NAVA enhances tidal volume and diaphragmatic electro-myographic activity matching: a Range90 analysis of supply and demand

Virtual patient with temporal evolution for mechanical ventilation trial studies: A stochastic model approach

BACKGROUND AND OBJECTIVE

Healthcare datasets are plagued by issues of data scarcity and class imbalance. Clinically validated virtual patient (VP) models can provide accurate in-silico representations of real patients and thus a means for synthetic data generation in hospital critical care settings. This research presents a realistic, time-varying mechanically ventilated respiratory failure VP profile synthesised using a stochastic model.

METHODS

A stochastic model was developed using respiratory elastance (Ers) data from two clinical cohorts and averaged over 30-minute time intervals. The stochastic model was used to generate future Ers data based on current Ers values with added normally distributed random noise. Self-validation of the VPs was performed via Monte Carlo simulation and retrospective Ers profile fitting. A stochastic VP cohort of temporal Ers evolution was synthesised and then compared to an independent retrospective patient cohort data in a virtual trial across several measured patient responses, where similarity of profiles validates the realism of stochastic model generated VP profiles.

RESULTS

A total of 120,000 3-hour VPs for pressure control (PC) and volume control (VC) ventilation modes are generated using stochastic simulation. Optimisation of the stochastic simulation process yields an ideal noise percentage of 5-10% and simulation iteration of 200,000 iterations, allowing the simulation of a realistic and diverse set of Ers profiles. Results of self-validation show the retrospective Ers profiles were able to be recreated accurately with a mean squared error of only 0.099 [0.009-0.790]% for the PC cohort and 0.051 [0.030-0.126]% for the VC cohort. A virtual trial demonstrates the ability of the stochastic VP cohort to capture Ers trends within and beyond the retrospective patient cohort providing cohort-level validation.

CONCLUSION

VPs capable of temporal evolution demonstrate feasibility for use in designing, developing, and optimising bedside MV guidance protocols through in-silico simulation and validation. Overall, the temporal VPs developed using stochastic simulation alleviate the need for lengthy, resource intensive, high cost clinical trials, while facilitating statistically robust virtual trials, ultimately leading to improved patient care and outcomes in mechanical ventilation.

Low-cost structured light imaging of regional volume changes for use in assessing mechanical ventilation

BACKGROUND

Optimal setting of mechanical ventilators is critical for improving outcomes. Accurate, predictive lung mechanics models are effective in optimizing MV settings, but only at a global level as they cannot estimate regional lung volume ventilation to assess the potential of local distension or under-ventilation. This study presents a low-cost structured light system for non-contact high resolution chest motion measurement to estimate regional lung volume changes.

METHODS

The system consists of a structured light projector and two cameras. A new pattern is designed to extract motion from sub-regions of the chest surface, and an efficient feature is proposed to provide a fast and accurate correspondence matching between two views. Reconstruction of 3D surface points is based on the matched points and stereo method. Asymmetric distribution of tidal volume into left and right lungs is estimated based on reconstructed regional chest expansion. A proof-of-concept experiment using a dummy model and two test lungs connected to a ventilator to provide differential chest expansion is conducted under tidal volumes of 400 ml, 500 ml and 600 ml, with results compared to the widely-used SURF and ORB methods.

RESULTS

Compared to the SURF and ORB methods, the proposed method is more computationally efficient with ∼40% less computational time cost, and higher accuracy for dense point correspondence. Finally, the proposed method estimated the region lung volumes with the maximum error of 8 ml under 600 ml tidal volume, indicating a good accuracy.

CONCLUSIONS

Surface reconstruction results in a proof-of-concept experiment with differential chest expansion show good performance for the proposed pattern and method in extracting the key information for regional chest expansion. The proposed method is generalizable, with potential for use in other applications.

Model-based estimation of negative inspiratory driving pressure in patients receiving invasive NAVA mechanical ventilation

BACKGROUND

Optimisation of mechanical ventilation (MV) and weaning requires insight into underlying patient breathing effort. Current identifiable models effectively describe lung mechanics, such as elastance (E) and resistance (R) at the bedside in sedated patients, but are less effective when spontaneous breathing is present. This research derives and regularises a single compartment model to identify patient-specific inspiratory effort.

METHODS

Constrained second-order b-spline basis functions (knot width 0.05 s) are used to describe negative inspiratory drive (Pp, cmH₂O) as a function of time. Breath-breath Pp are identified with single E and R values over inspiration and expiration from n = 20 breaths for N = 22 patients on NAVA ventilation. Pp is compared to measured electrical activity of the diaphragm (Eadi) and published results.

RESULTS

Average per-patient root-mean-squared model fit error was (median [interquartile range, IQR]) 0.9 [0.6-1.3] cmH₂O, and average per-patient median Pp was -3.9 [-4.5- -3.0] cmH₂O, with range -7.9 - -1.9 cmH₂O. Per-patient E and R were 16.4 [13.6-21.8] cmH₂O/L and 9.2 [6.4-13.1] cmH₂O.s/L, respectively. Most patients showed an inspiratory volume threshold beyond which Pp started to return to baseline, and Pp at peak Eadi (end-inspiration) was often strongly correlated with peak Eadi (R²=0.25-0.86). Similarly, average transpulmonary pressure was consistent breath-breath in most patients, despite differences in peak Eadi and thus peak airway pressure.

CONCLUSIONS

The model-based inspiratory effort aligns with electrical muscle activity and published studies showing neuro-muscular decoupling as a function of pressure and/or volume. Consistency in coupling/dynamics were patient-specific. Quantification of patient and ventilator work of breathing contributions may aid optimisation of MV modes and weaning.

Neurally adjusted ventilatory assist versus pressure support ventilation: a randomized controlled feasibility trial performed in patients at risk of prolonged mechanical ventilation

BACKGROUND

The clinical effectiveness of neurally adjusted ventilatory assist (NAVA) has yet to be demonstrated, and preliminary studies are required. The study aim was to assess the feasibility of a randomized controlled trial (RCT) of NAVA versus pressure support ventilation (PSV) in critically ill adults at risk of prolonged mechanical ventilation (MV).

METHODS

An open-label, parallel, feasibility RCT (n = 78) in four ICUs of one university-affiliated hospital. The primary outcome was mode adherence (percentage of time adherent to assigned mode), and protocol compliance (binary-≥ 65% mode adherence). Secondary exploratory outcomes included ventilator-free days (VFDs), sedation, and mortality.

RESULTS

In the 72 participants who commenced weaning, median (95% CI) mode adherence was 83.1% (64.0-97.1%) and 100% (100-100%), and protocol compliance was 66.7% (50.3-80.0%) and 100% (89.0-100.0%) in the NAVA and PSV groups respectively. Secondary outcomes indicated more VFDs to D28 (median difference 3.0 days, 95% CI 0.0-11.0; p = 0.04) and fewer in-hospital deaths (relative risk 0.5, 95% CI 0.2-0.9; p = 0.032) for NAVA. Although overall sedation was similar, Richmond Agitation and Sedation Scale (RASS) scores were closer to zero in NAVA compared to PSV (p = 0.020). No significant differences were observed in duration of MV, ICU or hospital stay, or ICU, D28, and D90 mortality.

CONCLUSIONS

This feasibility trial demonstrated good adherence to assigned ventilation mode and the ability to meet a priori protocol compliance criteria. Exploratory outcomes suggest some clinical benefit for NAVA compared to PSV. Clinical effectiveness trials of NAVA are potentially feasible and warranted.

TRIAL REGISTRATION

ClinicalTrials.gov, NCT01826890. Registered 9 April 2013.

Biomedical engineer's guide to the clinical aspects of intensive care mechanical ventilation

Background

Mechanical ventilation is an essential therapy to support critically ill respiratory failure patients. Current standards of care consist of generalised approaches, such as the use of positive end expiratory pressure to inspired oxygen fraction (PEEP–FiO₂) tables, which fail to account for the inter- and intra-patient variability between and within patients. The benefits of higher or lower tidal volume, PEEP, and other settings are highly debated and no consensus has been reached. Moreover, clinicians implicitly account for patient-specific factors such as disease condition and progression as they manually titrate ventilator settings. Hence, care is highly variable and potentially often non-optimal. These conditions create a situation that could benefit greatly from an engineered approach. The overall goal is a review of ventilation that is accessible to both clinicians and engineers, to bridge the divide between the two fields and enable collaboration to improve patient care and outcomes. This review does not take the form of a typical systematic review. Instead, it defines the standard terminology and introduces key clinical and biomedical measurements before introducing the key clinical studies and their influence in clinical practice which in turn flows into the needs and requirements around how biomedical engineering research can play a role in improving care. Given the significant clinical research to date and its impact on this complex area of care, this review thus provides a tutorial introduction around the review of the state of the art relevant to a biomedical engineering perspective.

Discussion

This review presents the significant clinical aspects and variables of ventilation management, the potential risks associated with suboptimal ventilation management, and a review of the major recent attempts to improve ventilation in the context of these variables. The unique aspect of this review is a focus on these key elements relevant to engineering new approaches. In particular, the need for ventilation strategies which consider, and directly account for, the significant differences in patient condition, disease etiology, and progression within patients is demonstrated with the subsequent requirement for optimal ventilation strategies to titrate for patient- and time-specific conditions.

Conclusion

Engineered, protective lung strategies that can directly account for and manage inter- and intra-patient variability thus offer great potential to improve both individual care, as well as cohort clinical outcomes.

Assessing mechanical ventilation asynchrony through iterative airway pressure reconstruction

BACKGROUND AND OBJECTIVE

Respiratory mechanics estimation can be used to guide mechanical ventilation (MV) but is severely compromised when asynchronous breathing occurs. In addition, asynchrony during MV is often not monitored and little is known about the impact or magnitude of asynchronous breathing towards recovery. Thus, it is important to monitor and quantify asynchronous breathing over every breath in an automated fashion, enabling the ability to overcome the limitations of model-based respiratory mechanics estimation during asynchronous breathing ventilation.

METHODS

An iterative airway pressure reconstruction (IPR) method is used to reconstruct asynchronous airway pressure waveforms to better match passive breathing airway waveforms using a single compartment model. The reconstructed pressure enables estimation of respiratory mechanics of airway pressure waveform essentially free from asynchrony. Reconstruction enables real-time breath-to-breath monitoring and quantification of the magnitude of the asynchrony (M_Asyn).

RESULTS AND DISCUSSION

Over 100,000 breathing cycles from MV patients with known asynchronous breathing were analyzed. The IPR was able to reconstruct different types of asynchronous breathing. The resulting respiratory mechanics estimated using pressure reconstruction were more consistent with smaller interquartile range (IQR) compared to respiratory mechanics estimated using asynchronous pressure. Comparing reconstructed pressure with asynchronous pressure waveforms quantifies the magnitude of asynchronous breathing, which has a median value M_Asyn for the entire dataset of 3.8%.

CONCLUSION

The iterative pressure reconstruction method is capable of identifying asynchronous breaths and improving respiratory mechanics estimation consistency compared to conventional model-based methods. It provides an opportunity to automate real-time quantification of asynchronous breathing frequency and magnitude that was previously limited to invasively method only.

Neurally adjusted ventilatory assist feasibility during anaesthesia: A randomised crossover study of two anaesthetics in a large animal model

BACKGROUND

Spontaneous breathing during mechanical ventilation improves gas exchange by redistribution of ventilation to dependent lung regions. Neurally adjusted ventilatory assist (NAVA) supports spontaneous breathing in proportion to the electrical activity of the diaphragm (EAdi). NAVA has never been used in the operating room and no studies have systematically addressed the influence of different anaesthetic drugs on EAdi.

OBJECTIVES

The aim of this study was to test the feasibility of NAVA under sedation and anaesthesia with two commonly used anaesthetics, sevoflurane and propofol, with and without remifentanil, and to study their effects on EAdi and breathing mechanics.

DESIGN

A crossover study with factorial design of NAVA during sedation and anaesthesia in pigs.

SETTING

University basic science laboratory in Uppsala, Sweden, from March 2009 to February 2011.

ANIMALS

Nine juvenile pigs were used for the experiment.

INTERVENTIONS

The lungs were ventilated using NAVA while the animals were sedated and anaesthetised with continuous low-dose ketamine combined with sevoflurane and propofol, with and without remifentanil.

MAIN OUTCOME MEASURES

During the last 5 min of each study period (total eight steps) EAdi, breathing pattern, blood gas analysis, neuromechanical efficiency (NME) and neuroventilatory efficiency (NVE) during NAVA were determined.

RESULTS

EAdi was preserved and normoventilation was reached with both sevoflurane and propofol during sedation as well as anaesthesia. Tidal volume (Vt) was significantly lower with sevoflurane anaesthesia than with propofol. NME was significantly higher with sevoflurane than with propofol during anaesthesia with and without remifentanil. NVE was significantly higher with sevoflurane than with propofol during sedation and anaesthesia.

CONCLUSION

NAVA is feasible during ketamine-propofol and ketamine-sevoflurane anaesthesia in pigs. Sevoflurane promotes lower Vt, and affects NME and NVE less than propofol. Our data warrant studies of NAVA in humans undergoing anaesthesia.

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.

Effects of Neurally Adjusted Ventilatory Assist (NAVA) levels in non-invasive ventilated patients: titrating NAVA levels with electric diaphragmatic activity and tidal volume matching

BACKGROUND

Neurally adjusted ventilatory assist (NAVA) delivers pressure in proportion to diaphragm electrical activity (Eadi). However, each patient responds differently to NAVA levels. This study aims to examine the matching between tidal volume (Vt) and patients' inspiratory demand (Eadi), and to investigate patient-specific response to various NAVA levels in non-invasively ventilated patients.

METHODS

12 patients were ventilated non-invasively with NAVA using three different NAVA levels. NAVA100 was set according to the manufacturer's recommendation to have similar peak airway pressure as during pressure support. NAVA level was then adjusted ±50% (NAVA50, NAVA150). Airway pressure, flow and Eadi were recorded for 15 minutes at each NAVA level. The matching of Vt and integral of Eadi (ʃEadi) were assessed at the different NAVA levels. A metric, Range90, was defined as the 5-95% range of Vt/ʃEadi ratio to assess matching for each NAVA level. Smaller Range90 values indicated better matching of supply to demand.

RESULTS

Patients ventilated at NAVA50 had the lowest Range90 with median 25.6 uVs/ml [Interquartile range (IQR): 15.4-70.4], suggesting that, globally, NAVA50 provided better matching between ʃEadi and Vt than NAVA100 and NAVA150. However, on a per-patient basis, 4 patients had the lowest Range90 values in NAVA100, 1 patient at NAVA150 and 7 patients at NAVA50. Robust coefficient of variation for ʃEadi and Vt were not different between NAVA levels.

CONCLUSIONS

The patient-specific matching between ʃEadi and Vt was variable, indicating that to obtain the best possible matching, NAVA level setting should be patient specific. The Range90 concept presented to evaluate Vt/ʃEadi is a physiologic metric that could help in individual titration of NAVA level.