Adaptive SLICE method: an enhanced method to determine nonlinear dynamic respiratory system mechanics

Accuracy of the estimations of respiratory mechanics using an expiratory time constant in passive and active breathing conditions: a bench study

BACKGROUND

Respiratory mechanics monitoring provides useful information for guiding mechanical ventilation, but many measuring methods are inappropriate for awake patients. This study aimed to evaluate the accuracy of dynamic mechanics estimation using expiratory time constant (RC_exp) calculation during noninvasive pressure support ventilation (PSV) with air leak in different lung models.

METHODS

A Respironics V60 ventilator was connected to an active breathing simulator for modeling five profiles: normal adult, restrictive, mildly and severely obstructive, and mixed obstructive/restrictive. Inspiratory pressure support was adjusted to maintain tidal volumes (V_T), achieving 5.0, 7.0, and 10.0 ml/kg body weight. PEEP was set at 5 cmH₂O, and the back-up rate was 10 bpm. Measurements were conducted at system leaks of 25-28 L/min. RC_exp was estimated from the ratio at 75% exhaled V_T and flow rate, which was then used to determine respiratory system compliance (C_rs) and airway resistance (R_aw).

RESULTS

In non-obstructive conditions (R_aw ≤ 10 cmH₂O/L/s), the C_rs was overestimated in the PSV mode. Peak inspiratory and expiratory flow and V_T increased with PS levels, as calculated C_rs decreased. In passive breathing, the difference of C_rs between different V_T was no significant. Underestimations of inspiratory resistance and expiratory resistance were observed at V_T of 5.0 ml/kg. The difference was minimal at V_T of 7.0 ml/kg. During non-invasive PSV, the estimation of airway resistance with the RC_exp method was accurately at V_T of 7.0 ml/kg.

CONCLUSIONS

The difference between the calculated C_rs and the preset value was influenced by the volume, status and inspiratory effort in spontaneously breathing.

Accuracy of the dynamic signal analysis approach in respiratory mechanics during noninvasive pressure support ventilation: a bench study

OBJECTIVE

To evaluate the accuracy of respiratory mechanics using dynamic signal analysis during noninvasive pressure support ventilation (PSV).

METHODS

A Respironics V60 ventilator was connected to an active lung simulator to model normal, restrictive, obstructive, and mixed obstructive and restrictive profiles. The PSV was adjusted to maintain tidal volumes (V_T) that achieved 5.0, 7.0, and 10.0 mL/kg body weight, and the positive end-expiration pressure (PEEP) was set to 5 cmH₂O. Ventilator performance was evaluated by measuring the flow, airway pressure, and volume. The system compliance (C_rs) and airway resistance (inspiratory and expiratory resistance, R_insp and R_exp, respectively) were calculated.

RESULTS

Under active breathing conditions, the C_rs was overestimated in the normal and restrictive models, and it decreased with an increasing pressure support (PS) level. The R_insp calculated error was approximately 10% at 10.0 mL/kg of V_T, and similar results were obtained for the calculated R_exp at 7.0 mL/kg of V_T.

CONCLUSION

Using dynamic signal analysis, appropriate tidal volume was beneficial for R_rs, especially for estimating R_exp during assisted ventilation. The C_rs measurement was also relatively accurate in obstructive conditions.

Lung-protective mechanical ventilation for patients undergoing abdominal laparoscopic surgeries: a randomized controlled trial

Background

Pneumoperitoneum and Trendelenburg position in laparoscopic surgeries could contribute to postoperative pulmonary dysfunction. In recent years, intraoperative lung-protective mechanical ventilation (LPV) has been reportedly able to attenuate ventilator-induced lung injuries (VILI). Our objectives were to test the hypothesis that LPV could improve intraoperative oxygenation function, pulmonary mechanics and early postoperative atelectasis in laparoscopic surgeries.

Methods

In this randomized controlled clinical trial, 62 patients indicated for elective abdominal laparoscopic surgeries with an expected duration of greater than 2 h were randomly assigned to receive either lung-protective ventilation (LPV) with a tidal volume (Vt) of 7 ml kg^− 1 ideal body weight (IBW), 10 cmH₂O positive end-expiratory pressure (PEEP) combined with regular recruitment maneuvers (RMs) or conventional ventilation (CV) with a Vt of 10 ml kg^− 1 IBW, 0 cmH₂O in PEEP and no RMs. The primary endpoints were the changes in the ratio of PaO₂ to FiO₂ (P/F). The secondary endpoints were the differences between the two groups in PaO₂, alveolar-arterial oxygen gradient (A-aO₂), intraoperative pulmonary mechanics and the incidence of atelectasis detected on chest x-ray on the first postoperative day.

Results

In comparison to CV group, the intraoperative P/F and PaO₂ in LPV group were significantly higher while the intraoperative A-aO₂ was clearly lower. C_dyn and C_stat at all the intraoperative time points in LPV group were significantly higher compared to CV group (p < 0.05). There were no differences in the incidence of atelectasis on day one after surgery between the two groups.

Conclusions

Lung protective mechanical ventilation significantly improved intraoperative pulmonary oxygenation function and pulmonary compliance in patients experiencing various abdominal laparoscopic surgeries, but it could not ameliorate early postoperative atelectasis and oxygenation function on the first day after surgery.

Trial registration

https://www.clinicaltrials.gov/identifier: NCT04546932 (09/05/2020).

Optimising mechanical ventilation through model-based methods and automation

Mechanical ventilation (MV) is a core life-support therapy for patients suffering from respiratory failure or acute respiratory distress syndrome (ARDS). Respiratory failure is a secondary outcome of a range of injuries and diseases, and results in almost half of all intensive care unit (ICU) patients receiving some form of MV. Funding the increasing demand for ICU is a major issue and MV, in particular, can double the cost per day due to significant patient variability, over-sedation, and the large amount of clinician time required for patient management. Reducing cost in this area requires both a decrease in the average duration of MV by improving care, and a reduction in clinical workload. Both could be achieved by safely automating all or part of MV care via model-based dynamic systems modelling and control methods are ideally suited to address these problems. This paper presents common lung models, and provides a vision for a more automated future and explores predictive capacity of some current models. This vision includes the use of model-based methods to gain real-time insight to patient condition, improve safety through the forward prediction of outcomes to changes in MV, and develop virtual patients for in-silico design and testing of clinical protocols. Finally, the use of dynamic systems models and system identification to guide therapy for improved personalised control of oxygenation and MV therapy in the ICU will be considered. Such methods are a major part of the future of medicine, which includes greater personalisation and predictive capacity to both optimise care and reduce costs. This review thus presents the state of the art in how dynamic systems and control methods can be applied to transform this core area of ICU medicine.

Intraoperative protective mechanical ventilation for prevention of postoperative pulmonary complications: a comprehensive review of the role of tidal volume, positive end-expiratory pressure, and lung recruitment maneuvers

Postoperative pulmonary complications are associated with increased morbidity, length of hospital stay, and mortality after major surgery. Intraoperative lung-protective mechanical ventilation has the potential to reduce the incidence of postoperative pulmonary complications. This review discusses the relevant literature on definition and methods to predict the occurrence of postoperative pulmonary complication, the pathophysiology of ventilator-induced lung injury with emphasis on the noninjured lung, and protective ventilation strategies, including the respective roles of tidal volumes, positive end-expiratory pressure, and recruitment maneuvers. The authors propose an algorithm for protective intraoperative mechanical ventilation based on evidence from recent randomized controlled trials.

Time-varying respiratory system elastance: a physiological model for patients who are spontaneously breathing

BACKGROUND

Respiratory mechanics models can aid in optimising patient-specific mechanical ventilation (MV), but the applications are limited to fully sedated MV patients who have little or no spontaneously breathing efforts. This research presents a time-varying elastance (E(drs)) model that can be used in spontaneously breathing patients to determine their respiratory mechanics.

METHODS

A time-varying respiratory elastance model is developed with a negative elastic component (E(demand)), to describe the driving pressure generated during a patient initiated breathing cycle. Data from 22 patients who are partially mechanically ventilated using Pressure Support (PS) and Neurally Adjusted Ventilatory Assist (NAVA) are used to investigate the physiology relevance of the time-varying elastance model and its clinical potential. E(drs) of every breathing cycle for each patient at different ventilation modes are presented for comparison.

RESULTS

At the start of every breathing cycle initiated by patient, E(drs) is < 0. This negativity is attributed from the E(demand) due to a positive lung volume intake at through negative pressure in the lung compartment. The mapping of E(drs) trajectories was able to give unique information to patients' breathing variability under different ventilation modes. The area under the curve of E(drs) (AUCE(drs)) for most patients is > 25 cmH2Os/l and thus can be used as an acute respiratory distress syndrome (ARDS) severity indicator.

CONCLUSION

The E(drs) model captures unique dynamic respiratory mechanics for spontaneously breathing patients with respiratory failure. The model is fully general and is applicable to both fully controlled and partially assisted MV modes.

A patient-specific airway branching model for mechanically ventilated patients

BACKGROUND

Respiratory mechanics models have the potential to guide mechanical ventilation. Airway branching models (ABMs) were developed from classical fluid mechanics models but do not provide accurate models of in vivo behaviour. Hence, the ABM was improved to include patient-specific parameters and better model observed behaviour (ABMps).

METHODS

The airway pressure drop of the ABMps was compared with the well-accepted dynostatic algorithm (DSA) in patients diagnosed with acute respiratory distress syndrome (ARDS). A scaling factor (α) was used to equate the area under the pressure curve (AUC) from the ABMps to the AUC of the DSA and was linked to patient state.

RESULTS

The ABMps recorded a median α value of 0.58 (IQR: 0.54-0.63; range: 0.45-0.66) for these ARDS patients. Significantly lower α values were found for individuals with chronic obstructive pulmonary disease (P < 0.001).

CONCLUSION

The ABMps model allows the estimation of airway pressure drop at each bronchial generation with patient-specific physiological measurements and can be generated from data measured at the bedside. The distribution of patient-specific α values indicates that the overall ABM can be readily improved to better match observed data and capture patient condition.

Visualisation of time-varying respiratory system elastance in experimental ARDS animal models

BACKGROUND

Patients with acute respiratory distress syndrome (ARDS) risk lung collapse, severely altering the breath-to-breath respiratory mechanics. Model-based estimation of respiratory mechanics characterising patient-specific condition and response to treatment may be used to guide mechanical ventilation (MV). This study presents a model-based approach to monitor time-varying patient-ventilator interaction to guide positive end expiratory pressure (PEEP) selection.

METHODS

The single compartment lung model was extended to monitor dynamic time-varying respiratory system elastance, Edrs, within each breathing cycle. Two separate animal models were considered, each consisting of three fully sedated pure pietrain piglets (oleic acid ARDS and lavage ARDS). A staircase recruitment manoeuvre was performed on all six subjects after ARDS was induced. The Edrs was mapped across each breathing cycle for each subject.

RESULTS

Six time-varying, breath-specific Edrs maps were generated, one for each subject. Each Edrs map shows the subject-specific response to mechanical ventilation (MV), indicating the need for a model-based approach to guide MV. This method of visualisation provides high resolution insight into the time-varying respiratory mechanics to aid clinical decision making. Using the Edrs maps, minimal time-varying elastance was identified, which can be used to select optimal PEEP.

CONCLUSIONS

Real-time continuous monitoring of in-breath mechanics provides further insight into lung physiology. Therefore, there is potential for this new monitoring method to aid clinicians in guiding MV treatment. These are the first such maps generated and they thus show unique results in high resolution. The model is limited to a constant respiratory resistance throughout inspiration which may not be valid in some cases. However, trends match clinical expectation and the results highlight both the subject-specificity of the model, as well as significant inter-subject variability.

Monitoring of intratidal lung mechanics: a Graphical User Interface for a model-based decision support system for PEEP-titration in mechanical ventilation

In mechanical ventilation, a careful setting of the ventilation parameters in accordance with the current individual state of the lung is crucial to minimize ventilator induced lung injury. Positive end-expiratory pressure (PEEP) has to be set to prevent collapse of the alveoli, however at the same time overdistension should be avoided. Classic approaches of analyzing static respiratory system mechanics fail in particular if lung injury already prevails. A new approach of analyzing dynamic respiratory system mechanics to set PEEP uses the intratidal, volume-dependent compliance which is believed to stay relatively constant during one breath only if neither atelectasis nor overdistension occurs. To test the success of this dynamic approach systematically at bedside or in an animal study, automation of the computing steps is necessary. A decision support system for optimizing PEEP in form of a Graphical User Interface (GUI) was targeted. Respiratory system mechanics were analyzed using the gliding SLICE method. The resulting shapes of the intratidal compliance-volume curve were classified into one of six categories, each associated with a PEEP-suggestion. The GUI should include a graphical representation of the results as well as a quality check to judge the reliability of the suggestion. The implementation of a user-friendly GUI was successfully realized. The agreement between modelled and measured pressure data [expressed as root-mean-square (RMS)] tested during the implementation phase with real respiratory data from two patient studies was below 0.2 mbar for data taken in volume controlled mode and below 0.4 mbar for data taken in pressure controlled mode except for two cases with RMS < 0.6 mbar. Visual inspections showed, that good and medium quality data could be reliably identified. The new GUI allows visualization of intratidal compliance-volume curves on a breath-by-breath basis. The automatic categorisation of curve shape into one of six shape-categories provides the rational decision-making model for PEEP-titration.

Optimizing positive end-expiratory pressure by oscillatory mechanics minimizes tidal recruitment and distension: an experimental study in a lavage model of lung injury

Introduction

It is well established that during mechanical ventilation of patients with acute respiratory distress syndrome cyclic recruitment/derecruitment and overdistension are potentially injurious for lung tissues. We evaluated whether the forced oscillation technique (FOT) could be used to guide the ventilator settings in order to minimize cyclic lung recruitment/derecruitment and cyclic mechanical stress in an experimental model of acute lung injury.

Methods

We studied six pigs in which lung injury was induced by bronchoalveolar lavage. The animals were ventilated with a tidal volume of 6 ml/kg. Forced oscillations at 5 Hz were superimposed on the ventilation waveform. Pressure and flow were measured at the tip and at the inlet of the endotracheal tube respectively. Respiratory system reactance (Xrs) was computed from the pressure and flow signals and expressed in terms of oscillatory elastance (E_X5). Positive end-expiratory pressure (PEEP) was increased from 0 to 24 cm H₂O in steps of 4 cm H₂O and subsequently decreased from 24 to 0 in steps of 2 cm H₂O. At each PEEP step CT scans and E_X5 were assessed at end-expiration and end-inspiration.

Results

During deflation the relationship between both end-expiratory and end-inspiratory E_X5 and PEEP was a U-shaped curve with minimum values at PEEP = 13.4 ± 1.0 cm H₂O (mean ± SD) and 13.0 ± 1.0 cm H₂O respectively. E_X5 was always higher at end-inspiration than at end-expiration, the difference between the average curves being minimal at 12 cm H₂O. At this PEEP level, CT did not show any substantial sign of intra-tidal recruitment/derecruitment or expiratory lung collapse.

Conclusions

Using FOT it was possible to measure E_X5 both at end-expiration and at end-inspiration. The optimal PEEP strategy based on end-expiratory E_X5 minimized intra-tidal recruitment/derecruitment as assessed by CT, and the concurrent attenuation of intra-tidal variations of E_X5 suggests that it may also minimize tidal mechanical stress.

Assessment of a volume-dependent dynamic respiratory system compliance in ALI/ARDS by pooling breathing cycles

New methods were developed to calculate the volume-dependent dynamic respiratory system compliance (C(rs)) in mechanically ventilated patients. Due to noise in respiratory signals and different characteristics of the methods, their results can considerably differ. The aim of the study was to establish a practical procedure to validate the estimation of intratidal dynamic C(rs). A total of 28 patients from intensive care units of eight German university hospitals with acute lung injury (ALI) and acute respiratory distress syndrome (ARDS) were studied retrospectively. Dynamic volume-dependent C(rs) was determined during ongoing mechanical ventilation with the SLICE method, dynostatic algorithm and adaptive slice method. Conventional two-point compliance C(2P) was calculated for comparison. A number of consecutive breathing cycles were pooled to reduce noise in the respiratory signals. C(rs)-volume curves produced with different methods converged when the number of pooling cycles increased (n ≥ 7). The mean volume-dependent C(rs) of 20 breaths was highly correlated with mean C(2P) (C(2P,mean) = 0.945 × C(rs,mean) - 0.053, r(2) = 0.968, p < 0.0001). The Bland-Altman analysis indicated that C(2P,mean) was lower than C(rs,mean) (-2.4 ± 6.4 ml cm(-1) H(2)O, mean bias ± 2 SD), but not significant according to the paired t-test (p > 0.05). Methods for analyzing dynamic respiratory mechanics are sensitive to noise and will converge to a unique solution when the number of pooled cycles increases. Under steady-state conditions, assessment of the volume-dependent C(rs) in ALI/ARDS patients can be validated by pooling respiratory data of consecutive breaths regardless of which method is applied. Confidence in dynamic C(rs) determination may be increased with the proposed pooling.