“…The HLM lung mechanics model is described as follows [ 7 ]: where V is the volume of air delivered to the lungs widely used in respiratory mechanics models [ 6 ], and K e represents the alveolar recruitment elastance. V h 1 and V h 2 are hysteretic volume response during inspiration and expiration, respectively, representing the key characteristics of nonlinear stress–strain or force–deformation relation, thus critical for determining the two nonlinear hysteretic springs, K h1 and K h2 , for alveolar hysteresis elastance during inspiration and expiration, respectively.…”
Section: Methodsmentioning
confidence: 99%
“…6 . A detailed derivation of the model parameters based on HLA segmentation in the HLM lung mechanics model can be found in [ 7 ], which is based in part on the prior basis function methods and models of [ 5 , 8 , 43 ]. Fig.…”
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.
“…The HLM lung mechanics model is described as follows [ 7 ]: where V is the volume of air delivered to the lungs widely used in respiratory mechanics models [ 6 ], and K e represents the alveolar recruitment elastance. V h 1 and V h 2 are hysteretic volume response during inspiration and expiration, respectively, representing the key characteristics of nonlinear stress–strain or force–deformation relation, thus critical for determining the two nonlinear hysteretic springs, K h1 and K h2 , for alveolar hysteresis elastance during inspiration and expiration, respectively.…”
Section: Methodsmentioning
confidence: 99%
“…6 . A detailed derivation of the model parameters based on HLA segmentation in the HLM lung mechanics model can be found in [ 7 ], which is based in part on the prior basis function methods and models of [ 5 , 8 , 43 ]. Fig.…”
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.
“…Currently, deterministic basis-function based virtual patient lung mechanics models provide accurate modelling and prediction using a linear single compartment lung model [30][31][32] . They assume the shape of lung elastance and resistance over pressure, volume and flow.…”
Section: Abbreviationsmentioning
confidence: 99%
“…Ethics approval for the McREM trial was granted by local ethics committee for each of the eight participating German ICUs [61] . The 14 patients in the Maastricht trail (METC17-4-053) [32] were treated using Bi-level Positive Airway Pressure (BIPAP), a common PC mode. Each patient in both cohorts underwent a 6-step staircase RM using PEEP = 2cmH2O/step, except Patient 18 in the McREM cohort who had only 4 steps.…”
“…2) Retrospective mode: This mode allows analysis of retrospective patient data. Virtual patients can be simulated in this mode for research purposes [10], [19], [20]. It also enables user training or testing.…”
Section: B Data Acquisition System and Data Storage Systemmentioning
The rise of model-based and machine learning methods have created increasingly realistic opportunities to implement personalized, patient-specific mechanical ventilation (MV) in the ICU. These methods require monitoring of real-time patient ventilation waveform data (VWD) during MV treatment. However, there are relatively few non-invasive and/or non-proprietary systems to monitor and record patientspecific lung condition in real-time. In this paper, we present a CARE network data acquisition and monitoring system (CARENet) to automate data collection and to remotely monitor patient-specific lung condition and ventilation parameters. The automated system acquires VWD from a mechanical ventilator using a data acquisition device (DAQ), stores data in network-attached storage (NAS), and processes VWDs via a data management platform (DMP) web application. The web application enables real-time and retrospective modelbased monitoring and analysis of clinical MV data. In particular, CARENet provides detailed breath-by-breath patient-specific respiratory mechanics, as well as the overall trends assessing changes in patient condition. Validation testing with a retrospective data set illustrated how breath-to-breath and time-varying patientventilator interaction during MV can be monitored, and, in turn, can be used to guide MV treatment. The network data acquisition system design presented is low-cost, open, and enables continuous, automated, scalable, realtime collection and analysis of waveform data, to help improve decision making, care, and outcomes in MV.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
