Quantifying neonatal pulmonary mechanics in mechanical ventilation

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.

An identifiable model of lung mechanics to diagnose and monitor COPD

BACKGROUND

Current methods to diagnose and monitor COPD employ spirometry as the gold standard to identify lung function reduction with reduced forced expiratory volume (FEV₁)/vital capacity (VC) ratio. Current methods utilise linear assumptions regarding airway resistance, where nonlinear resistance modelling may provide rapid insight into patient specific condition and disease progression. This study examines model-based expiratory resistance in healthy lungs and those with progressively more severe COPD.

METHODS

Healthy and COPD pressure (P)[cmH₂O] and flow (Q)[L/s] data is obtained from the literature, and 5 intermediate levels of COPD and responses are created to simulate COPD progression and assess model-based metric resolution. Linear and nonlinear single compartment models are used to identify changes in inspiratory (R_1,insp) and linear (R_1,exp)/nonlinear (R₂Φ) expiratory resistance with disease severity and over the course of expiration.

RESULTS

R_1,insp increases from 2.1 to 7.3 cmH₂O/L/s, R_1,exp increases from 2.4 to 10.0 cmH₂O/L/s with COPD severity. Nonlinear R₂Φ increases (mean R₂Φ: 2.5 cmH₂O/L/s (healthy) to 24.4 cmH₂O/L/s (COPD)), with increasing end-expiratory nonlinearity as COPD severity increases.

CONCLUSION

Expiratory resistance is increasingly highly nonlinear with COPD severity. These results show a simple, nonlinear model can capture fundamental COPD dynamics and progression from regular breathing data, and such an approach may be useful for patient-specific diagnosis and monitoring.

Predicting mechanically ventilated patients future respiratory system elastance - A stochastic modelling approach

BACKGROUND AND OBJECTIVE

Respiratory mechanics of mechanically ventilated patients evolve significantly with time, disease state and mechanical ventilation (MV) treatment. Existing deterministic data prediction methods fail to comprehensively describe the multiple sources of heterogeneity of biological systems. This research presents two respiratory mechanics stochastic models with increased prediction accuracy and range, offering improved clinical utility in MV treatment.

METHODS

Two stochastic models (SM2 and SM3) were developed using retrospective patient respiratory elastance (E_rs) from two clinical cohorts which were averaged over time intervals of 10 and 30 min respectively. A stochastic model from a previous study (SM1) was used to benchmark performance. The stochastic models were clinically validated on an independent retrospective clinical cohort of 14 patients. Differences in predictive ability were evaluated using the difference in percentile lines and cumulative distribution density (CDD) curves.

RESULTS

Clinical validation shows all three models captured more than 98% (median) of future E_rs data within the 5th - 95th percentile range. Comparisons of stochastic model percentile lines reported a maximum mean absolute percentage difference of 5.2%. The absolute differences of CDD curves were less than 0.25 in the ranges of 5 < E_rs (cmH₂O/L) < 85, suggesting similar predictive capabilities within this clinically relevant E_rs range.

CONCLUSION

The new stochastic models significantly improve prediction, clinical utility, and thus feasibility for synchronisation with clinical interventions. Paired with other MV protocols, the stochastic models developed can potentially form part of decision support systems, providing guided, personalised, and safe MV treatment.

Model-based patient matching for in-parallel pressure-controlled ventilation

BACKGROUND

Surges of COVID-19 infections have led to insufficient supply of mechanical ventilators (MV), resulting in rationing of MV care. In-parallel, co-mechanical ventilation (Co-MV) of multiple patients is a potential solution. However, due to lack of testing, there is currently no means to match ventilation requirements or patients, with no guidelines to date. In this research, we have developed a model-based method for patient matching for pressure control mode MV.

METHODS

The model-based method uses a single-compartment lung model (SCM) to simulate the resultant tidal volume of patient pairs at a set ventilation setting. If both patients meet specified safe ventilation criteria under similar ventilation settings, the actual mechanical ventilator settings for Co-MV are determined via simulation using a double-compartment lung model (DCM). This method allows clinicians to analyse Co-MV in silico, before clinical implementation.

RESULTS

The proposed method demonstrates successful patient matching and MV setting in a model-based simulation as well as good discrimination to avoid mismatched patient pairs. The pairing process is based on model-based, patient-specific respiratory mechanics identified from measured data to provide useful information for guiding care. Specifically, the matching is performed via estimation of MV delivered tidal volume (mL/kg) based on patient-specific respiratory mechanics. This information can provide insights for the clinicians to evaluate the subsequent effects of Co-MV. In addition, it was also found that Co-MV patients with highly restrictive respiratory mechanics and obese patients must be performed with extra care.

CONCLUSION

This approach allows clinicians to analyse patient matching in a virtual environment without patient risk. The approach is tested in simulation, but the results justify the necessary clinical validation in human trials.

Protocol conception for safe selection of mechanical ventilation settings for respiratory failure Patients

BACKGROUND AND OBJECTIVE

Mechanical ventilation is the primary form of care provided to respiratory failure patients. Limited guidelines and conflicting results from major clinical trials means selection of mechanical ventilation settings relies heavily on clinician experience and intuition. Determining optimal mechanical ventilation settings is therefore difficult, where non-optimal mechanical ventilation can be deleterious. To overcome these difficulties, this research proposes a model-based method to manage the wide range of possible mechanical ventilation settings, while also considering patient-specific conditions and responses.

METHODS

This study shows the design and development of the "VENT" protocol, which integrates the single compartment linear lung model with clinical recommendations from landmark studies, to aid clinical decision-making in selecting mechanical ventilation settings. Using retrospective breath data from a cohort of 24 patients, 3,566 and 2,447 clinically implemented VC and PC settings were extracted respectively. Using this data, a VENT protocol application case study and clinical comparison is performed, and the prediction accuracy of the VENT protocol is validated against actual measured outcomes of pressure and volume.

RESULTS

The study shows the VENT protocols' potential use in narrowing an overwhelming number of possible mechanical ventilation setting combinations by up to 99.9%. The comparison with retrospective clinical data showed that only 33% and 45% of clinician settings were approved by the VENT protocol. The unapproved settings were mainly due to exceeding clinical recommended settings. When utilising the single compartment model in the VENT protocol for forecasting peak pressures and tidal volumes, median [IQR] prediction error values of 0.75 [0.31 - 1.83] cmH₂O and 0.55 [0.19 - 1.20] mL/kg were obtained.

CONCLUSIONS

Comparing the proposed protocol with retrospective clinically implemented settings shows the protocol can prevent harmful mechanical ventilation setting combinations for which clinicians would be otherwise unaware. The VENT protocol warrants a more detailed clinical study to validate its potential usefulness in a clinical setting.

Quantifying neonatal patient effort using non-invasive model-based methods

Patient-specific spontaneous breathing effort (SB) is common in invasively mechanically ventilated (MV) adult patients, and especially common in preterm neonates who are not typically sedated. However, there is no proven, ethically feasible and non-invasive method to quantify SB effort in neonates, creating the potential for model-based measures. Lung mechanics and SB effort are segregated using a basis function model to identify passive lung mechanics, and an additional time-varying elastance model to identify patient-specific SB effort and asynchrony as negative and positive added elastances, respectively. Data from ten preterm neonates on standard MV care in the neonatal intensive care unit (NICU) are used to assess this model-based approach, using area under the curve (AUC) for positive (asynchrony) and negative (SB effort) time-varying elastance. Median [interquartile-range (IQR)] of passive pulmonary lung elastance was 3.82 [2.09-5.80] cmH₂O/ml. Median [IQR] AUC quantified SB effort was -0.32 [-0.43--0.12]cmH₂O/ml. AUC quantified asynchrony was 0.00 [0.00-0.01]cmH₂O/ml, and affected 28% of the 25,287 total breaths. This proof of concept model-based approach provides a non-invasive, computationally straightforward, and thus clinically feasible means to quantify patient-specific spontaneous breathing effort and asynchrony.

Stochastic Modelling of Respiratory System Elastance for Mechanically Ventilated Respiratory Failure Patients

While lung protective mechanical ventilation (MV) guidelines have been developed to avoid ventilator-induced lung injury (VILI), a one-size-fits-all approach cannot benefit every individual patient. Hence, there is significant need for the ability to provide patient-specific MV settings to ensure safety, and optimise patient care. Model-based approaches enable patient-specific care by identifying time-varying patient-specific parameters, such as respiratory elastance, Ers, to capture inter- and intra-patient variability. However, patient-specific parameters evolve with time, as a function of disease progression and patient condition, making predicting their future values crucial for recommending patient-specific MV settings. This study employs stochastic modelling to predict future Ers values using retrospective patient data to develop and validate a model indicating future intra-patient variability of Ers. Cross validation results show stochastic modelling can predict future elastance ranges with 92.59 and 68.56% of predicted values within the 5-95% and the 25-75% range, respectively. This range can be used to ensure patients receive adequate minute ventilation should elastance rise and minimise the risk of VILI should elastance fall. The results show the potential for model-based protocols using stochastic model prediction of future Ers values to provide safe and patient-specific MV. These results warrant further investigation to validate its clinical utility.

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.

Mechanically ventilated premature babies have sex differences in specific elastance: A pilot study

OBJECTIVES

A pilot study to compare pulmonary mechanics in a neonatal intensive care unit (NICU) cohort, specifically, comparing lung elastance between male and female infants in the NICU.

HYPOTHESIS

Anecdotally, male infants are harder to ventilate than females. We hypothesize that males have higher model-based elastance (converse: lower specific compliance) compared to females, reflecting underlying stiffer lungs.

STUDY DESIGN

A clinically validated, single-compartment model is used to identify specific elastance (inverse of specific compliance) and resistance for each breath. Specific elastance accounts for weight differences when comparing male and female infants. Relative percent breath-to-breath variability (%ΔE) in specific elastance is also compared. Level of asynchrony was also determined.

PATIENT-SUBJECT SELECTION

Ten invasively mechanically ventilated patients from Christchurch Women's Hospital.

METHODOLOGY

Airway pressure and flow data from 10 invasive mechanical ventilation (MV) infants from Christchurch Women's Hospital Neonatal Intensive Care Unit, New Zealand was prospectively recorded under standard MV care. Model-based specific elastance and resistance are identified for each breath, as well as relative percent breath-to-breath variability (%ΔE) in specific elastance.

RESULTS

Male infants overall had higher specific elastance compared to females infants (P ≤ .01), with median (interquartile range) for males of 1.91 (1.33-2.48) cmH₂ O·kg/mL compared to 1.31 (0.86-2.02) cmH₂ O·kg/mL in females. Male infants had lower variability with %ΔE of -0.03 (-7.56 to 8.01)% vs female infants of -0.59 (12.56-12.86)%. Males had 14.75% asynchronous breaths whereas females had 17.54%.

CONCLUSION

Overall, males had higher specific elastance and correspondingly lower breath-to-breath variability. These results indicate male and female infants may require different MV settings, mode, and monitoring.