Higher vs. lower PEEP in ARDS: just one part of the whole

Miller A, Firment P, Leškanič J, Porubän A, Halaš P, Mandinec S, Filka V, Zajac H, Gentile MA, Zdravkovic M. Programmed multi-level ventilation in COVID-19-related acute respiratory distress syndrome: a multi-center retrospective observational study

Objective

We evaluated pressure-controlled ventilation (PCV) with multiple programmed levels of positive end expiratory pressure (programmed multi-level ventilation; PMLV) in patients with coronavirus disease 2019 (COVID-19)-related acute respiratory distress syndrome (ARDS).

Methods

We conducted a multicenter, retrospective study from November 2020 to February 2021. PMLV was used with PCV in all patients with intensive care admission until improvement in oxygenation (fraction of inspired oxygen [FiO2] ≤0.50 and oxygen saturation [SpO2] >92%). The observed outcomes were improvement of hypoxemia, length of mechanical ventilation, partial pressure of carbon dioxide (PaCO2) stability, and adverse events.

Results

Of 188 mechanically ventilated patients with COVID-19-related ARDS, we analyzed 60 patients treated with PMLV. Hypoxemia improved in 55 (92%) patients, as measured by the change in partial pressure of oxygen/FiO2 and SpO2/FiO2 ratios on day 3 versus day 1, and in 32 (66%) ventilated patients on day 7 versus day 3. The median (interquartile range) length of mechanical ventilation for survivors and non-survivors was 8.4 (4.7–14.9) and 6.7 (3.6–10.3) days, respectively.

Conclusions

PMLV appears to be a safe and effective ventilation strategy for improving hypoxemia in patients with COVID-19-related ARDS. Further studies are needed comparing the PMLV mode with the conventional ARDS ventilatory approach.

Automatic Lung Segmentation and Quantification of Aeration in Computed Tomography of the Chest Using 3D Transfer Learning

BACKGROUND

Identification of lung parenchyma on computer tomographic (CT) scans in the research setting is done semi-automatically and requires cumbersome manual correction. This is especially true in pathological conditions, hindering the clinical application of aeration compartment (AC) analysis. Deep learning based algorithms have lately been shown to be reliable and time-efficient in segmenting pathologic lungs. In this contribution, we thus propose a novel 3D transfer learning based approach to quantify lung volumes, aeration compartments and lung recruitability.

METHODS

Two convolutional neural networks developed for biomedical image segmentation (uNet), with different resolutions and fields of view, were implemented using Matlab. Training and evaluation was done on 180 scans of 18 pigs in experimental ARDS (u2Net Pig ) and on a clinical data set of 150 scans from 58 ICU patients with lung conditions varying from healthy, to COPD, to ARDS and COVID-19 (u2Net Human ). One manual segmentations (MS) was available for each scan, being a consensus by two experts. Transfer learning was then applied to train u2Net Pig on the clinical data set generating u2Net Transfer . General segmentation quality was quantified using the Jaccard index (JI) and the Boundary Function score (BF). The slope between JI or BF and relative volume of non-aerated compartment (S JI and S BF , respectively) was calculated over data sets to assess robustness toward non-aerated lung regions. Additionally, the relative volume of ACs and lung volumes (LV) were compared between automatic and MS.

RESULTS

On the experimental data set, u2Net Pig resulted in JI = 0.892 [0.88 : 091] (median [inter-quartile range]), BF = 0.995 [0.98 : 1.0] and slopes S JI = -0.2 {95% conf. int. -0.23 : -0.16} and S BF = -0.1 {-0.5 : -0.06}. u2Net Human showed similar performance compared to u2Net Pig in JI, BF but with reduced robustness S JI = -0.29 {-0.36 : -0.22} and S BF = -0.43 {-0.54 : -0.31}. Transfer learning improved overall JI = 0.92 [0.88 : 0.94], P < 0.001, but reduced robustness S JI = -0.46 {-0.52 : -0.40}, and affected neither BF = 0.96 [0.91 : 0.98] nor S BF = -0.48 {-0.59 : -0.36}. u2Net Transfer improved JI compared to u2Net Human in segmenting healthy (P = 0.008), ARDS (P < 0.001) and COPD (P = 0.004) patients but not in COVID-19 patients (P = 0.298). ACs and LV determined using u2Net Transfer segmentations exhibited < 5% volume difference compared to MS.

CONCLUSION

Compared to manual segmentations, automatic uNet based 3D lung segmentation provides acceptable quality for both clinical and scientific purposes in the quantification of lung volumes, aeration compartments, and recruitability.

Longitudinal PEEP Responses Differ Between Children With ARDS and at Risk for ARDS

BACKGROUND

It is unknown whether lung mechanics differ between patients with pediatric ARDS and at risk for ARDS. We aimed to examine the hypothesis that, compared to ARDS, subjects at risk of ARDS are characterized by higher end-expiratory lung volume (EELV) or respiratory system compliance (C_RS) and lower distending pressure (stress) applied on the lung or parenchymal deformation (strain) during mechanical ventilation.

METHODS

Consecutively admitted subjects fulfilling the PALICC ARDS criteria were considered eligible for inclusion in this study. A ventilator with an integrated gas exchange module was used to calculate EELV, C_RS, strain, and stress after a steady state had been achieved based on nitrogen washout/washin technique. All subjects were subjected to incremental PEEP trials at 0, 6, 12, 24, 48, and 72 h.

RESULTS

A total of 896 measurements were longitudinally calculated in 32 mechanically ventilated subjects (n = 15 subjects with ARDS; n = 17 subjects at risk for ARDS). EELV correlated positively with strain or stress in the ARDS group (r = 0.30, P < .001) and the at risk group (r = 0.60, P < .001). C_RS correlated with strain (r = 0.40, P < .001) only in subjects at risk for ARDS. EELV increased over time as PEEP rose from 4 to 10 cm H₂O in subjects with ARDS (P = .001). In the at risk group, EELV only increased at 48 h (P = .001). Longitudinally, C_RS (P = .001) and EELV (P = .002) were lower and strain and stress were higher in subjects with ARDS compared to those at risk for ARDS (P = .002), remaining within safe limits. Strain and stress increased by 24 h but declined by 72 h in subjects with ARDS at a PEEP of 4 cm H₂O (P = .02). In the at risk group, strain and stress declined from 6 h to 72 h at a PEEP of 10 cm H₂O (P = .001).

CONCLUSIONS

Longitudinally, C_RS and EELV were lower and strain and stress were higher in subjects with ARDS compared to subjects at risk for ARDS. These parameters behaved differently over time at PEEP values of 4 or 10 cm H₂O. At these PEEP levels, strain and stress remained within safe limits in both groups.

Non-specialist therapeutic strategies in acute respiratory distress syndrome

INTRODUCTION

Acute respiratory distress syndrome (ARDS) is associated with significant morbidity and mortality. We undertook a meta-analysis of randomized controlled trials (RCTs) to determine the mortality benefit of non-specialist therapeutic interventions for ARDS available to general critical care units.

EVIDENCE ACQUISITION

A systematic search of MEDLINE, Embase, and the Cochrane Central Register for RCTs investigating therapeutic interventions in ARDS including corticosteroids, fluid management strategy, high PEEP, low tidal volume ventilation, neuromuscular blockade, prone position ventilation, or recruitment maneuvers. Data was collected on demographic information, treatment strategy, duration and dose of treatment, and primary (28 or 30-day mortality) and secondary (P<inf>a</inf>O<inf>2</inf>:FiO<inf>2</inf> ratio at 24-48 hours) outcomes.

EVIDENCE SYNTHESIS

No improvement in 28-day mortality could be demonstrated in three RCTs investigating high PEEP (28.0% vs. 30.2% control; risk ratio [confidence interval] 0.93 [0.82-1.06]; eight assessing prone position ventilation (39.3% vs. 44.5%; RR 0.83 [0.68-1.01]; seven investigating neuromuscular blockade (37.8% vs. 42.0%; RR 0.91 [0.81-1.03]); ten investigating recruitment maneuvers (42.4% vs. 42.1%; RR 1.01 [0.91-1.12]); eight investigating steroids (34.8% vs. 41.1%; RR 0.81 [0.59-1.12]); and one investigating conservative fluid strategies (25.4% vs. 28.4%; RR 0.90 [0.73-1.10]). Three studies assessing low tidal volume ventilation (33.1% vs. 41.9%; RR 0.79 (0.68-0.91); P=0.001), and subgroup analyses within studies investigating prone position ventilation greater than 12 hours (33.1% vs. 44.4%; RR 0.75 [0.59-0.95), P=0.02) did reveal outcome benefit.

CONCLUSIONS

Among non-specialist therapeutic strategies available to general critical care units, low tidal volumes and prone position ventilation for greater than 12 hours improve mortality in ARDS.

Patient-specific optimization of mechanical ventilation for patients with acute respiratory distress syndrome using quasi-static pulmonary P-V data

Quasi-static, pulmonary pressure-volume (P-V) curves were combined with a respiratory system model to analyze tidal pressure cycles, simulating mechanical ventilation of patients with acute respiratory distress syndrome (ARDS). Two important quantities including 1) tidal recruited volume and 2) tidal hyperinflated volume were analytically computed by integrating the distribution of alveolar elements over the affected pop-open pressure range. We analytically predicted the tidal recruited volume of four canine subjects and compared our results with similar experimental measurements on canine models for the validation. We then applied our mathematical model to the P-V data of ARDS populations in four stages of Early ARDS, Deep Knee, Advanced ARDS and Baby Lung to quantify the tidal recruited volume and tidal hyperinflated volume as an indicator of ventilator-induced lung injury (VILI). These quantitative predictions based on patient-specific P-V data suggest that the optimum parameters of mechanical ventilation including PEEP and Tidal Pressure (Volume) are largely varying among ARDS population and are primarily influenced by the degree in the severity of ARDS.