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Reconstructing asynchrony for mechanical ventilation using a hysteresis loop virtual patient model
BACKGROUND: Patient-specific lung mechanics during mechanical ventilation (MV) can be identified from measured waveforms of fully ventilated, sedated patients. However, asynchrony due to spontaneous breathing (SB) effort can be common, altering these waveforms and reducing the accuracy of identified...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8900099/ https://www.ncbi.nlm.nih.gov/pubmed/35255922 http://dx.doi.org/10.1186/s12938-022-00986-9 |
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author | Zhou, Cong Chase, J. Geoffrey Sun, Qianhui Knopp, Jennifer Tawhai, Merryn H. Desaive, Thomas Möller, Knut Shaw, Geoffrey M. Chiew, Yeong Shiong Benyo, Balazs |
author_facet | Zhou, Cong Chase, J. Geoffrey Sun, Qianhui Knopp, Jennifer Tawhai, Merryn H. Desaive, Thomas Möller, Knut Shaw, Geoffrey M. Chiew, Yeong Shiong Benyo, Balazs |
author_sort | Zhou, Cong |
collection | PubMed |
description | BACKGROUND: Patient-specific lung mechanics during mechanical ventilation (MV) can be identified from measured waveforms of fully ventilated, sedated patients. However, asynchrony due to spontaneous breathing (SB) effort can be common, altering these waveforms and reducing the accuracy of identified, model-based, and patient-specific lung mechanics. METHODS: Changes in patient-specific lung elastance over a pressure–volume (PV) loop, identified using hysteresis loop analysis (HLA), are used to detect the occurrence of asynchrony and identify its type and pattern. The identified HLA parameters are then combined with a nonlinear mechanics hysteresis loop model (HLM) to extract and reconstruct ventilated waveforms unaffected by asynchronous breaths. Asynchrony magnitude can then be quantified using an energy-dissipation metric, E(asyn), comparing PV loop area between model-reconstructed and original, altered asynchronous breathing cycles. Performance is evaluated using both test-lung experimental data with a known ground truth and clinical data from four patients with varying levels of asynchrony. RESULTS: Root mean square errors for reconstructed PV loops are within 5% for test-lung experimental data, and 10% for over 90% of clinical data. E(asyn) clearly matches known asynchrony magnitude for experimental data with RMS errors < 4.1%. Clinical data performance shows 57% breaths having E(asyn) > 50% for Patient 1 and 13% for Patient 2. Patient 3 only presents 20% breaths with E(asyn) > 10%. Patient 4 has E(asyn) = 0 for 96% breaths showing accuracy in a case without asynchrony. CONCLUSIONS: Experimental test-lung validation demonstrates the method’s reconstruction accuracy and generality in controlled scenarios. Clinical validation matches direct observations of asynchrony in incidence and quantifies magnitude, including cases without asynchrony, validating its robustness and potential efficacy as a clinical real-time asynchrony monitoring tool. |
format | Online Article Text |
id | pubmed-8900099 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-89000992022-03-07 Reconstructing asynchrony for mechanical ventilation using a hysteresis loop virtual patient model Zhou, Cong Chase, J. Geoffrey Sun, Qianhui Knopp, Jennifer Tawhai, Merryn H. Desaive, Thomas Möller, Knut Shaw, Geoffrey M. Chiew, Yeong Shiong Benyo, Balazs Biomed Eng Online Research BACKGROUND: Patient-specific lung mechanics during mechanical ventilation (MV) can be identified from measured waveforms of fully ventilated, sedated patients. However, asynchrony due to spontaneous breathing (SB) effort can be common, altering these waveforms and reducing the accuracy of identified, model-based, and patient-specific lung mechanics. METHODS: Changes in patient-specific lung elastance over a pressure–volume (PV) loop, identified using hysteresis loop analysis (HLA), are used to detect the occurrence of asynchrony and identify its type and pattern. The identified HLA parameters are then combined with a nonlinear mechanics hysteresis loop model (HLM) to extract and reconstruct ventilated waveforms unaffected by asynchronous breaths. Asynchrony magnitude can then be quantified using an energy-dissipation metric, E(asyn), comparing PV loop area between model-reconstructed and original, altered asynchronous breathing cycles. Performance is evaluated using both test-lung experimental data with a known ground truth and clinical data from four patients with varying levels of asynchrony. RESULTS: Root mean square errors for reconstructed PV loops are within 5% for test-lung experimental data, and 10% for over 90% of clinical data. E(asyn) clearly matches known asynchrony magnitude for experimental data with RMS errors < 4.1%. Clinical data performance shows 57% breaths having E(asyn) > 50% for Patient 1 and 13% for Patient 2. Patient 3 only presents 20% breaths with E(asyn) > 10%. Patient 4 has E(asyn) = 0 for 96% breaths showing accuracy in a case without asynchrony. CONCLUSIONS: Experimental test-lung validation demonstrates the method’s reconstruction accuracy and generality in controlled scenarios. Clinical validation matches direct observations of asynchrony in incidence and quantifies magnitude, including cases without asynchrony, validating its robustness and potential efficacy as a clinical real-time asynchrony monitoring tool. BioMed Central 2022-03-07 /pmc/articles/PMC8900099/ /pubmed/35255922 http://dx.doi.org/10.1186/s12938-022-00986-9 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Zhou, Cong Chase, J. Geoffrey Sun, Qianhui Knopp, Jennifer Tawhai, Merryn H. Desaive, Thomas Möller, Knut Shaw, Geoffrey M. Chiew, Yeong Shiong Benyo, Balazs Reconstructing asynchrony for mechanical ventilation using a hysteresis loop virtual patient model |
title | Reconstructing asynchrony for mechanical ventilation using a hysteresis loop virtual patient model |
title_full | Reconstructing asynchrony for mechanical ventilation using a hysteresis loop virtual patient model |
title_fullStr | Reconstructing asynchrony for mechanical ventilation using a hysteresis loop virtual patient model |
title_full_unstemmed | Reconstructing asynchrony for mechanical ventilation using a hysteresis loop virtual patient model |
title_short | Reconstructing asynchrony for mechanical ventilation using a hysteresis loop virtual patient model |
title_sort | reconstructing asynchrony for mechanical ventilation using a hysteresis loop virtual patient model |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8900099/ https://www.ncbi.nlm.nih.gov/pubmed/35255922 http://dx.doi.org/10.1186/s12938-022-00986-9 |
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