Model-based estimation of Frank-Starling curves at the patient bedside
Determining physiological mechanisms contributing to circulatory failure can be challenging, contributing to the difficulties of delivering effective hemodynamic management in critical care. Measured or estimated Frank-Starling curves could potentially make it much easier to assess patient response...
| Main Authors: | , , , , , |
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| Format: | Conference item |
| Language: | English |
| Published: |
International Federation of Automatic Control
2021
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| _version_ | 1826308956746678272 |
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| author | Smith, R Chase, JG Pretty, CG Davidson, S Shaw, GM Desaive, T |
| author_facet | Smith, R Chase, JG Pretty, CG Davidson, S Shaw, GM Desaive, T |
| author_sort | Smith, R |
| collection | OXFORD |
| description | Determining physiological mechanisms contributing to circulatory failure can be challenging, contributing to the difficulties of delivering effective hemodynamic management in critical care. Measured or estimated Frank-Starling curves could potentially make it much easier to assess patient response to interventions, and thus to manage circulatory failure. This study combines non-additionally invasive model-based methods to estimate left ventricle end-diastolic volume (LEDV) and stroke volume (SV) during hemodynamic interventions in a pig trial. Frank-Starling curves are created using these metrics and Frank-Starling contractility (FSC) is identified as the gradient. Bland-Altman median bias [limits of agreement (2.5th, 97.5th percentile)] are 0.14[-0.56, 0.57] for model-based FSC agreement with measured reference method FSC using admittance catheter LEDV and aortic flow probe SV. This study provides proof-of-concept Frank-Starling curves could be non-additionally invasively estimated clinically for critically ill patients to provide clearer insight into cardiovascular function than is currently possible. |
| first_indexed | 2024-03-07T07:27:04Z |
| format | Conference item |
| id | oxford-uuid:39cb468a-2183-4528-b472-e6fbaff17f6d |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T07:27:04Z |
| publishDate | 2021 |
| publisher | International Federation of Automatic Control |
| record_format | dspace |
| spelling | oxford-uuid:39cb468a-2183-4528-b472-e6fbaff17f6d2022-11-28T13:50:53ZModel-based estimation of Frank-Starling curves at the patient bedsideConference itemhttp://purl.org/coar/resource_type/c_5794uuid:39cb468a-2183-4528-b472-e6fbaff17f6dEnglishSymplectic ElementsInternational Federation of Automatic Control2021Smith, RChase, JGPretty, CGDavidson, SShaw, GMDesaive, TDetermining physiological mechanisms contributing to circulatory failure can be challenging, contributing to the difficulties of delivering effective hemodynamic management in critical care. Measured or estimated Frank-Starling curves could potentially make it much easier to assess patient response to interventions, and thus to manage circulatory failure. This study combines non-additionally invasive model-based methods to estimate left ventricle end-diastolic volume (LEDV) and stroke volume (SV) during hemodynamic interventions in a pig trial. Frank-Starling curves are created using these metrics and Frank-Starling contractility (FSC) is identified as the gradient. Bland-Altman median bias [limits of agreement (2.5th, 97.5th percentile)] are 0.14[-0.56, 0.57] for model-based FSC agreement with measured reference method FSC using admittance catheter LEDV and aortic flow probe SV. This study provides proof-of-concept Frank-Starling curves could be non-additionally invasively estimated clinically for critically ill patients to provide clearer insight into cardiovascular function than is currently possible. |
| spellingShingle | Smith, R Chase, JG Pretty, CG Davidson, S Shaw, GM Desaive, T Model-based estimation of Frank-Starling curves at the patient bedside |
| title | Model-based estimation of Frank-Starling curves at the patient bedside |
| title_full | Model-based estimation of Frank-Starling curves at the patient bedside |
| title_fullStr | Model-based estimation of Frank-Starling curves at the patient bedside |
| title_full_unstemmed | Model-based estimation of Frank-Starling curves at the patient bedside |
| title_short | Model-based estimation of Frank-Starling curves at the patient bedside |
| title_sort | model based estimation of frank starling curves at the patient bedside |
| work_keys_str_mv | AT smithr modelbasedestimationoffrankstarlingcurvesatthepatientbedside AT chasejg modelbasedestimationoffrankstarlingcurvesatthepatientbedside AT prettycg modelbasedestimationoffrankstarlingcurvesatthepatientbedside AT davidsons modelbasedestimationoffrankstarlingcurvesatthepatientbedside AT shawgm modelbasedestimationoffrankstarlingcurvesatthepatientbedside AT desaivet modelbasedestimationoffrankstarlingcurvesatthepatientbedside |