Recruitment-to-inflation Ratio Assessed through Sequential End-expiratory Lung Volume Measurement in Acute Respiratory Distress Syndrome

High vs Low PEEP in Patients With ARDS Exhibiting Intense Inspiratory Effort During Assisted Ventilation: A Randomized Crossover Trial

BACKGROUND

Positive end-expiratory pressure (PEEP) can potentially modulate inspiratory effort (ΔPes), which is the major determinant of self-inflicted lung injury.

RESEARCH QUESTION

Does high PEEP reduce ΔPes in patients with moderate-to-severe ARDS on assisted ventilation?

STUDY DESIGN AND METHODS

Sixteen patients with Pao2/Fio2 ≤ 200 mm Hg and ΔPes ≥ 10 cm H2O underwent a randomized sequence of four ventilator settings: PEEP = 5 cm H2O or PEEP = 15 cm H2O + synchronous (pressure support ventilation [PSV]) or asynchronous (pressure-controlled intermittent mandatory ventilation [PC-IMV]) inspiratory assistance. ΔPes and respiratory system, lung, and chest wall mechanics were assessed with esophageal manometry and occlusions. PEEP-induced alveolar recruitment and overinflation, lung dynamic strain, and tidal volume distribution were assessed with electrical impedance tomography.

RESULTS

ΔPes was not systematically different at high vs low PEEP (pressure support ventilation: median, 20 cm H2O; interquartile range (IQR), 15-24 cm H2O vs median, 15 cm H2O; IQR, 13-23 cm H2O; P = .24; pressure-controlled intermittent mandatory ventilation: median, 20; IQR, 18-23 vs median, 19; IQR, 17-25; P = .67, respectively). Similarly, respiratory system and transpulmonary driving pressures, tidal volume, lung/chest wall mechanics, and pendelluft extent were not different between study phases. High PEEP resulted in lower or higher ΔPes, respiratory system driving pressure, and transpulmonary driving pressure according to whether this increased or decreased respiratory system compliance (r = -0.85, P < .001; r = -0.75, P < .001; r = -0.80, P < .001, respectively). PEEP-induced changes in respiratory system compliance were driven by its lung component and were dependent on the extent of PEEP-induced alveolar overinflation (r = -0.66, P = .006). High PEEP caused variable recruitment and systematic redistribution of tidal volume toward dorsal lung regions, thereby reducing dynamic strain in ventral areas (pressure support ventilation: median, 0.49; IQR, 0.37-0.83 vs median, 0.96; IQR, 0.62-1.56; P = .003; pressure-controlled intermittent mandatory ventilation: median, 0.65; IQR, 0.42-1.31 vs median, 1.14; IQR, 0.79-1.52; P = .002). All results were consistent during synchronous and asynchronous inspiratory assistance.

INTERPRETATION

The impact of high PEEP on ΔPes and lung stress is interindividually variable according to different effects on the respiratory system and lung compliance resulting from alveolar overinflation. High PEEP may help mitigate the risk of self-inflicted lung injury solely if it increases lung/respiratory system compliance.

TRIAL REGISTRATION

ClinicalTrials.gov; No.: NCT04241874; URL: www.

CLINICALTRIALS

gov.

Monitoring lung recruitment

PURPOSE OF REVIEW

This review explores lung recruitment monitoring, covering techniques, challenges, and future perspectives.

RECENT FINDINGS

Various methodologies, including respiratory system mechanics evaluation, arterial bold gases (ABGs) analysis, lung imaging, and esophageal pressure (Pes) measurement are employed to assess lung recruitment. In support to ABGs analysis, the assessment of respiratory mechanics with hysteresis and recruitment-to-inflation ratio has the potential to evaluate lung recruitment and enhance mechanical ventilation setting. Lung imaging tools, such as computed tomography scanning, lung ultrasound, and electrical impedance tomography (EIT) confirm their utility in following lung recruitment with the advantage of radiation-free and repeatable application at the bedside for sonography and EIT. Pes enables the assessment of dorsal lung tendency to collapse through end-expiratory transpulmonary pressure. Despite their value, these methodologies may require an elevated expertise in their application and data interpretation. However, the information obtained by these methods may be conveyed to build machine learning and artificial intelligence algorithms aimed at improving the clinical decision-making process.

SUMMARY

Monitoring lung recruitment is a crucial component of managing patients with severe lung conditions, within the framework of a personalized ventilatory strategy. Although challenges persist, emerging technologies offer promise for a personalized approach to care in the future.

Setting positive end-expiratory pressure: does the 'best compliance' concept really work?

PURPOSE OF REVIEW

Determining the optimal positive end-expiratory pressure (PEEP) setting remains a central yet debated issue in the management of acute respiratory distress syndrome (ARDS).The 'best compliance' strategy set the PEEP to coincide with the peak respiratory system compliance (or 2 cmH 2 O higher) during a decremental PEEP trial, but evidence is conflicting.

RECENT FINDINGS

The physiological rationale that best compliance is always representative of functional residual capacity and recruitment has raised serious concerns about its efficacy and safety, due to its association with increased 28-day all-cause mortality in a randomized clinical trial in ARDS patients.Moreover, compliance measurement was shown to underestimate the effects of overdistension, and neglect intra-tidal recruitment, airway closure, and the interaction between lung and chest wall mechanics, especially in obese patients. In response to these concerns, alternative approaches such as recruitment-to-inflation ratio, the nitrogen wash-in/wash-out technique, and electrical impedance tomography (EIT) are gaining attention to assess recruitment and overdistention more reliably and precisely.

SUMMARY

The traditional 'best compliance' strategy for determining optimal PEEP settings in ARDS carries risks and overlooks some key physiological aspects. The advent of new technologies and methods presents more reliable strategies to assess recruitment and overdistention, facilitating personalized approaches to PEEP optimization.