TY - JOUR
T1 - Reconstructing asynchrony for mechanical ventilation using a hysteresis loop virtual patient model
AU - Zhou, Cong
AU - Chase, J. Geoffrey
AU - Sun, Qianhui
AU - Knopp, Jennifer
AU - Tawhai, Merryn H.
AU - Desaive, Thomas
AU - Möller, Knut
AU - Shaw, Geoffrey M.
AU - Chiew, Yeong Shiong
AU - Benyo, Balazs
N1 - Funding Information:
This work was supported by the NZ Tertiary Education Commission (TEC) fund MedTech CoRE (Centre of Research Excellence; #3705718) and the NZ National Science Challenge 7, Science for Technology and Innovation (2019-S3-CRS). This work was also supported by the IMHM Fund (14005-5140200002) from Taicang Yangtze River Delta Research Institute of Northwestern Polytechnical University, and the EU H2020 R&I programme (MSCA-RISE-2019 call) under grant agreement #872488-DCPM.
Publisher Copyright:
© 2022, The Author(s).
PY - 2022/3/7
Y1 - 2022/3/7
N2 - 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, Easyn, 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. Easyn clearly matches known asynchrony magnitude for experimental data with RMS errors < 4.1%. Clinical data performance shows 57% breaths having Easyn > 50% for Patient 1 and 13% for Patient 2. Patient 3 only presents 20% breaths with Easyn > 10%. Patient 4 has Easyn = 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.
AB - 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, Easyn, 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. Easyn clearly matches known asynchrony magnitude for experimental data with RMS errors < 4.1%. Clinical data performance shows 57% breaths having Easyn > 50% for Patient 1 and 13% for Patient 2. Patient 3 only presents 20% breaths with Easyn > 10%. Patient 4 has Easyn = 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.
KW - Asynchrony
KW - Hysteresis loop model
KW - Hysteretic lung mechanics
KW - Lung mechanics
KW - Mechanical ventilation
KW - Virtual patient
UR - http://www.scopus.com/inward/record.url?scp=85125975107&partnerID=8YFLogxK
U2 - 10.1186/s12938-022-00986-9
DO - 10.1186/s12938-022-00986-9
M3 - Article
C2 - 35255922
AN - SCOPUS:85125975107
SN - 1475-925X
VL - 21
JO - BioMedical Engineering Online
JF - BioMedical Engineering Online
IS - 1
M1 - 16
ER -