Impact of reductive ventilatory strategies on mechanical power in severely burned patients undergoing pressure-controlled ventilation

Mechanical power (MP) is a promising tool for guidance of lung protective ventilation. Different equations have been proposed to calculate MP in pressure control ventilation (PCV). The aim of this study is to introduce an easy to use MP equation MP<inf>pcv(m-simpl)</inf> and compare it to an equation proposed by Van der Meijden et al. (MP<inf>pcv</inf>) which considered as the reference equation in PCV. Ventilatory parameters of 206 Covid-19 ARDS patients recorded between 24-72 hours after admission to intensive care unit. The PCV data from these patients were retrospectively investigated. MP in PCV was calculated with a modified equation (MP<inf>pcv(m-simpl)</inf>) derived from the equation (MP<inf>pcv</inf>) of Van der Meijden et al.: 0.098xRRx∆Vx(PEEP+∆P<inf>insp</inf> - 1). The results from MP<inf>pcv(slope)</inf>, MP<inf>pcv(simpl)</inf>, and<inf> </inf>MP<inf>pcv(m-simpl) </inf>were compared to MP<inf>pcv</inf> at 15 cmH<inf>2</inf>O ∙ s/L inspiratory resistance levels by univariable regression and Bland-Altman analysis. Inspiratory resistance levels at 15 cmH<inf>2</inf>O s/L was found to be correlated between the power values calculated by MP<inf>pcv(simpl)</inf>/MP<inf>pcv(m-simpl)</inf> and the MP<inf>pcv(slope)</inf>/MP<inf>pcv</inf> based on univariable logistic regression (R2≥98) analyses. In the comparison of all patients average MP values computed by the MP<inf>pcv(m-simpl)</inf> equation and the MP<inf>pcv</inf> reference equation. Bland-Altman analysis mean difference and p values at 15 cmH<inf>2</inf>O s/L inspiratory resistance values (J/min) were found to be MP<inf>pcv(m-simpl)</inf> vs MP<inf>pcv</inf>=-0,04 (P=0.014); MP<inf>pcv(slope)</inf> vs. MP<inf>pcv</inf>=0.63 (P<0.0001); MP<inf>pcv(simpl)</inf> vs. MP<inf>pcv</inf>=0.64 J/min (P<0.0001), respectively. The results of this study confirmed that the MP<inf>pcv(m-simpl)</inf> equation can be used easily to calculate MP at bedside in pressure control ventilated COVID-19 ARDS patients.

In recent years, mechanical power (MP) has emerged as an important concept that can significantly impact outcomes from mechanical ventilation. Several individual components of ventilatory support such as tidal volume (VT), breathing frequency, and PEEP have been shown to contribute to the extent of MP delivered from a mechanical ventilator to patients in respiratory distress/failure. The aim of this study was to identify which common individual setting of mechanical ventilation is more efficient in maintaining safe and protective levels of MP using different modes of ventilation in simulated subjects with ARDS. We used an interactive mathematical model of ventilator output during volume control ventilation (VCV) with either constant inspiratory flow (VCV-CF) or descending ramp inspiratory flow, as well as pressure control ventilation (PCV). MP values were determined for simulated subjects with mild, moderate, and severe ARDS; and whenever MP > 17 J/min, VT, breathing frequency, or PEEP was manipulated independently to bring back MP to ≤ 17 J/min. Finally, the optimum VT-breathing frequency combinations for MP = 17 J/min were determined with all 3 modes of ventilation. VCV-CF always resulted in the lowest MPs while PCV resulted in highest MPs. Reductions in VT were the most efficient for maintaining safer and protective MP. At targeted MPs of 17 J/min and maximized minute ventilation, the optimum VT-breathing frequency combinations were 250-350 mL for VT and 32-35 breaths/min for breathing frequency in mild ARDS, 200-350 mL for VT and 34-40 breaths/min for breathing frequency in moderate ARDS, and 200-300 mL for VT and 37-45 breaths/min for breathing frequency for severe ARDS. VCV-CF resulted in the lowest MP. VT was the most efficient for maintaining safe and protective MP in a mathematical simulation of subjects with ARDS. In the context of maintaining low and safe MPs, ventilatory strategies with lower-than-normal VT and higher-than-normal breathing frequency will need to be implemented in patients with ARDS.

Background Mechanical ventilation is a critical therapeutic intervention in the management of patients with respiratory failure. Understanding the implications of different ventilation modes is essential in preventing ventilator-induced lung injuries (VILI). Recently, mechanical power has emerged as a critical element in the development of VILI and mortality. Previous bench work studies have suggested that new optimal (adaptive) modes, such as Adaptive Ventilation Mode 2 (AVM-2), can reduce the mechanical power in turn might reduce the rates of VILI. This study aims to compare the conventional Pressure-Controlled Ventilation (PCV) mode with an emerging design of Adaptive Ventilation Mode-2 (AVM-2), to measure the differences in mechanical power, alongside it’s components of PEEP, Tidal, Elastic, Resistive, Inspiratory, Total work, tidal volume, driving pressure and Power Compliance Index. Methods Between January 2023 and June of 2023, we conducted a prospective crossover study on twenty-two subjects admitted to our ICU within the first day after initiation of mechanical ventilation. Subjects were initially started on PCV settings chosen by the primary treatment team, then switched to AVM-2 with comparable minute ventilation. Mechanical power and its work components (tidal, resistive, PEEP, elastic, inspiratory, total), tidal volume, driving pressure, respiratory rate, and positive end-expiratory pressure, were recorded for each patient every 15 min for the duration of 2 consecutive hours on each mode. Statistical analysis, including paired t-tests were performed to assess the significance of differences between the two ventilation modes. The data is provided in means and 土 SD. Results There were significant differences between PCV and AVM-2 in mechanical power (J/min): 21.62 土 7.61 vs 14.21 土 6.41 (P &lt; 0.001), PEEP work (J): 4.83 土 2.71 vs 4.11 土 2.51 (P &lt; 0.001), Tidal work (J): 3.83 土 1.51 vs 2.21 土 0.89 (P &lt; 0.001), Elastic work (J): 8.62 土 3.13 vs 6.32 土 3.21 (P &lt; 0.001), Resistive work (J): 3.23 土 1.61 vs 1.81 土 1.31 (P 0.013), Inspiratory work (J): 6.95 土 2.58 vs 4.05 土 2.01 (P &lt; 0.001), Total work (J): 11.81 土 3.81 vs 8.11 土 4.23 (P &lt; 0.001). There were significant differences between PCV and AVM-2 in tidal volume (ml): 511 土 8.22 vs 413 土 10.21 (P &lt; 0.001), tidal volume / IBW 7.38 土 1.74 vs 6.49 土 1.72 (P 0.004), driving pressure (cmH2O): 24.45 土 6.29 vs 20.11 土 6.59 (P 0.012), minute ventilation (L/min): 8.96 土 1.34 vs 7.42 土 1.41 (P &lt; 0.001). The respiratory rate (bpm) was not significantly different between PCV and AVM-2 19.61 土 4.32 vs 18.32 土 1.43 (P 0.176). There were no significant differences between PCV and AVM-2 in static compliance (ml/cmH2O) 20.24 土 5.16 vs 22.72 土 6.79 (P 0.346), PaCO2 (mmHg) 44.94 土 9.62 vs 44.13 土 10.11 (P 0.825), and PaO2:FiO2 243.54 土 109.85 vs 274.21 土 125.13 (P 0.343), but significantly higher power compliance index in PCV vs AVM-2: 1.11 土 0.41 vs 0.71 土 0.33 (P &lt; 0.001). Conclusion This study demonstrates that the choice of mechanical ventilation mode, whether PCV or AVM-2, significantly impacts mechanical power and its constituent variables. AVM-2 mode was associated with reduced mechanical power, and its’ components alongside the driving pressure, and tidal volumes, indicating its potential superiority in terms of lung-protective ventilation strategies. Clinicians should consider these findings when selecting the most appropriate ventilation mode to minimize the risk of ventilator-associated complications and improve patient outcomes. Further research is warranted to explore the clinical implications of these findings and to refine best practices in mechanical ventilation. Key words: Mechanical power, Work, PCV, AVM-2, VILI

Mechanical power (MP), a predictor of ventilator-induced lung injury (VILI), is influenced by ventilatory parameters such as inspiratory rise time (Tslope). While Tslope affects the flow profile, its impact on MP in acute respiratory distress syndrome (ARDS) has not been thoroughly studied, particularly using the geometric method. In this prospective observational study, 30 deeply sedated and paralyzed ARDS patients were ventilated in both volume-controlled ventilation (VCV) and pressure-controlled ventilation (PCV) modes using a Maquet Servo-u ventilator. At inspiratory-to-expiratory (I:E) ratios of 1:2 and 1:1, Tslope was adjusted from 5 to 15%, and pressure-volume (P-V) loop screenshots were captured. Geometric mechanical power (MPtotal) was calculated based on the area enclosed by the P-V loops. A total of 720 images were analyzed. In VCV mode, increasing Tslope from 5 to 15% led to a statistically significant increase in MPtotal: 0.8 J/min (5%) at I:E 1:2 and 0.1 J/min (1%) at I:E 1:1. Conversely, in PCV mode, Tslope prolongation resulted in a significant decrease in MPtotal: 1.8 J/min (12.5%) at I:E 1:2 and 1 J/min (7%) at I:E 1:1. No intrinsic PEEP was detected. Modifying Tslope alters MPtotal in opposing directions in PCV and VCV modes. In VCV, prolonging Tslope from 5 to 15% increased MP, whereas increasing the I:E ratio from 1:2 to 1:1 reduced MP. In PCV, prolongation of Tslope from 5 to 15% decreased MP by more than 1 J/min, and changes in the I:E ratio exerted minimal effects on MP.

We compared the effects of pressure-controlled volume-guaranteed ventilation (PCV) and volume-controlled ventilation (VCV) on respiratory mechanics and mechanical power (MP) in elderly patients undergoing laparoscopy. Fifty patients aged 65–80 years scheduled for laparoscopic cholecystectomy were randomly assigned to either the VCV group (n = 25) or the PCV group (n = 25). The ventilator had the same settings in both modes. The change in MP over time was insignificant between the groups (p = 0.911). MP significantly increased during pneumoperitoneum in both groups compared with anesthesia induction (IND). The increase in MP from IND to 30 min after pneumoperitoneum (PP30) was not different between the VCV and PCV groups. The change in driving pressure (DP) over time were significantly different between the groups during surgery, and the increase in DP from IND to PP30 was significantly higher in the VCV group than in the PCV group (both p = 0.001). Changes in MP during PCV and VCV were similar in elderly patients, and MP increased significantly during pneumoperitoneum in both groups. However, MP did not reach clinical significance (≥12 J/min). In contrast, the PCV group had a significantly lower increase in DP after pneumoperitoneum than the VCV group.

Mechanical power (MP) represents the amount of energy applied by the ventilator to the respiratory system over time. There are two main methods to calculate MP in mechanical ventilation. The first is the geometric method, which directly measures the dynamic inspiratory area of the pressure-volume loop during the respiratory cycle. The second involves using various algebraic equations to estimate MP. However, almost all calculations are either complex or not reliable compared with the geometric method, considered the gold standard. This study aimed to develop an easy to use, reliable equation for bedside calculation of MP and to compare its accuracy with other existing equations for calculating MP. In a preliminary study, we measured MP in 56 cases who were mechanically ventilated and without spontaneous breathing efforts. The measurements were done at the ICU of a single university medical center in the Netherlands. We found that the MP can be accurately calculated using an equation that incorporates the plateau pressure in 56 cases in 42 patients. The MP estimated with our new proposed equation (MP calculated using plateau pressure) correlated well with the reference value of MP with a bias of 0.2 J/min. The 95% limits of agreement (LoAs) were -3.1 to + 3.4 J/min. Other equations give the following bias and LoAs; bias of -0.8, LoA -3.8 to 1.9 J/min (van der Meijden equation), bias of -1.9, LoA -3.7 to -0.0 J/min (comprehensive Becher equation), bias of -2.4, LoA -4.5 to -0.3 J/min (simplified Becher equation), and a bias of -1.9, LoA -3.7 to 0.1 J/min (linear model equation). The equation we propose to calculate MP in pressure-controlled ventilation is a reliable, simple, and accurate alternative for the previously published equations. Consequently, this method is highly suitable for routine use in clinical practice.

Mechanical ventilation is a life-support system used in many environments, including the emergency department, operating room, and intensive care unit (ICU). The monitoring of respiratory mechanics has received much attention recently. In this context, tidal volume, respiratory rate and respiratory system plateau (Pplat) and driving (ΔP) pressures are the ventilator variables classically assessed and reported in clinical trials. However, when analyzed separately, they are not associated with inhospital mortality in patients without the acute respiratory distress syndrome (ARDS). On the other hand, mechanical power, which represents the amount of energy transferred from the mechanical ventilator to the respiratory system over time, is demonstrably associated with inhospital mortality. Mechanical power is calculated by the combination of tidal volume, respiratory rate, Pplat, and ΔP. Recent advances have been made in the calculation of mechanical power, not only in volume-controlled but also in pressure-controlled ventilation, in critically ill patients. These advances rely on the computation of the elastic and resistive components of mechanical power, as well as on the computation of mechanical power during assisted breaths. In this chapter, we list ten reasons why mechanical power should be monitored as a tool to guide ventilator strategies in patients without ARDS. We believe that the development of algorithms for embedding in mechanical ventilators and the provision of real-time information on mechanical power could help physicians better guide their ventilator strategies in patients without ARDS.

BackgroundMechanical power may serve as a valuable parameter for predicting ventilation-induced injury in mechanically ventilated patients. Over time, several equations have been developed to calculate power in both volume control ventilation (VCV) and pressure control ventilation (PCV). Among these equations, the linear model mechanical power equation (MPLM) closely approximates the reference method when applied in PCV. The dynamic mechanical power equation (MPdyn) computes power by utilizing the ventilatory work of breathing parameter (WOBv), which is automatically measured by the mechanical ventilator. In our study, conducted in patients with Covid-19 Acute Respiratory Distress Syndrome (C-ARDS), we calculated mechanical power using both the MPLM and MPdyn equations, employing different inspiratory rise times (Tslope) at intervals of 5%, ranging from 5 to 20% and compared the obtained results.ResultsIn our analysis, we used univariate linear regression at both I:E ratios of 1:2 and 1:1, considering all Tslope values. These analyses revealed that the MPdyn and MPLM equations exhibited strong correlations, with R2 values exceeding 0.96. Furthermore, our Bland–Altman analysis, which compared the power values derived from the MPdyn and MPLM equations for patient averages and all measurements, revealed a mean difference of −0.42 ± 0.41 J/min (equivalent to 2.6% ± 2.3%, p < 0.0001) and −0.39 ± 0.57 J/min (equivalent to 3.6% ± 3.5%, p < 0.0001), respectively. While there was a statistically significant difference between the equations in both absolute value and relative proportion, this difference was not considered clinically relevant. Additionally, we observed that each 5% increase in Tslope time corresponded to a decrease in mechanical power values by approximately 1 J/min.ConclusionsThe differences between mechanical power values calculated using the MPdyn and MPLM equations at various Tslope durations were determined to lack clinical significance. Consequently, for practical and continuous mechanical power estimation in Pressure-Controlled Ventilation (PCV) mode, the MPdyn equation presents itself as a viable option. It is important to note that as Tslope times increased, the calculated mechanical power exhibited a clinically relevant decrease.

Aims: Postoperative pulmonary complications (PPCs) are a major cause of perioperative morbidity and mortality in patients undergoing major abdominal surgery. Although various factors contribute to PPCs, intraoperative mechanical ventilation strategies play a critical role. Mechanical power, a parameter encompassing factors like tidal volume and respiratory rate, has emerged as a potential risk factor for ventilator-associated lung injury (VILI). This study aims to investigate the relationship between intraoperative mechanical power and PPCs. Methods: This prospective, observational study included 207 patients aged 18 years and older undergoing elective major abdominal surgery between April and December 2022. Mechanical power was calculated using a simplified formula based on ventilator parameters recorded at 15-minute intervals. PPCs were evaluated within 24 hours postoperatively, following the European Perioperative Clinical Outcome guidelines. Primary outcome was the relationship between intraoperative mechanical power and PPCs, with secondary outcomes assessing the incidence of specific PPCs. Results: PPCs occurred in 22.2% (n=46) of the patients. The mean mechanical power was 8.99 J/min in patients with PPCs and 8.56 J/min in those without, with no statistically significant difference. Atelectasis was the most common PPC. Factors such as chronic obstructive pulmonary disease, prolonged surgery, and higher ASA scores were associated with increased PPC risk. Conclusion: Although no significant association between mechanical power and PPCs was found in this study, the findings underscore the importance of considering mechanical power in intraoperative ventilation strategies to reduce the risk of ventilator-associated lung injury. Further large-scale, prospective studies involving diverse patient populations are essential to clarify the role of mechanical power in minimizing PPCs and improving perioperative outcomes. Careful selection and management of ventilation strategies, with a focus on optimizing mechanical power, remain crucial in reducing PPC incidence and enhancing patient care.

OBJECTIVES:Mechanical power (MP) is a way of estimating the energy delivered by the ventilator to the patient. For both volume-controlled ventilation (VCV) and pressure-controlled ventilation (PCV) methods have been described to calculate the MP. The pressure-volume (PV) loop, from which the MP is calculated, is different for VCV compared with PCV. We aimed to compare the MP of VCV with zero pause time (VCV-0), VCV with 10% pause time (VCV-10), and PCV within patients in different patient categories based on severity of lung injury.DESIGN:In a proof-of-concept study, we enrolled 46 mechanically ventilated patients without spontaneous breathing efforts. Baseline measurements were done in pressure-controlled mode. Subsequently, measurements were done in VCV-0 and VCV-10. Tidal volume and all other settings were kept the same.SETTING:ICU, single university medical center.PATIENTS:Fifty-eight cases in 46 patients on controlled ventilation modes.INTERVENTIONS:Comparison between the MP of PCV, VCV-0, and VCV-10.MEASUREMENT AND MAIN RESULTS:The mean MP of VCV-0, VCV-10, and PCV was 19.30, 21.80, and 20.87 J/min, respectively (p < 0.05 for all comparisons). The transpulmonary MP of VCV-0, VCV-10, and PCV was 6.75, 8.60, and 7.99 J/min, respectively (p < 0.05 for all comparisons).CONCLUSIONS:In patients ventilated in a controlled mode, VCV without pause time had the lowest MP followed by PCV. VCV with 10% pause time had the highest MP.

We aimed to assess the reproducibility of mechanical power (MP) equations in comparison with the geometric method in critically ill children during respiratory support with invasive pressure-controlled ventilation (PCV). Prospective, exploratory research study. Single-center, PICU in The Netherlands. Children (< 18 yr old) admitted to the PICU receiving PCV. None. MP was calculated in a cohort of 37 children, with a median (interquartile range [IQR]) age of 12 months (IQR, 2-60 mo). Three, previously proposed MP equations (simplified, comprehensive, and linear MP) were compared with the geometric mean ("gold standard"), measuring the area-under-the-pressure-volume-loop, and assessed using agreement (Bland-Altman) analysis and reliability (intraclass correlation coefficient [ICC]) analysis of parameters. The mean difference (95% CI) was as follows: simplified MP -0.02 J/min (95% CI, -1.02 to 0.99 J/min), comprehensive MP 0.03 J/min (95% CI, -0.94 to 1.00 J/min), and linear MP 0.16 J/min (95% CI, -0.76 to 1.08 J/min). The ICCs for all comparisons were excellent (i.e., > 0.99; p < 0.001). In critically ill children undergoing invasive PCV, all three MP equations acceptably reproduce the geometric method for calculating MP in J/min.

IMPORTANCE:Mechanical power (MP) could serve as a valuable parameter in clinical practice to estimate the likelihood of adverse outcomes. However, the safety thresholds for MP in mechanical ventilation remain underexplored and contentious.OBJECTIVES:This study aims to investigate the association between MP and hospital mortality across varying degrees of lung disease severity, classified by Pao2/Fio2 ratios.DESIGN, SETTING, AND PARTICIPANTS:This is a retrospective cohort study using automatically extracted data. Patients admitted to the ICU of a tertiary referral hospital in The Netherlands between 2018 and 2024 and ventilated in pressure-controlled mode were included.MAIN OUTCOMES AND MEASURES:Logistic regression, adjusted for age, sex, Acute Physiology and Chronic Health Evaluation-IV score, and Pao2/Fio2 ratio, was used to calculate the odds ratio (OR) for all-cause in-hospital mortality.RESULTS:A total of 2184 patients were analyzed, with a mean age of 62.5 ± 13.8 years, of whom 1508 (70.2%) were male. The mean MP was highest in patients with the lowest Pao2/Fio2 ratios (21.5 ± 6.5 J/min) compared with those with the highest ratios (12.0 ± 3.8 J/min; p < 0.001). Adjusted analyses revealed that increased MP was associated with higher mortality (OR, 1.06; 95% CI, 1.03–1.09 per J/min increase). Similarly, MP normalized for body weight showed a stronger association with mortality (OR, 1.004; 95% CI, 1.002–1.006 per J/min/kg increase). An increase in mortality was seen when MP exceeded 16–18 J/min.CONCLUSIONS AND RELEVANCE:Our findings demonstrate a significant association between MP and hospital mortality, even after adjusting for key confounders. Mortality increases notably when MP exceeds 16–18 J/min. Normalized MP presents an even stronger association with mortality. These results underscore the need for further research into ventilation strategies that consider MP adjustments.

IntroductionCritically ill patients with acute respiratory failure are exposed to high-intensity mechanical ventilation making them prone to ventilator-induced lung injury (VILI)1. The ventilator-generated causes of VILI include the pressures2, volume3 delivered, gas flows4, and respiratory rate5. They all contribute to mechanical power (MP) induced lung injury and can worsen the outcome of an already diseased lung.ObjectivesThe primary objective of this study is to explore the correlation between mechanical power and mortality outcomes in critically ill patients.Materials and methodsIt is a single-centre, prospective observational study. Out of 100 patients to be enrolled in the study, 44 patients have so far met the inclusion criteria. Inclusion criteria includes age >18 years, on invasive controlled mechanical ventilation > 48 hours. Demographic data, Comorbidities, APACHE II, SOFA score, and 6 hourly ventilator parameters have been recorded. MP was calculated at 24 hr, 48 hr, and 72 hr as per the formula given by Chiumello et al6 for Pressure-controlled ventilation (PCV) and Giossa et al7 for Volume-controlled ventilation(VCV). Patients were divided into survivors and non-survivors at day 28. Chi-square and independent t-test have been used for statistical calculation using SPSS 23 software. ROC curve and Kaplan Meier survival analysis were used to correlate MP to 28-day mortality.ResultsIn a total of 113 patients screened between May 2023 to Nov 2023, 44 patients have been analysed. The median age is 42.16 years (28.5-54.75), and 56.8% are males. In the survivor group, the median age is 41.46 years (25.75-55.5) (p value-0.68), 53.3% males (p-0.53) as compared to the non-survivor group. At 24 hr APACHE-II score was 23.2 (18.5-27.25) and SOFA score was 13.78 (11.75-16) which were significantly higher in non-survivors. Non-survivors group also had significantly higher MP (P value<0.05). The Pearson correlation analysis showed a good correlation between MP and Driving pressures (DP) (r values: 24 h: 0.794; 48 h: 0.81; all P < 0.01) and MP and Plateau pressures at all points of time. (r values: 24 h: 0.865; 48 h: 0.869; all P value < 0.01). The ROC curves showed that MP at 24h and 48 h were significant values in predicting the outcome, with areas under the curve of 0.71 and 0.82.ConclusionOur preliminary results show a significant correlation between MP at 24 hrs and 48 hours with 28-day mortality. It can be an independent risk factor for mortality in ICU patients.

Mechanical power (MP) transferred from the ventilator to the lungs has been proposed as a summary variable that may impact mortality in children with acute respiratory distress syndrome (ARDS). To date, no study has shown an association between higher MP and mortality in children with ARDS. Secondary analysis of a prospective observational study. Single-center, tertiary, academic PICU. Five hundred forty-six intubated children with ARDS enrolled between January 2013 and December 2019 receiving pressure-controlled ventilation. None. Higher MP was associated with increased mortality (adjusted hazard ratio [HR] 1.34 per 1 sd increase, 95% CI 1.08-1.65; p = 0.007). When assessing the contribution of individual components of MP, only positive end-expiratory pressure (PEEP) was associated with mortality (HR 1.32; p = 0.007), whereas tidal volume, respiratory rate, and driving pressure (ΔP = [peak inspiratory pressure (PIP)-PEEP]) were not. Finally, we tested whether there remained an association when specific terms were removed from the MP equation by calculating MP from static strain (remove ΔP), MP from dynamic strain (remove PEEP), and mechanical energy (remove respiratory rate). MP from static strain (HR 1.44; p < 0.001), MP from dynamic strain (HR 1.25; p = 0.042), and mechanical energy (HR 1.29; p = 0.009) were all associated with mortality. MP was associated with ventilator-free days only when using MP normalized to predicted body weight, but not when using measured weight. Higher MP was associated with mortality in pediatric ARDS, and PEEP appears to be the component most consistently driving this association. As higher PEEP is used in sicker patients, the association between MP and mortality may reflect a marker of illness severity rather than MP itself being causal for mortality. However, our results support future trials testing different levels of PEEP in children with ARDS as a potential means to improve outcome.

BackgroundThe optimal mechanical ventilation strategy to minimise ventilator-induced lung injury (VILI) remains uncertain. Mechanical power (MP) is a key VILI determinant, but whether and how MP should be normalised to individual patient characteristics is unclear. In this study, we aimed to evaluate whether the discriminatory accuracy of MP for ICU mortality in mechanically ventilated patients improves when normalised to physiologically relevant variables that reflect individual susceptibility to VILI. We also explored whether the relationship between MP, MPratio, and mortality is linear or exhibits a threshold effect.MethodsIn this retrospective observational study, we extracted granular electronic healthcare record data for mechanically ventilated adults in a single centre over a seven-year period. Primary exposures were MP with five normalisations: for dead space (expressed as corrected minute ventilation, ventilatory ratio, or end-tidal to arterial CO2 ratio); aerated lung size (compliance), and normal idealised MP (MPratio). We used logistic regression to assess associations with ICU mortality. We calculated the Area Under the Receiver Operating Characteristic Curve (AUROC) to compare discriminative accuracy of individual models. Additionally, we evaluated the linearity or presence of a threshold for the relationships between MP, MPratio and ICU mortality.ResultWe included 3,578 patients in our analyses. We found MP normalised to compliance (AUROC 0.71, 95% confidence interval (CI) 0.69–0.73, p = 0.007 (DeLong’s test)) and MPratio (AUROC 0.71, 95% CI 0.68–0.73, p = 0.0014) performed better than MP alone (AUROC 0.69, 95% CI 0.66–0.71) for predicting ICU mortality. Other methods of MP normalisation were no more discriminative than MP without normalisation. The relationship between MP and MPratio with ICU mortality showed a statistically significant but small departure from linearity.ConclusionsMechanical power normalised to compliance and MPratio had better discrimination for ICU mortality than MP, although the difference was modest and absolute predictive power remained limited.Supplementary InformationThe online version contains supplementary material available at 10.1186/s13613-025-01562-9.