Design and nonlinear modeling of a sensitive sensor for the measurement of flow in mice

Jawde, Samer Bou; Smith, Bradford J.; Sonnenberg, Adam; Bates, Jason H. T.; Suki, Bélâ

doi:10.1088/1361-6579/aacb1b

Cited by 5 publications

(6 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Baseline ventilation, consisting of a tidal volume (Vt) = 6 ml/kg, PEEP = 3 cmH 2 O, and respiratory rate (RR) = 250 breaths/min, was applied for a 10 min stabilization period with recruitment maneuvers at 3 min intervals. Pressure and volume were recorded with a custom flowmeter based on our previously published design (Jawde et al, 2018). Four types of ventilation were recorded for analysis: (1) VCV-PEEP0, consisting the baseline ventilation with PEEP = 0 cmH 2 O, (2) VCV-PEEP12 that was the baseline ventilation with PEEP = 12 cmH 2 O, (3) HighPressure-PEEP0 that consisted of a inspiratory pressure (Pplat) = 35 cmH 2 O and PEEP = 0 cmH 2 O with RR = 60 breaths/min, and (4) PCV-PEEP0 with Pplat = 10 and PEEP = 0 cmH 2 O with RR = 70 breaths/min.…”

Section: Mouse Mechanical Ventilation Experimentsmentioning

confidence: 99%

A Damaged-Informed Lung Ventilator Model for Ventilator Waveforms

et al. 2021

View full text Add to dashboard Cite

Motivated by a desire to understand pulmonary physiology, scientists have developed physiological lung models of varying complexity. However, pathophysiology and interactions between human lungs and ventilators, e.g., ventilator-induced lung injury (VILI), present challenges for modeling efforts. This is because the real-world pressure and volume signals may be too complex for simple models to capture, and while complex models tend not to be estimable with clinical data, limiting clinical utility. To address this gap, in this manuscript we developed a new damaged-informed lung ventilator (DILV) model. This approach relies on mathematizing ventilator pressure and volume waveforms, including lung physiology, mechanical ventilation, and their interaction. The model begins with nominal waveforms and adds limited, clinically relevant, hypothesis-driven features to the waveform corresponding to pulmonary pathophysiology, patient-ventilator interaction, and ventilator settings. The DILV model parameters uniquely and reliably recapitulate these features while having enough flexibility to reproduce commonly observed variability in clinical (human) and laboratory (mouse) waveform data. We evaluate the proof-in-principle capabilities of our modeling approach by estimating 399 breaths collected for differently damaged lungs for tightly controlled measurements in mice and uncontrolled human intensive care unit data in the absence and presence of ventilator dyssynchrony. The cumulative value of mean squares error for the DILV model is, on average, ≈12 times less than the single compartment lung model for all the waveforms considered. Moreover, changes in the estimated parameters correctly correlate with known measures of lung physiology, including lung compliance as a baseline evaluation. Our long-term goal is to use the DILV model for clinical monitoring and research studies by providing high fidelity estimates of lung state and sources of VILI with an end goal of improving management of VILI and acute respiratory distress syndrome.

show abstract

Section: Mouse Mechanical Ventilation Experimentsmentioning

confidence: 99%

A Damaged-Informed Lung Ventilator Model for Ventilator Waveforms

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Moreover, the 3D printing of high precision enhances the possibility of manufacturing the capillary passages of such a flowmeter accurately. It is worth noting that the 3D printing technique has been used to fabricate a flow sensor for small animals' ventilators by Jawde et al [19], but for human ventilators, the 3D printed technique has not been used.…”

Section: Other Flowmetersmentioning

confidence: 99%

Laminar flowmeter for mechanical ventilator: Manufacturing challenge of Covid-19 pandemic

Alsalaet

Munahi

Al-Sabur

et al. 2021

Flow Measurement and Instrumentation

View full text Add to dashboard Cite

“…The experimental protocol was approved by the Institutional Animal Care and Use Committee of Boston University and all methods were carried out in accordance with relevant guidelines and regulations for the care and use of animals. The experimental setup was described previously 38 . However, utilizing the data for ZVV is presented here for the first time.…”

Section: Measurement Of Respiratory Impedance (Z) Via Variable Ventilmentioning

confidence: 99%

“…Briefly, mice (C57BL/6, n = 5, 29.8 ± 2.0 g) were anaesthetized, tracheostomized, and ventilated (flexiVent Legacy, Scireq, Montreal, CA) at V T,B = 8 ml/Kg using VV with a specific mouse-optimized V T distribution 24 . A differential pressure transducer (Biopac Systems, Model TSD160A) recorded pressure drop across a lab-designed flow sensor 38 , while a gauge pressure transducer (WPI, 07B PNEU05) was connected distal to the sensor to measure airway opening pressure. The signals were digitized (WPI, DataTrax) at 500 Hz.…”

Section: Measurement Of Respiratory Impedance (Z) Via Variable Ventilmentioning

confidence: 99%

Tracking respiratory mechanics around natural breathing rates via variable ventilation

Jawde

Walkey

Majumdar

et al. 2020

Sci Rep

Self Cite

View full text Add to dashboard Cite

Measuring respiratory resistance and elastance as a function of time, tidal volume, respiratory rate, and positive end-expiratory pressure can guide mechanical ventilation. However, current measurement techniques are limited since they are assessed intermittently at non-physiological frequencies or involve specialized equipment. to this end, we introduce ZVV, a practical approach to continuously track resistance and elastance during Variable Ventilation (VV), in which frequency and tidal volume vary from breath-to-breath. ZVV segments airway pressure and flow recordings into individual breaths, calculates resistance and elastance for each breath, bins them according to frequency or tidal volume and plots the results against bin means. ZVV's feasibility was assessed clinically in five human patients with acute lung injury, experimentally in five mice ventilated before and after lavage injury, and computationally using a viscoelastic respiratory model. ZVV provided continuous measurements in both settings, while the computational study revealed <2% estimation errors. Our findings support ZVV as a feasible technique to assess respiratory mechanics under physiological conditions. Additionally, in humans, ZVV detected a decrease in resistance and elastance with time by 12.8% and 6.2%, respectively, suggesting that VV can improve lung recruitment in some patients and can therefore potentially serve both as a dual diagnostic and therapeutic tool.Respiratory resistance (R) and elastance (E) in mechanically ventilated patients relate to disease severity and progression, and patient response to treatment or changes in ventilator settings 1-3 . The manner in which R and E vary with frequency can reflect lung heterogeneity, a sensitive indicator of pathology 4 . Thus, a technique that can continuously and reliably assess R and E at physiological frequencies and tidal volumes (V T 's) could advance the treatment of mechanically ventilated patients. For example, increases in R can reveal bronchospasm 5 or a blocked endotracheal tube 6,7 , while increases in E can reflect pulmonary edema and alveolar derecruitment 8,9 . Tracking continuously R and E including their frequency dependencies could improve patient management via detection of airway obstruction in asthma, or optimization of mechanical ventilator settings for preventing ventilation-induced lung injury 10-15 .Nevertheless, estimates of R and E are rarely performed in clinical practice 16 , likely due to current strategies of intermittent end inspiratory occlusion measurements that require deep patient sedation or paralysis or the need for specialized equipment 10,17 . In order to adjust ventilation settings, clinicians are currently guided by gas exchange parameters, breath-to-breath peak pressures, and plateau pressures 16,[18][19][20] . However, these measures lack the ability to reveal whether abnormalities in mechanics are related to altered R or E and often require a heavily sedated or paralyzed patient 9,21 . The limitations in measuring R and E are not only pres...

show abstract

Design and nonlinear modeling of a sensitive sensor for the measurement of flow in mice

Abstract: We demonstrate that the application of nonparametric nonlinear Volterra series modeling in combination with 3D printing technology allows the inexpensive and rapid fabrication of an accurate flow sensor for continuously measuring small flows in various physiological conditions.

Cited by 5 publications

References 22 publications

A Damaged-Informed Lung Ventilator Model for Ventilator Waveforms

A Damaged-Informed Lung Ventilator Model for Ventilator Waveforms

Laminar flowmeter for mechanical ventilator: Manufacturing challenge of Covid-19 pandemic

Tracking respiratory mechanics around natural breathing rates via variable ventilation

Contact Info

Product

Resources

About