Total elastic power has been associated with an increased risk of developing ventilator-associated lung injury (VALI), as demonstrated by both experimental1,2 and clinical studies.3 The total elastic power is composed of several components: (a) elastic power,1 which includes positive end-expiratory pressure (PEEP) representing static strain, (b) driving power, encompassing driving pressure as a surrogate for strain in volume-controlled ventilation, and (c) minute ventilation (V′E), which includes tidal volume (VT) and respiratory rate (RR), representing dynamic strain. Together, these components reflect the dynamic loop that may explain the potential injury associated with the elastic energy transmitted by the ventilator to the lungs.4,5

Experimental studies suggest that the cyclic speed of energy delivery, a dynamic component, is a key factor in ergotrauma mechanisms. However, the capacity of lung tissue to absorb this energy and the time allowed for assimilation also play critical roles.6 Any alteration in these mechanisms of energy absorption may increase the risk of VALI.5

In experimental models of ARDS, elastic power has been identified as a crucial component of energy transfer implicated in the development of VALI.1,7 However, at present, no clinical study has evaluated the association between various power equations and the severity of ARDS.

We therefore conducted a secondary analysis of data from the Mechanical Power Day (MPD) international multicenter analytical cross-sectional study.8 This study aimed to evaluate various power equations: total mechanical power, total elastic power (comprising elastic static and elastic dynamic power), and resistive power, in a cohort of mechanically ventilated patients with and without ARDS.

This study is a post-hoc analysis of data from the Mechanical Power Day (MPD)8 study (ClinicalTrials.gov identifier: NCT03936231). The MPD study involved 113 intensive care units (ICUs) across 15 countries and 4 continents. The original study received approval from the Clinical Research Ethics Committee (CEIC) of the leading center, with subsequent approvals obtained from the CEICs of each participating center, ensuring compliance with ethical standards across all sites. Additionally, the study was approved by the Scientific Committee of the Spanish Society of Intensive Care Medicine and Coronary Units (SEMICYUC), the Spanish Society of Anesthesiology and Resuscitation (SEDAR), the Spanish Society of Pediatric Intensive Care (SECIP), and the Spanish Society of Neonatology (SENeo).

The MPD study aimed to analyze the prevalence of ARDS in the different ICUs using a cross-sectional design. Variables were collected from patients on invasive mechanical ventilation, both with and without ARDS. The specific timing of data collection during hospitalization was not specified in the study design.

Data collection occurred in two phases in 2019: the first phase in November for patient selection and the second phase in December for follow-up. Inclusion criteria for this analysis were adults ≥18 years old, admitted to the ICU, with or without ARDS (non-ARDS), and receiving invasive volume-controlled mechanical ventilation. Patients receiving mechanical ventilation with modes other than volume-assisted-controlled ventilation were excluded. The original MPD study aimed to determine the value of mechanical power used in daily clinical practice using formulas from the literature on controlled mechanical ventilation patients.

The following formulas used for the analysis were:

Initially, ARDS severity was classified using the Berlin definition.11,12 Due to the low number of severe cases, ARDS was subsequently classified into two groups: mild ARDS (PaO2/FiO2 ≤ 300 mmHg) and moderate/severe ARDS (PaO2/FiO2 ≤ 200 mmHg), to ensure sample homogeneity.13

The original MPD study included 372 patients, with respiratory failure being the primary cause of tracheal intubation (33%; 95% CI, 12–63), followed by neurological impairment (31%; 95% CI, 11–60). Among these, 181 patients (68.4% female) who were receiving volume-controlled ventilation were analyzed. The mean age of these patients was 58.2 years (SD, 17.5 years), with a mean height of 167.5 cm (SD, 9.1 cm), a mean weight of 76.8 kg (SD, 16.7 kg), and a mean predicted body weight of 60.7 kg (SD, 7.3 kg). Of the patients analyzed, 68% met the criteria for ARDS, including 33.2% with mild ARDS and 33.9% with moderate/severe ARDS. Among these patients, 85.7% were receiving continuous sedation infusion, while 14.4% were on continuous neuromuscular blockade.

Arterial blood gas (ABG), ventilatory, and power equations are summarized in Table 1.

Bayesian regression analysis identified the best models associated with ARDS severity. Elastic static power demonstrated the highest a posteriori inclusion probability (PIP) of 0.978 (Mean, 0.09; SD, 0.02), followed by total mechanical power with a PIP of 0.08 (Mean, 0.001; SD, 0.01), resistive power with a PIP of 0.07 (Mean, -0.001; SD, 0.01), and elastic dynamic power with a PIP of 0.07 (Mean, 0.0005; SD, 0.01) [Fig. 1]. Post-estimation analysis confirmed that elastic static power had the highest probability of a posteriori correlation with ARDS severity (Mean, 0.09; 95% Cred. Interval, 0.02–0.13). The model with a posteriori probability inclusion, as determined by post-estimation, showed an analytical mean probability of 1.2 and followed a normal distribution. The final model, along with the results of this post-estimation probability analysis, is depicted in Fig. 2.

Figure 1.

Bayesian analytical posterior density: a posteriori inclusion probability (PIP) of power equations according to ARDS severity. On the abscissa axis are represented the a posteriori means. On the ordinate axis, on the left is represented the density, and on the right the a posteriori inclusion probability.

Figure 2.

Bayesian model: model with a posteriori probability inclusion, as determined by post-estimation, with the power equations regarding the severity of ARDS. The normal distribution of the a posteriori probabilities obtained using the model analyzed is shown opposite.

Further analysis of the Bayesian a posteriori probabilities of the relationship between different severities of ARDS and the respiratory power variables revealed that elastic static power was the only power equation consistently correlated with both mild (Mean, 1.3; 95% Cred. Interval, 0.2–2.2) and moderate/severe ARDS (Mean, 2.8; 95% Cred. Interval, 1.7–3.8). Fig. 3 shows the Bayesian matrix plot representing the a posteriori probability distribution of the correlation between elastic static power and ARDS severity, in comparison with non-ARDS. Additional results are presented in Table 2.

Figure 3.

Bayesian matrix plot: the distribution of a posteriori probabilities of the correlation between elastic static power and ARDS severity are shown comparatively regarding non-ARDS.

Finally, analysis of the components of elastic static power showed that PEEP was the most relevant variable in this power equation. It was significantly correlated with higher a posteriori probability of both mild (Mean, 0.77; 95% Cred. Interval, 0.73–0.82) and moderate/severe ARDS (Mean, 0.90; 95% Cred. Interval, 0.83–0.97) [Table 3].

In this post-hoc analysis of a large international cross-sectional study, we found that values of elastic static power higher than 4.8 J/min were indicative of a higher energy load related to ARDS (Table 1). Among the various power equations, elastic static power showed the highest a posteriori inclusion probability in the Bayesian model, making it the most accurate predictor of ARDS severity. Although both mechanical power and total elastic power showed correlation with moderate/severe ARDS, neither of these power equations showed correlation with mild ARDS. In summary, elastic static power was the power equation mostly strongly associated with both the presence and severity of ARDS.

Our study has several strengths. First, we analyzed data from a large sample of mechanically ventilated patients collected through a multicenter, international observational study, enhancing the applicability of our results across different clinical settings. To our knowledge, this is the first study to examine the correlation between various power equations and ARDS severity (mild, moderate/severe). The advanced Bayesian analyses employed in our study provide a compelling hypothesis regarding the clinical utility of elastic static power as a metric that could enhance the understanding of energy transfer in mechanical ventilation. By utilizing real data known a priori for model calculations and a posteriori probabilities, our study robustly supports the relevance of elastic static power in ARDS severity. Bayesian statistics offer several advantages over traditional frequentist methods, making it a powerful tool for data analysis and interpretation in our study.14

Another finding is that PEEP was an important component of elastic static power in both ARDS (<8 cmH2O) and non-ARDS (<6 cmH2O) groups, according to Bayesian statistical analysis. It appears that the primary characteristics of power transmission are not solely related to lung status but rather to the interaction between lung condition and ventilatory strategy. Thus, commonly accepted notions such as “higher power equals more lung damage” and “lower power equals more lung protection” warrant careful re-evaluation.

Static variables (plateau and driving pressures) are relevant for energy generation, but dynamic variables (such as respiratory rate) are also part of the power equation.15 While these components contribute to energy transfer, they do not fully explain it. The cyclic variability of lung volume (strain and strain rate) plays a role in injury from energy inputs16 (stress and stress raisers).17 As demonstrated by Syed et al.,9 PEEP is crucial for providing a baseline strain, which adds to the global strain depending on the lung's elastic properties. PEEP likely establishes a critical baseline in the total elastic configuration of the lung, influencing the total inflation energy.18

The interaction of these power forms may better characterize the energy transfer within the mechanical ventilator-lung complex. Additionally, energy accumulated at the end of each ventilatory cycle19 and subsequent alterations in lung microarchitecture may contribute to ventilator-associated lung injury (VALI).20 PEEP may also lead to heterogeneous effects across different degrees of ARDS (BF,0.3 for mild and BF,1.5 for moderate/severe ARDS) compared with no-ARDS.

Dianti et al.21 conducted a meta-regression analysis of clinical trials involving 4731 patients with ARDS to identify an association between mechanical power and mortality. They found moderate heterogeneity (I2 = 55.8%) and concluded a weak association between elastic power and mortality. Their finding that increased elastic power was proportional to higher PEEP levels (difference 5; 95% CI, 3.5–6.6) aligns with our Bayesian analysis results.

Syed et al.,9 explored the impact of elastic power in a study with 213 patients, comparing those with and without severe hypoxemia. They found that for non-obese patients without severe hypoxemia, the mean elastic power was 9.8 J/min (range, 8.4–11.3 J/min), while for those with severe hypoxemia, it was 16.4 J/min (range, 14.8–17.8 J/min), with a statistically significant difference (P < .01). These findings are consistent with our study, where non-obese patients without hypoxemia had a mean elastic power of 4.5 J/min (range, 3.9–5.1 J/min) and those with severe hypoxemia had a mean of 6.5 J/min (range, 5.9–7.1 J/min), similar to our analysis values (≥7.6 J/min).

Similarly, Gonzalez-Castro et al.22 performed a retrospective longitudinal analysis of 253 patients to evaluate the energy burden associated with mechanical ventilation in obese patients with severe hypoxemia secondary to SARS-CoV-2. They found a mean dynamic power value of 14.6 J/min, which differs from the results of our analysis (mean 5.2 J/min). This discrepancy is mainly due to the difference in conceptualization of the power equations, as there is currently no consensus on this matter. Nevertheless, we used the data available from the work of González-Castro et al. to calculate the elastic static power, finding a mean of 9.7 J/min in obese patients with severe hypoxemia. These values are similar to those found in our study (mean 7.4 J/min) for patients with severe hypoxemia.

Finally, resistive energy was not prominently featured in our analysis, likely because the resistance, both in patients with and without ARDS, was within safe or normal ranges. Given that average respiratory rates in our dataset were around 20 breaths per minute, the energy required to overcome such resistance was minimal.18

In the current post-hoc analysis of the Mechanical Power Day study, elastic static power correlated with a higher risk for the development and severity of ARDS. Further clinical trials are required on different degrees of ARDS severity incorporating power variables as instruments for bedside measurement of the transferred energy load.

AF-C, AG-C, VMA, MI-E, CC-M, AM, PEA designed the study; AF-C take full responsibility for the accuracy of the data presented and analyzed in this article and performed the statistical analysis; AF-C, DB, PRMR, CR, PP wrote and edited the manuscript. All authors have approved the submitted version of the manuscript and agreed to be accountable for any part of the work.

This work has not received any funding. P.R.M.R. was supported by the Brazilian Council for Scientific and Technological Development (408124/2021-0) and the Rio de Janeiro State Research Foundation (E-26/010.001488/2019).

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.