Noninvasive estimation of alveolar pressure

Vicario, Francesco; Buizza, Roberto; Truschel, William A.; Chbat, Nicolas W.

doi:10.1109/embc.2016.7591292

Cited by 7 publications

(4 citation statements)

References 8 publications

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“…In contrast, ZVV on data from VV produces a spectrum of values with specific physiological information that may provide more insight to the clinician. ZVV's primary limitation is that it should be applied to ventilation-driven breath cycles with zero intrinsic PEEP (iPEEP) unless the esophageal pressure and iPEEP are measured and accounted for [47][48][49][50] . Otherwise, the patient's spontaneous breathing (or muscular activity) and iPEEP will affect the estimated parameters.…”

Section: Discussionmentioning

confidence: 99%

Tracking respiratory mechanics around natural breathing rates via variable ventilation

Jawde

Walkey

Majumdar

et al. 2020

Sci Rep

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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...

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Section: Discussionmentioning

confidence: 99%

Tracking respiratory mechanics around natural breathing rates via variable ventilation

Jawde

Walkey

Majumdar

et al. 2020

Sci Rep

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“…For the estimation of the respiratory time constant τ , we exploit the fact that under assisted spontaneous ventilation, most patients tend to be (more) passive during expirations. This can be recognized by the typical exponential flow curve and would allow a direct estimation of τ from the slope of the flow-volume loop and has already been exploited in previous works [37], [38]. Unfortunately, in many cases, expirations are confounded by residual patient activity, e.g., missed efforts or active expirations, which complicate the automatic determination of the time constant.…”

Section: Model-based Effort Estimationmentioning

confidence: 99%

Model-Based Estimation of Inspiratory Effort Using Surface EMG

Grahoff

Petersen

Walterspacher

et al. 2023

IEEE Trans. Biomed. Eng.

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Objective: The quantification of inspiratory patient effort in assisted mechanical ventilation is essential for the adjustment of ventilatory assistance and for assessing patient-ventilator interaction. The inspiratory effort is usually measured via the respiratory muscle pressure (Pmus) derived from esophageal pressure (Pes) measurements. As yet, no reliable non-invasive and unobtrusive alternatives exist to continuously quantify Pmus. Methods: We propose a model-based approach to estimate Pmus noninvasively during assisted ventilation using surface electromyographic (sEMG) measurements. The method combines the sEMG and ventilator signals to determine the lung elastance and resistance as well as the neuromechanical coupling of the respiratory muscles via a novel regression technique. Using the equation of motion, an estimate for Pmus can then be calculated directly from the lung mechanical parameters and the pneumatic ventilator signals. Results: The method was applied to data recorded from a total of 43 ventilated patients and validated against Pesderived Pmus. Patient effort was quantified via the Pmus pressure-time-product (PTP). The sEMG-derived PTP estimated using the proposed method was highly correlated to Pes-derived PTP (r = 0.95 ± 0.04), and the breathwise deviation between the two quantities was −0.83 ± 1.73 cmH 2 O s. Conclusion: The estimated, sEMG-derived Pmus is closely related to the Pes-based reference and allows to reliably quantify inspiratory effort. Significance: The proposed technique provides a valuable tool for physicians to assess patients undergoing assisted mechanical ventilation and, thus, may support clinical decision making.

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“…The leak estimation algorithm then requires to i) fit ( 6) to the measured data to obtain an estimate of 𝑄 D (and 𝜏, if desired), which can be done by computing the pseudoinverse of the data matrix in (7), ii) solve numerically the following set of two equations for 𝐺 +,-and 𝛾 𝐺 +,-𝑃 ' . 𝑡 𝑑𝑡 The key idea of obtaining preliminary information by fitting the exhalation data was inspired by the respiratory mechanics estimation algorithm in [8].…”

Section: Estimation Algorithmmentioning

confidence: 99%

“…In [6] the resistance and compliance of the respiratory system had to be fixed, assumed a priori and never updated. Instead, we formulate a method that leverages the estimation scheme presented in [8] and, as a result, i) it does not require explicit knowledge of the resistance and compliance, ii) it features the respiratory time constant as the only physiological parameter of the patient's lung mechanics needed for leak estimation, iii) it estimates such parameter on a breath-by-breath basis, overcoming the limitation of using fixed values, often derived from population averages that poorly represent the specific individual. Finally, we validate the concept using experimental data from a lung simulator where the patient's flow (or the leaks) can be directly measured.…”

Section: Introductionmentioning

confidence: 99%

Two-parameter leak estimation in non-invasive ventilation

Vicario

Alkhairy

Buizza

et al. 2017

2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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In this paper we present a method for the estimation of leaks in non-invasive ventilation. Accurate estimation of leaks is a key component of a ventilator, since it determines the ventilator performance in terms of patient-ventilator synchrony and air volume delivery. In particular, in non-invasive ventilation, the patient flow is significantly different from the flow measured at the ventilator outlet. This is mostly due to the vent orifice along the tube that is used for exhalation, but also to the non-intentional leaks that occur elsewhere in the circuit (e.g., at the mask). Such leaks are traditionally quantified via a model with two parameters, but only one of them is continually updated - the other is fixed. The new algorithm allows for breath-by-breath update of both parameters. This was made possible by leveraging a model describing the patient respiratory mechanics.

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Noninvasive estimation of alveolar pressure

Cited by 7 publications

References 8 publications

Tracking respiratory mechanics around natural breathing rates via variable ventilation

Tracking respiratory mechanics around natural breathing rates via variable ventilation

Model-Based Estimation of Inspiratory Effort Using Surface EMG

Two-parameter leak estimation in non-invasive ventilation

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