Preload & Frank-Starling curves, from textbook to bedside: clinically applicable non-additionally invasive model-based estimation in pigs
Background Determining physiological mechanisms leading to circulatory failure can be challenging, contributing to the difficulties in delivering effective hemodynamic management in critical care. Continuous, non-additionally invasive monitoring of preload changes, and assessment of contractility fr...
| Main Authors: | , , , , , |
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| Format: | Journal article |
| Language: | English |
| Published: |
Elsevier
2021
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| _version_ | 1797108278327508992 |
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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 | Background
Determining physiological mechanisms leading to circulatory failure can be challenging, contributing to the difficulties in delivering effective hemodynamic management in critical care. Continuous, non-additionally invasive monitoring of preload changes, and assessment of contractility from Frank-Starling curves could potentially make it much easier to diagnose and manage circulatory failure.
Method
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 (N = 6). Agreement of model-based LEDV and measured admittance catheter LEDV is assessed. Model-based LEDV and SV are used to identify response to hemodynamic interventions and create Frank-Starling curves, from which Frank-Starling contractility (FSC) is identified as the gradient.
Results
Model-based LEDV had good agreement with measured admittance catheter LEDV, with Bland-Altman median bias [limits of agreement (2.5th, 97.5th percentile)] of 2.2 ml [-13.8, 22.5]. Model LEDV and SV were used to identify non-responsive interventions with a good area under the receiver-operating characteristic (ROC) curve of 0.83. FSC was identified using model LEDV and SV with Bland-Altman median bias [limits of agreement (2.5th, 97.5th percentile)] of 0.07 [-0.68, 0.56], with FSC from admittance catheter LEDV and aortic flow probe SV used as a reference method.
Conclusions
This study provides proof-of-concept preload changes and Frank-Starling curves could be non-additionally invasively estimated for critically ill patients, which could potentially enable much clearer insight into cardiovascular function than is currently possible at the patient bedside. |
| first_indexed | 2024-03-07T07:26:58Z |
| format | Journal article |
| id | oxford-uuid:25cf28f3-373c-4f6b-a152-ee58aa42e49f |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T07:26:58Z |
| publishDate | 2021 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | oxford-uuid:25cf28f3-373c-4f6b-a152-ee58aa42e49f2022-11-28T13:02:02ZPreload & Frank-Starling curves, from textbook to bedside: clinically applicable non-additionally invasive model-based estimation in pigsJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:25cf28f3-373c-4f6b-a152-ee58aa42e49fEnglishSymplectic ElementsElsevier2021Smith, RChase, JGPretty, CGDavidson, SShaw, GMDesaive, TBackground Determining physiological mechanisms leading to circulatory failure can be challenging, contributing to the difficulties in delivering effective hemodynamic management in critical care. Continuous, non-additionally invasive monitoring of preload changes, and assessment of contractility from Frank-Starling curves could potentially make it much easier to diagnose and manage circulatory failure. Method 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 (N = 6). Agreement of model-based LEDV and measured admittance catheter LEDV is assessed. Model-based LEDV and SV are used to identify response to hemodynamic interventions and create Frank-Starling curves, from which Frank-Starling contractility (FSC) is identified as the gradient. Results Model-based LEDV had good agreement with measured admittance catheter LEDV, with Bland-Altman median bias [limits of agreement (2.5th, 97.5th percentile)] of 2.2 ml [-13.8, 22.5]. Model LEDV and SV were used to identify non-responsive interventions with a good area under the receiver-operating characteristic (ROC) curve of 0.83. FSC was identified using model LEDV and SV with Bland-Altman median bias [limits of agreement (2.5th, 97.5th percentile)] of 0.07 [-0.68, 0.56], with FSC from admittance catheter LEDV and aortic flow probe SV used as a reference method. Conclusions This study provides proof-of-concept preload changes and Frank-Starling curves could be non-additionally invasively estimated for critically ill patients, which could potentially enable much clearer insight into cardiovascular function than is currently possible at the patient bedside. |
| spellingShingle | Smith, R Chase, JG Pretty, CG Davidson, S Shaw, GM Desaive, T Preload & Frank-Starling curves, from textbook to bedside: clinically applicable non-additionally invasive model-based estimation in pigs |
| title | Preload & Frank-Starling curves, from textbook to bedside: clinically applicable non-additionally invasive model-based estimation in pigs |
| title_full | Preload & Frank-Starling curves, from textbook to bedside: clinically applicable non-additionally invasive model-based estimation in pigs |
| title_fullStr | Preload & Frank-Starling curves, from textbook to bedside: clinically applicable non-additionally invasive model-based estimation in pigs |
| title_full_unstemmed | Preload & Frank-Starling curves, from textbook to bedside: clinically applicable non-additionally invasive model-based estimation in pigs |
| title_short | Preload & Frank-Starling curves, from textbook to bedside: clinically applicable non-additionally invasive model-based estimation in pigs |
| title_sort | preload frank starling curves from textbook to bedside clinically applicable non additionally invasive model based estimation in pigs |
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