David W Kaczka

Introduction: Acute respiratory distress syndrome (ARDS) is characterized by significant heterogeneity in lung mechanics, leading to ventilation-perfusion mismatch and potential for ventilator-induced lung injury. Selective lobar ventilation offers a tailored approach for managing the impact of such heterogeneity by isolating and ventilating specific lung regions. However, its clinical feasibility, particularly the time needed for intubation, has not been established. This study evaluated the time required for selective lobar intubation and ventilation using a novel airway device in a high-fidelity mannequin. The primary hypothesis was that procedural times would follow a log-normal probability distribution. A secondary hypothesis was that the probability of the procedure exceeding five minutes would be less than 5% at the 95% upper confidence limit, if a log-normal distribution were suitable.

Methods: This was a prospective observational study conducted at a university simulation center. Clinicians were invited to perform endotracheal intubation, followed by selective endobronchial intubation of the left lower lobe on an AirSim Advance Bronchi X mannequin (TruCorp Ltd., Lurgan, UK). Participants used a "tube-thru-tube" technique with video laryngoscopy and fiberoptic bronchoscopy, guided by stepwise visual instructions. Data collected included the total time from tube insertion to successful selective lobar ventilation and the participant's self-reported number of intubations performed in the previous year. The fit of procedural times to log-normal and other distributions was assessed using the Shapiro-Wilk and Pearson chi-square tests.

Results: Among all 52 participants, there was a poor fit to a log-normal distribution (P = 0.0040). However, the six participants who performed less than five endotracheal intubations in the preceding year had significantly longer times (P = 0.0006). Among the other 46 clinicians, procedural times showed a strong fit to a log-normal distribution (Shapiro-Wilk W = 0.98, P = 0.73), superior to normal or Weibull distributions. The mean time for successful selective lobar intubation was 2.26 minutes. No participant exceeded a five-minute threshold. Utilizing the log-normal model, the calculated 95% upper confidence limit on the probability of exceeding five minutes was 0.02%.

Conclusions: This simulation shows that procedure times for selective lobar ventilation follow a log-normal distribution. This statistical predictability is essential for quantitatively evaluating safety and for designing future clinical trials, including novel ARDS therapies with selective lobar ventilation. The confirmation of the log-normal distribution for an advanced airway task can be applied to other assessments of intubation times to make quantitative comparisons (e.g., ratios of means) and to calculate probabilities of exceeding tolerance limits.

Introduction: Protective Conventional Mechanical Ventilation (CMV) has substantially improved outcomes in patients with ARDS over the last two decades, although mortality remains unacceptably high. We hypothesized that multi-frequency ventilation (MFV), in which small volume oscillations at multiple frequencies are added to a CMV waveform, would improve lung aeration and ventilation homogeneity, as assessed by quantitative Computed Tomography (qCT).

Methods: Twenty-five pigs were mechanically ventilated using a hybrid ventilator/oscillator (OscillaVent Inc., Iowa City, Iowa). Lung injury was induced by intravenous infusion of oleic acid, after which the animals were randomized to receive: 1) Protective CMV; 2) high frequency oscillatory ventilation (HFOV) per established protocol; or 3) MFV with a waveform consisting of 3.5 and 7 Hz oscillations superimposed on a CMV waveform. For each group, seven whole-lung CT scans were obtained during static breath holds from 30 to 0 cmH2O, in decrements of 5 cmH2O. Images were obtained at five time points, including before and immediately after lung injury, as well as 3-hour intervals thereafter. Quantitative CT analyses, including aeration and texture assessment, were performed on each segmented image.

Results: Air volume decreased with reduced pressure at each time point and was significantly higher in HFOV compared to CMV. Aeration analysis revealed a higher percentage of non-aerated regions during CMV compared to MFV and HFOV, with significant increases with decreasing pressure. Normally aerated regions were more prevalent during HFOV than in CMV, and decreased with decreasing airway pressure. Texture analysis showed a significant increase in consolidated areas with decreasing pressure, with higher values in CMV compared to MFV and HFOV.

Conclusions: This study shows that in a porcine model of ARDS, both MFV and HFOV improved lung aeration and reduced consolidation compared to CMV. MFV demonstrated results similar to HFOV, but with lower mean airway pressures. These findings suggest that MFV may offer similar benefits in lung recruitment and ventilation homogeneity compared to HFOV, with less risk of hemodynamic impairment. Further research is needed to assess the clinical applicability of MFV in patients, and its long-term effects in ARDS management.

Introduction: Ventilatory support in ARDS typically relies on lung-protective strategies, aimed at minimizing risk for ventilator-induced lung injury (VILI). In this context, the ability to anticipate changes in respiratory function may support individualized treatment, thus improving patient outcomes. Ventilator waveforms such as airway flow, pressure, and volume are continuously monitored and can be analyzed with machine learning techniques to identify patterns associated with key physiological derangements. In this study, we investigate the use of a convolutional neural network (CNN) to estimate respiratory system compliance. Our objective is to assess whether this approach can predict changes in compliance over time in a large animal model of ARDS.

Methods: Acute lung injury was induced via oleic acid infusion into the pulmonary artery. Following injury maturation, nine pigs were ventilated with volume-controlled, lung-protective conventional mechanical ventilation and monitored for nine hours. Airway pressure and flow waveforms were recorded at five timepoints: baseline (BL), immediately after injury maturation (T0), and 3, 6, and 9 hours post-injury (T1, T2, T3). Respiratory system mechanics parameters were estimated using multiple linear regression based on the equation of motion. A CNN was developed to predict respiratory system compliance at T1, T2, and T3.

Results: Model predictions of future compliance (i.e., 3 hours later) showed a strong alignment with observed compliances at all time points. At T1, predicted compliance was slightly higher than true compliance, with no significant difference (p = 0.471), achieving the highest correlation and lowest RMSE. At T2, predicted closely matched true compliance, with strong correlation. At T3, predicted matched true compliance, with significant correlation. (r2 = 0.54, p = 0.025) and RMSE 2.89 mL cmH2O-1.

Conclusions: Overall, our findings support the capability of a CNN to capture accurately individual compliance trajectories over time. Future research should aim to validate this approach in larger and more diverse populations, and to explore the practical use of ventilator waveform data for real-time patient monitoring at the bedside. Such developments could provide clinicians with valuable tools to support clinical decision-making.

BackgroundDespite the implementation of protective mechanical ventilation, ventilator-induced lung injury remains a significant driver of ARDS-associated morbidity and mortality. Mechanical ventilation must be personalized and adaptive for the patient and evolving disease course to achieve sustained improvements in patient outcomes. In this study, we modified a military-grade transport ventilator to deliver the airway pressure release ventilation (APRV) modality. We developed a computationally-directed (CD) method of adjusting the expiratory duration (TLow) during APRV using physiologic feedback to reduce alveolar derecruitment and tested this modality in a porcine model of moderate-to-severe ARDS.MethodsFemale Yorkshire-cross pigs (n = 27) were ventilated using a ZOLL EMV+® 731 Series ventilator during general anesthesia and subjected to a heterogeneous Tween lung injury followed by injurious mechanical ventilation. Animals were subsequently ventilated for 6 hours under general anesthesia after randomization to one of three groups: VT6 (n = 9) with a tidal volume (VT) of 6 mL/kg and stepwise adjustments in PEEP and FiO2; VT10 (n = 9) with VT of 10 mL/kg and PEEP of 5 cmH2O; CD-APRV group (n = 9) with computationally-directed adjustments in TLow based on a nonlinear equation of motion to describe respiratory mechanics. Results are reported as median [interquartile range].ResultsAll groups developed moderate-to-severe ARDS and had similar recovery in lung injury, with all demonstrating final PaO2:FiO2 > 300 mmHg (VT6: 415.5 [383.0–443.4], VT10: 353.3 [297.3–397.7], CD-APRV: 316.6 [269.8–362.4]; p = 0.12). PaCO2 was significantly higher in the VT6 group compared with the CD-APRV group (59.3 [52.3–60.1] mmHg vs. 38.5 [32.7–52.2] mmHg, p = 0.04) but not significantly different from the VT10 group (47.5 [45.3–54.4] mmHg; p = 0.32 vs. VT6) despite having a significantly higher respiratory rate (30.0 [30.0–32.0] breaths/min) compared with VT10 (12.0 [12.0–15.0] breaths/min, p = 0.001) and CD-APRV (14.0 [14.0–14.0] breaths/min, p < 0.001) groups at the study end.ConclusionWe successfully implemented a computationally directed APRV modality on a transport ventilator, adjusting TLow based on respiratory mechanics. This study demonstrated that CD-APRV can be safely used, with the advantage of guiding expiratory duration adjustments based on physiologic feedback from the lungs.

Patients with acute respiratory failure often require supportive mechanical ventilation to maintain adequate gas exchange. Recent studies have shown that multi-frequency ventilation (MFV), the technique of presenting multiple simultaneous frequencies in flow or pressure at the airway opening, may provide more uniform ventilation distribution and parenchymal strain throughout the mechanically heterogeneous lung. In this study, we simulated gas flow within a porcine central airway tree, from the trachea to the fifth generation, with dynamic boundary conditions during volume-controlled conventional mechanical ventilation (CMV) cycled at 0.27 Hz (16.2 min-1), as well as MFV waveforms comprised of two fast sinusoidal components (i.e., 3.5 Hz and 7.0 Hz) superimposed on the 0.27 Hz CMV waveform. By using forced gas flows at the airway opening of the computational lung model, dynamic pressures at various airway segments were predicted, based on the interactions of internal flow with the downstream elastances and peripheral airway resistances. Internal airflows were simulated and analyzed in both time- and frequency-domains. The results indicate that MFV resulted in stronger asymmetric flow (i.e., “pendelluft”) at end-inspiration and end-expiration. MFV also appeared to augment inlet-outlet phase differences for both pressure and flow compared with CMV, suggesting that MFV may enhance gas mixing, thus facilitating more efficient ventilation.

Accurate modeling of lung parenchymal biomechanics is critical for understanding respiratory function and improving diagnoses. Traditional hyperelastic models capture tissue deformation but miss essential physiological interactions. This study evaluates an experimentally informed poroelastic model (Birzle's formulation) against hyperelastic-only models within a finite element framework. Using porcine lung geometry and CT-based boundary conditions, we simulate realistic breathing cycles and compare deformation, stress, strain, and volume change. Results show that poroelasticity better reproduces pressure-volume behavior and ventilation distribution, underscoring the importance of fluid-influenced mechanics for robust, clinically relevant lung modeling.

Physiologic closed-loop control (PCLC) of inspired oxygen during mechanical ventilation involves frequent adjustment of inspired oxygen fraction (FiO2) based on feedback from monitoring peripheral oxygen saturation (SpO2). Safety assessments of PCLC algorithms are important prerequisites for patient care, but clinical trials often fail to represent worst-case scenarios that identify limits of safe usage and often do not quantify performance sensitivity to physiologic deviations. The objective of this study was to develop and validate a virtual patient model of pulmonary and systemic gas exchange to assess the safety and efficacy of a PCLC algorithm for FiO2 control. A large-scale (3,780,000 simulated patients) virtual observational study was conducted with three clinically relevant scenarios: (1) a step change in patient cardiorespiratory condition; (2) a step change in target SpO2; and (3) a step change in PCLC activation. Virtual patients were simulated using a uniform sampling approach to evaluate controller performance in challenging and extreme conditions representing worst-case scenarios. Results in the virtual cohort are not intended to convey predictions of controller performance in typical real-world cohorts. The results demonstrate that PCLC of FiO2 is effective for reducing the duration and severity of desaturation during a sudden change in patient condition, and in many cases prevents desaturation altogether (e.g., reducing the occurrence of prolonged desaturation from 69.8% to 1.5%). Performance was most sensitive to the physiologic delay between changes in arterial and peripheral oxygenation saturations. Longer physiologic delays (120 to 300 seconds) coupled with positive SpO2 sensor bias (1.5 to 3.0 %) were also associated with increased likelihood of system response oscillations. The impact of initial FiO2 setting on performance metrics was nonuniform (although 0.4 initial FiO2 was optimal in most cases), and was most affected by variations in pulmonary shunt fraction and SpO2 sensor bias. This study demonstrates the utility of large-scale virtual patient modeling for sampling wide ranges of physiologic parameters using a multifactorial approach. Sampled conditions may be rarely observed in clinical practice or underrepresented in clinical trials yet warrant careful consideration when evaluating safety and efficacy of autonomous medical device control. The potential impact of the virtual patient model and proposed study design is improved rigor in the evaluation of medical device safety and efficacy, achieved by using computational modeling to complement the shortcomings of clinical trials.
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•Verification and validation of a respiratory-circulatory virtual patient model•Practical scenarios: patient disturbance, changing target, controller activation•Controller safety and efficacy tested over wide ranges of physiological variables•Limits of effective control determined in challenging cases and extreme physiology•Controller performance most sensitive to long lung-to-periphery sensing delays

The acute respiratory distress syndrome (ARDS) is characterized by pathologic and heterogeneous alterations in the mechanical properties of lung tissue. While several techniques exist that allow for assessment of global lung mechanics in health and disease, few techniques allow for quantitative assessment of regional mechanics, which is important for understanding the impact of therapeutic interventions on local structure-function relationships. X-ray computed tomography (CT) is a widely available imaging modality for assessment of regional lung structure, given its high spatial resolution, as well as its ability to provide detailed information on regional anatomic and pathologic features. Quantitative computed tomography (qCT) has evolved into an important tool for assessment of regional and global mechanical changes associated with deranged structure-function relationships in many lung diseases, especially ARDS. The purpose of this study was to determine how specific structural and functional characteristics of the acutely injured lung may be altered, as assessed with various qCT imaging metrics. Such alterations may serve as a template for characterizing the severity of ARDS in patients. We evaluated and compared pressure-volume relationships, distensibility, aeration, tissue texture, and parenchymal deformation in healthy and injured lungs of anesthetized pigs, using volumetric CT images obtained during static breath holds from 30 to 0 cmH
O airway pressure. We demonstrate how qCT imaging provides unique insight into structure-function changes associated with acute lung injury, and how such techniques may enhance our understanding of regional and global parenchymal mechanics in patients with ARDS or other forms of lung injury.