Mechanical Power Is Associated With Increased Mortality and Worsened Oxygenation in Ards

Mahmoud, Omar

doi:10.1016/j.chest.2020.08.638

Cited by 5 publications

(4 citation statements)

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“…This study has several important strengths. Other studies have reassessed the prognostic accuracy of mechanical power, ventilatory ratio, and PaO 2 /FiO 2 after 24–48 h 26 , 32 , 33 but, to our knowledge, this study is the first to identify subphenotypes based on respiratory and gas exchange variables in a large cohort of patients, with external validation of the findings. We combined cross-sectional latent class analysis, time-dependent latent class analysis, and group-based trajectory modelling to move from a simplistic model of two cross-sectional subphenotypes to a classification based on the individual trajectories of ventilatory ratio and mechanical power during the first 96 h of mechanical ventilation.…”

Section: Discussionmentioning

confidence: 89%

Longitudinal respiratory subphenotypes in patients with COVID-19-related acute respiratory distress syndrome: results from three observational cohorts

Bos

Sjoding

Sinha

et al. 2021

The Lancet Respiratory Medicine

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Introduction Patients with COVID-19-related acute respiratory distress syndrome (ARDS) have been postulated to present with distinct respiratory subphenotypes. However, most phenotyping schema have been limited by sample size, disregard for temporal dynamics, and insufficient validation. We aimed to identify respiratory subphenotypes of COVID-19-related ARDS using unbiased data-driven approaches. Methods PRoVENT–COVID was an investigator-initiated, national, multicentre, prospective, observational cohort study at 22 intensive care units (ICUs) in the Netherlands. Consecutive patients who had received invasive mechanical ventilation for COVID-19 (aged 18 years or older) served as the derivation cohort, and similar patients from two ICUs in the USA served as the replication cohorts. COVID-19 was confirmed by positive RT-PCR. We used latent class analysis to identify subphenotypes using clinically available respiratory data cross-sectionally at baseline, and longitudinally using 8-hourly data from the first 4 days of invasive ventilation. We used group-based trajectory modelling to evaluate trajectories of individual variables and to facilitate potential clinical translation. The PRoVENT-COVID study is registered with ClinicalTrials.gov , NCT04346342 . Findings Between March 1, 2020, and May 15, 2020, 1007 patients were admitted to participating ICUs in the Netherlands, and included in the derivation cohort. Data for 288 patients were included in replication cohort 1 and 326 in replication cohort 2. Cross-sectional latent class analysis did not identify any underlying subphenotypes. Longitudinal latent class analysis identified two distinct subphenotypes. Subphenotype 2 was characterised by higher mechanical power, minute ventilation, and ventilatory ratio over the first 4 days of invasive mechanical ventilation than subphenotype 1, but PaO 2 /FiO 2 , pH, and compliance of the respiratory system did not differ between the two subphenotypes. 185 (28%) of 671 patients with subphenotype 1 and 109 (32%) of 336 patients with subphenotype 2 had died at day 28 (p=0·10). However, patients with subphenotype 2 had fewer ventilator-free days at day 28 (median 0, IQR 0–15 vs 5, 0–17; p=0·016) and more frequent venous thrombotic events (109 [32%] of 336 patients vs 176 [26%] of 671 patients; p=0·048) compared with subphenotype 1. Group-based trajectory modelling revealed trajectories of ventilatory ratio and mechanical power with similar dynamics to those observed in latent class analysis-derived trajectory subphenotypes. The two trajectories were: a stable value for ventilatory ratio or mechanical power over the first 4 days of invasive mechanical ventilation (trajectory A) or an upward trajectory (trajectory B). However, upward trajectories were better independent prognosticators for 28-day m...

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Section: Discussionmentioning

confidence: 89%

Longitudinal respiratory subphenotypes in patients with COVID-19-related acute respiratory distress syndrome: results from three observational cohorts

Bos

Sjoding

Sinha

et al. 2021

The Lancet Respiratory Medicine

View full text Add to dashboard Cite

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“…Moreover, ECMO is a constrained resource that is not available in all ICUs. Hence, for the purpose of our study, we only considered patients receiving MV for > 48 h [ 32 , 33 ]. Fifth, regarding the potential consequences of using the new ratio at the bedside, further studies are needed to examine whether it could help for clinical decision making and guiding therapy.…”

Section: Discussionmentioning

confidence: 99%

“…The median length of an ICU LOS of all selected ARDS patients in eICU was 11.72 days (6.92–18.84). All selected patients from both databases fulfilled the Berlin criteria for ARDS and were stratified into mild, moderate or severe ARDS [ 6 ] and received mechanical ventilation (MV) for > 48 h [ 32 , 33 ]. Disease progression of ARDS in each database was tracked along those 3 ICU days to assess lung severity.…”

Section: Methodsmentioning

confidence: 99%

Novel criteria to classify ARDS severity using a machine learning approach

2021

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Background Usually, arterial oxygenation in patients with the acute respiratory distress syndrome (ARDS) improves substantially by increasing the level of positive end-expiratory pressure (PEEP). Herein, we are proposing a novel variable [PaO2/(FiO2xPEEP) or P/FPE] for PEEP ≥ 5 to address Berlin’s definition gap for ARDS severity by using machine learning (ML) approaches. Methods We examined P/FPE values delimiting the boundaries of mild, moderate, and severe ARDS. We applied ML to predict ARDS severity after onset over time by comparing current Berlin PaO2/FiO2 criteria with P/FPE under three different scenarios. We extracted clinical data from the first 3 ICU days after ARDS onset (N = 2738, 1519, and 1341 patients, respectively) from MIMIC-III database according to Berlin criteria for severity. Then, we used the multicenter database eICU (2014–2015) and extracted data from the first 3 ICU days after ARDS onset (N = 5153, 2981, and 2326 patients, respectively). Disease progression in each database was tracked along those 3 ICU days to assess ARDS severity. Three robust ML classification techniques were implemented using Python 3.7 (LightGBM, RF, and XGBoost) for predicting ARDS severity over time. Results P/FPE ratio outperformed PaO2/FiO2 ratio in all ML models for predicting ARDS severity after onset over time (MIMIC-III: AUC 0.711–0.788 and CORR 0.376–0.566; eICU: AUC 0.734–0.873 and CORR 0.511–0.745). Conclusions The novel P/FPE ratio to assess ARDS severity after onset over time is markedly better than current PaO2/FiO2 criteria. The use of P/FPE could help to manage ARDS patients with a more precise therapeutic regimen for each ARDS category of severity.

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“…This energy in turn is dependent on tidal volume, plateau pressure (Pplat), and respiratory rate (RR) and is also associated with increased mortality. 5,6 Mechanical power has been recently proposed as a unifying metric for estimating the risk of VILI. It can be calculated accurately through power equations (Table 1), which can increase its applicability in Highlights A simple model for calculation of mechanical power, using only Driving pressure and respiratory rate, was not found shown to be useful in predicting mortality in COVID-19 ARDS patients.…”

Section: Introductionmentioning

confidence: 99%

4DPRR- Index for predicting mortality in COVID-19 ARDS

Paul

Krishna

Gautam

2022

JMV

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Background Mortality in ARDS was reduced significantly after the introduction of the low tidal volume ventilation strategy. It has been recently shown that lung-protective ventilation strategies should primarily target driving pressure rather than Vt and that ventilator induced lung injury is not just dependent on tidal volume but also other factors like respiratory rate and driving pressure. Ventilator induced lung injury is also thought to be dependent on the amount of energy transferred by the ventilator to the patient which in turn is dependent on tidal volume size (VT), plateau pressure (Pplat), respiratory rate (RR). Mechanical power can be calculated accurately through power equations which can increase their applicability in clinical practice. One simple composite equation (driving pressure multiplied by four plus respiratory rate [4DPRR]) has been recently suggested as a simple surrogate for the power equation. This equation also doesn’t include PEEP as it has been theorized that it is the only elastic dynamic component of driving energy which affects the outcome and not the elastic static component (i.e., PEEP) and the resistive power (related to flow and airway resistance). Objectives To assess the mechanical power as measured by 4DPRR in mechanically ventilated patients who have moderate to severe COVID-19 ARDS. Methods: We obtained data on ventilatory variables and mechanical power from the patients who were admitted with moderate to severe COVID ARDS in our hospital from March 2021 to June 2021. Results We included 34 patients (28% women; mean age, 57 ± 17 yrs.). The average ΔP was 21.44 ± 3.98 cmH2O, the RR was 23.8 ± 3.84 breaths/min, and the mean driving pressure was 21.4 cmH2O. 28% (n = 10) of patients expired. There was no significant association of 4DPRR (P 0.72), Pplat (P 0.79).and RR (P 0.21) with mortality as predicted by area under ROC curves. Conclusions Driving power and plateau pressure were associated with mortality during controlled mechanical ventilation in COVID ARDS, but a simpler model of mechanical power using only the driving pressure and respiratory rate was found to be a poor predictor of mortality. Keywords: COVID-19, ARDS, Mechanical power, Driving pressure, Plateau pressure

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Mechanical Power Is Associated With Increased Mortality and Worsened Oxygenation in Ards

Cited by 5 publications

References 0 publications

Longitudinal respiratory subphenotypes in patients with COVID-19-related acute respiratory distress syndrome: results from three observational cohorts

Longitudinal respiratory subphenotypes in patients with COVID-19-related acute respiratory distress syndrome: results from three observational cohorts

Novel criteria to classify ARDS severity using a machine learning approach

4DPRR- Index for predicting mortality in COVID-19 ARDS

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