Prediction of high airway pressure using a non-linear autoregressive model of pulmonary mechanics

Effects of training flights of combat jet pilots on parameters of airway function, diffusing capacity and systemic oxidative stress, and their association with flight parameters

BACKGROUND

Fighter aircraft pilots are regularly exposed to physiological challenges from high acceleration (Gz) forces, as well as increased breathing pressure and oxygen supply in the support systems. We studied whether effects on the lung and systemic oxidative stress were detectable after real training flights comprising of a wide variety of exposure conditions, and their combinations.

METHODS

Thirty-five pilots of the German Air Force performed 145 flights with the Eurofighter Typhoon. Prior to and after flight lung diffusing capacity for carbon monoxide (DLCO) and nitric oxide (DLNO), alveolar volume (VA), and diffusing capacities per volume (KCO, KNO) were assessed. In addition, the fractional concentration of exhaled nitric oxide (FeNO) was determined, and urine samples for the analysis of molecular species related to 8-hydroxy-2'-deoxyguanosine (8-OHdG) were taken. For statistical analysis, mixed ANOVA models were used.

RESULTS

DLNO, DLCO, KNO, KCO and VA were reduced (p < 0.001) after flights, mean ± SD changes being 2.9 ± 5.0, 3.2 ± 5.2, 1.5 ± 3.7, 1.9 ± 3.7 and 1.4 ± 3.1%, respectively, while FeNO decreased by 11.1% and the ratio of 8-OHdG to creatinine increased by 15.7 ± 37.8%. The reductions of DLNO (DLCO) were smaller (p < 0.001) than those of KNO (KCO). In repeated flights on different days, baseline values were restored. Amongst various flight parameters comprising Gz-forces and/or being indicative of positive pressure breathing and oxygenation support, the combination of long flight duration and high altitude appeared to be linked to greater changes in DLNO and DLCO.

CONCLUSIONS

The pattern of reductions in diffusing capacities suggests effects arising from atelectasis and increased diffusion barrier, without changes in capillary blood volume. The decrease in exhaled endogenous NO suggests bronchial mucosal irritation and/or local oxidative stress, and the increase in urinary oxidized guanosine species suggests systemic oxidative stress. Although changes were small and not clinically relevant, their presence demonstrated physiological effects of real training flights in a modern 4th generation fighter jet.

Pulmonary response prediction through personalized basis functions in a virtual patient model

BACKGROUND AND OBJECTIVE

Recruitment maneuvers with subsequent positive-end-expiratory-pressure (PEEP) have proven effective in recruiting lung volume and preventing alveoli collapse. However, determining a safe, effective, and patient-specific PEEP is not standardized, and this more optimal PEEP level evolves with patient condition, requiring personalised monitoring and care approaches to maintain optimal ventilation settings.

METHODS

This research examines 3 physiologically relevant basis function sets (exponential, parabolic, cumulative) to enable better prediction of elastance evolution for a virtual patient or digital twin model of MV lung mechanics, including novel elements to model and predict distension elastance. Prediction accuracy and robustness are validated against recruitment maneuver data from 18 volume-controlled ventilation (VCV) patients at 7 different baseline PEEP levels (0 to 12 cmH2O) and 14 pressure-controlled ventilation (PCV) patients at 4 different baseline PEEP levels (6 to 12 cmH2O), yielding 623 and 294 prediction cases, respectively. Predictions were made up to 12 cmH2O of added PEEP ahead, covering 6 × 2 cmH2O PEEP steps.

RESULTS

The 3 basis function sets yield median absolute peak inspiratory pressure (PIP) prediction error of 1.63 cmH2O for VCV patients, and median peak inspiratory volume (PIV) prediction error of 0.028 L for PCV patients. The exponential basis function set yields a better trade-off of overall performance across VCV and PCV prediction than parabolic and cumulative basis function sets from other studies. Comparing predicted and clinically measured distension prediction in VCV demonstrated consistent, robust high accuracy with R2 = 0.90-0.95.

CONCLUSIONS

The results demonstrate recruitment mechanics are best captured by an exponential basis function across different mechanical ventilation modes, matching physiological expectations, and accurately capture, for the first time, distension mechanics to within 5-10 % accuracy. Enabling the risk of lung injury to be predicted before changing ventilator settings. The overall outcomes significantly extend and more fully validate this digital twin or virtual mechanical ventilation patient model.

Over-distension prediction via hysteresis loop analysis and patient-specific basis functions in a virtual patient model

BACKGROUND AND OBJECTIVE

Recruitment maneuvers (RMs) with subsequent positive-end-expiratory-pressure (PEEP) have proven effective in recruiting lung volume and preventing alveolar collapse. However, a suboptimal PEEP could induce undesired injury in lungs by insufficient or excessive breath support. Thus, a predictive model for patient response under PEEP changes could improve clinical care and lower risks.

METHODS

This research adds novel elements to a virtual patient model to identify and predict patient-specific lung distension to optimise and personalise care. Model validity and accuracy are validated using data from 18 volume-controlled ventilation (VCV) patients at 7 different baseline PEEP levels (0-12cmH₂O), yielding 623 prediction cases. Predictions were made up to ΔPEEP = 12cmH₂O ahead covering 6x2cmH₂O PEEP steps.

RESULTS

Using the proposed lung distension model, 90% of absolute peak inspiratory pressure (PIP) prediction errors compared to clinical measurement are within 3.95cmH₂O, compared with 4.76cmH₂O without this distension term. Comparing model-predicted and clinically measured distension had high correlation increasing to R² = 0.93-0.95 if maximum ΔPEEP ≤ 6cmH₂O. Predicted dynamic functional residual capacity (V_frc) changes as PEEP rises yield 0.013L median prediction error for both prediction groups and overall R² of 0.84.

CONCLUSIONS

Overall results demonstrate nonlinear distension mechanics are accurately captured in virtual lung mechanics patients for mechanical ventilation, for the first time. This result can minimise the risk of lung injury by predicting its potential occurrence of distension before changing ventilator settings. The overall outcomes significantly extend and more fully validate this virtual mechanical ventilation patient model.

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.

Airway Fibroblast Secretory Products Enhance Cell Migration

Background:

The nasal fibroblast secretome, which includes various cytokines, chemokines, and growth factors, promotes cell migration. Currently, the proteomics of airway fibroblast (AF) conditioned medium (AFCM) are being actively studied.

Objective:

This study was aimed at profiling and identifying the AF secreted proteins that can enhance wound healing of the airway epithelium and predict the potential pathway involved.

Methods:

Airway epithelial cells (AECs) and AFs were isolated from redundant human nasal turbinate and cultured. AFCM was collected by culturing the AFs either with serum-free airway epithelium basal medium (AECM) or with serum-free F12:DMEM (FDCM). For evaluating cell migration, the AECs were supplemented with airway epithelium medium and defined keratinocyte medium (1:1; AEDK; control), or with AEDK supplemented with 20% AECM or 20% FDCM. The mass spectrometry sample was prepared by protein precipitation, followed by gel electrophoresis and in-gel digestion.

Results :

AECM promoted better cell migration compared to the FDCM and the control medium. Bioinformatics analysis identified a total of 121, and 92 proteins from AECM and FDCM, respectively: 109 and 82 were identified as secreted proteins, respectively. STRING® analysis predicted that 23 proteins from the AECM and 16 proteins from the FDCM are involved in wound healing.

Conclusion:

Conditioned medium promotes wound healing by enhancing cell migration, and we successfully identified various secretory proteins in a conditioned medium that play important roles in wound healing.

Virtual patients for mechanical ventilation in the intensive care unit

BACKGROUND

Mechanical ventilation (MV) is a core intensive care unit (ICU) therapy. Significant inter- and intra- patient variability in lung mechanics and condition makes managing MV difficult. Accurate prediction of patient-specific response to changes in MV settings would enable optimised, personalised, and more productive care, improving outcomes and reducing cost. This study develops a generalised digital clone model, or in-silico virtual patient, to accurately predict lung mechanics in response to changes in MV.

METHODS

An identifiable, nonlinear hysteresis loop model (HLM) captures patient-specific lung dynamics identified from measured ventilator data. Identification and creation of the virtual patient model is fully automated using the hysteresis loop analysis (HLA) method to identify lung elastances from clinical data. Performance is evaluated using clinical data from 18 volume-control (VC) and 14 pressure-control (PC) ventilated patients who underwent step-wise recruitment maneuvers.

RESULTS

Patient-specific virtual patient models accurately predict lung response for changes in PEEP up to 12 cmH₂O for both volume and pressure control cohorts. R² values for predicting peak inspiration pressure (PIP) and additional retained lung volume, V_frc in VC, are R²=0.86 and R²=0.90 for 106 predictions over 18 patients. For 14 PC patients and 84 predictions, predicting peak inspiratory volume (PIV) and V_frc yield R²=0.86 and R²=0.83. Absolute PIP, PIV and V_frc errors are relatively small.

CONCLUSIONS

Overall results validate the accuracy and versatility of the virtual patient model for capturing and predicting nonlinear changes in patient-specific lung mechanics. Accurate response prediction enables mechanically and physiologically relevant virtual patients to guide personalised and optimised MV therapy.