral superior vena cava cohorts but no other demographic discrepancies were observed for HFD or iPL. Multivariate regression analyses show that the best predictors for sensitivity involve flow pulsatilities, time-averaged flow rates, and geometric characteristics of the Fontan connection. These predictors provide patient-specific guidelines to determine the effectiveness of analyzing patient-specific surgical options with time-averaged BCs within a clinical time frame.</abstract><cop>United States</cop><pub>Elsevier Ltd</pub><pmid>27855985</pmid><doi>10.1016/j.jbiomech.2016.11.025</doi><tpages>8</tpages><oa>free_for_read</oa></addata></record> |
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| source | MEDLINE; ScienceDirect Journals (5 years ago - present); ProQuest Central UK/Ireland |
| subjects | Adolescent Adult Anatomy Aorta Blood flow Boundary conditions Cardiovascular diseases Child Child, Preschool Children Children & youth Computational fluid dynamics Computer applications Computer Simulation Conduits Congenital defects Coronary artery disease EKG Energy loss Female Filters Flow velocity Fluid dynamics Fontan Procedure Heart Defects, Congenital - physiopathology Heart diseases Heart surgery Hemodynamics Hospitals Humans Hydrodynamics Image processing Lesions Liver - blood supply Magnetic resonance imaging Male Mathematical models Models, Cardiovascular NMR Nuclear magnetic resonance Optimization Optimization techniques Patient-specific Patient-Specific Modeling Physical Medicine and Rehabilitation Power loss Resonance Return flow Segmentation Sensitivity Simulation Surgical planning Translation Turnover time Univentricular Ventricle Young Adult |
| title | Can time-averaged flow boundary conditions be used to meet the clinical timeline for Fontan surgical planning? |
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