Mechanical power and 30-day mortality in mechanically ventilated, critically ill patients with and without Coronavirus Disease-2019: a hospital registry study

Understanding ventilator-induced lung injury: The role of mechanical power

Mechanical ventilation stands as a life-saving intervention in the management of respiratory failure. However, it carries the risk of ventilator-induced lung injury. Despite the adoption of lung-protective ventilation strategies, including lower tidal volumes and pressure limitations, mortality rates remain high, leaving room for innovative approaches. The concept of mechanical power has emerged as a comprehensive metric encompassing key ventilator parameters associated with the genesis of ventilator-induced lung injury, including volume, pressure, flow, resistance, and respiratory rate. While numerous animal and human studies have linked mechanical power and ventilator-induced lung injury, its practical implementation at the bedside is hindered by calculation challenges, lack of equation consensus, and the absence of an optimal threshold. To overcome the constraints of measuring static respiratory parameters, dynamic mechanical power is proposed for all patients, regardless of their ventilation mode. However, establishing a causal relationship is crucial for its potential implementation, and requires further research. The objective of this review is to explore the role of mechanical power in ventilator-induced lung injury, its association with patient outcomes, and the challenges and potential benefits of implementing a ventilation strategy based on mechanical power.

Effect of automated versus conventional ventilation on mechanical power of ventilation-A randomized crossover clinical trial

INTRODUCTION

Mechanical power of ventilation, a summary parameter reflecting the energy transferred from the ventilator to the respiratory system, has associations with outcomes. INTELLiVENT-Adaptive Support Ventilation is an automated ventilation mode that changes ventilator settings according to algorithms that target a low work-and force of breathing. The study aims to compare mechanical power between automated ventilation by means of INTELLiVENT-Adaptive Support Ventilation and conventional ventilation in critically ill patients.

MATERIALS AND METHODS

International, multicenter, randomized crossover clinical trial in patients that were expected to need invasive ventilation > 24 hours. Patients were randomly assigned to start with a 3-hour period of automated ventilation or conventional ventilation after which the alternate ventilation mode was selected. The primary outcome was mechanical power in passive and active patients; secondary outcomes included key ventilator settings and ventilatory parameters that affect mechanical power.

RESULTS

A total of 96 patients were randomized. Median mechanical power was not different between automated and conventional ventilation (15.8 [11.5-21.0] versus 16.1 [10.9-22.6] J/min; mean difference -0.44 (95%-CI -1.17 to 0.29) J/min; P = 0.24). Subgroup analyses showed that mechanical power was lower with automated ventilation in passive patients, 16.9 [12.5-22.1] versus 19.0 [14.1-25.0] J/min; mean difference -1.76 (95%-CI -2.47 to -10.34J/min; P < 0.01), and not in active patients (14.6 [11.0-20.3] vs 14.1 [10.1-21.3] J/min; mean difference 0.81 (95%-CI -2.13 to 0.49) J/min; P = 0.23).

CONCLUSIONS

In this cohort of unselected critically ill invasively ventilated patients, automated ventilation by means of INTELLiVENT-Adaptive Support Ventilation did not reduce mechanical power. A reduction in mechanical power was only seen in passive patients.

STUDY REGISTRATION

Clinicaltrials.gov (study identifier NCT04827927), April 1, 2021.

URL OF TRIAL REGISTRY RECORD

https://clinicaltrials.gov/study/NCT04827927?term=intellipower&rank=1.

Prevalence of mortality among mechanically ventilated patients in the intensive care units of Ethiopian hospitals and the associated factors: A systematic review and meta-analysis

BACKGROUND

In the intensive care unit (ICU), mechanical ventilation (MV) is a typical way of respiratory support. The severity of the illness raises the likelihood of death in patients who require MV. Several studies have been done in Ethiopia; however, the mortality rate differs among them. The objective of this systematic review and meta-analysis is to provide a pooled prevalence of mortality and associated factors among ICU-admitted patients receiving MV in Ethiopian hospitals.

METHODS

We used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 criteria to conduct a comprehensive systematic review and meta-analysis in this study. We searched PubMed/Medline, SCOPUS, Embase, Hinari, and Web of Science and found 22 articles that met our inclusion criteria. We used a random-effects model. To identify heterogeneity within the included studies, meta-regression and subgroup analysis were used. We employed Egger's regression test and funnel plots for assessing publication bias. STATA version 17.0 software was used for all statistical analyses.

RESULTS

In this systematic review and meta-analysis, the pooled prevalence of mortality among 7507 ICU-admitted patients from 22 articles, who received MV was estimated to be 54.74% [95% CI = 47.93, 61.55]. In the subgroup analysis by region, the Southern Nations, Nationalities, and Peoples (SNNP) subgroup (64.28%, 95% CI = 51.19, 77.37) had the highest prevalence. Patients with COVID-19 have the highest mortality rate (75.80%, 95% CI = 51.10, 100.00). Sepsis (OR = 6.85, 95%CI = 3.24, 14.46), Glasgow Coma Scale (GCS) score<8 (OR = 6.58, 95%CI = 1.96, 22.11), admission with medical cases (OR = 4.12, 95%CI = 2.00, 8.48), Multi Organ Dysfunction Syndrome (MODS) (OR = 2.70, 95%CI = 4.11, 12.62), and vasopressor treatment (OR = 19.06, 95%CI = 9.34, 38.88) were all statistically associated with mortality.

CONCLUSION

Our review found that the pooled prevalence of mortality among mechanically ventilated ICU-admitted patients in Ethiopia was considerably high compared to similar studies in the United States (US), China, and other countries. Sepsis, GCS<8, medical cases, MODS, and use of vasopressors were statistically associated with mortality. Clinicians should exercise caution while mechanically ventilating ICU-admitted patients with these factors. However, it should be noted that the exact cause and effect relationship could not be established with this meta-analysis, as the available evidence is not sufficient. Thus, more studies using prospective methods will be required.

Different ventilation intensities among various categories of patients ventilated for reasons other than ARDS--A pooled analysis of 4 observational studies

PURPOSE

We investigated driving pressure (ΔP) and mechanical power (MP) and associations with clinical outcomes in critically ill patients ventilated for reasons other than ARDS.

MATERIALS AND METHODS

Individual patient data analysis of a pooled database that included patients from four observational studies of ventilation. ΔP and MP were compared among invasively ventilated non-ARDS patients with sepsis, with pneumonia, and not having sepsis or pneumonia. The primary endpoint was ΔP; secondary endpoints included MP, ICU mortality and length of stay, and duration of ventilation.

RESULTS

This analysis included 372 (11%) sepsis patients, 944 (28%) pneumonia patients, and 2040 (61%) patients ventilated for any other reason. On day 1, median ΔP was higher in sepsis (14 [11-18] cmH2O) and pneumonia patients (14 [11-18]cmH2O), as compared to patients not having sepsis or pneumonia (13 [10-16] cmH2O) (P < 0.001). Median MP was also higher in sepsis and pneumonia patients. ΔP, as opposed to MP, was associated with ICU mortality in sepsis and pneumonia patients.

CONCLUSIONS

The intensity of ventilation differed between patients with sepsis or pneumonia and patients receiving ventilation for any other reason; ΔP was associated with higher mortality in sepsis and pneumonia patients.

REGISTRATION

This post hoc analysis was not registered; the individual studies that were merged into the used database were registered at clinicaltrials.gov: NCT01268410 (ERICC), NCT02010073 (LUNG SAFE), NCT01868321 (PRoVENT), and NCT03188770 (PRoVENT-iMiC).

Recent advances in cardiorespiratory monitoring in acute respiratory distress syndrome patients

BACKGROUND

Recent advances on cardiorespiratory monitoring applied in ARDS patients undergoing invasive mechanical ventilation and noninvasive ventilatory support are available in the literature and may have potential prognostic implication in ARDS treatment.

MAIN BODY

The measurement of oxygen saturation by pulse oximetry is a valid, low-cost, noninvasive alternative for assessing arterial oxygenation. Caution must be taken in patients with darker skin pigmentation, who may experience a greater incidence of occult hypoxemia. Dead space surrogates, which are easy to calculate, have important prognostic implications. The mechanical power, which can be automatically computed by intensive care ventilators, is an important parameter correlated with ventilator-induced lung injury and outcome. In patients undergoing noninvasive ventilatory support, the use of esophageal pressure can measure inspiratory effort, avoiding possible delays in endotracheal intubation. Fluid responsiveness can also be evaluated using dynamic indices in patients ventilated at low tidal volumes (< 8 mL/kg). In patients ventilated at high levels of positive end expiratory pressure (PEEP), the PEEP test represents a valid alternative to passive leg raising. There is growing evidence on alternative parameters for evaluating fluid responsiveness, such as central venous oxygen saturation variations, inferior vena cava diameter variations and capillary refill time.

CONCLUSION

Careful cardiorespiratory monitoring in patients affected by ARDS is crucial to improve prognosis and to tailor treatment via mechanical ventilatory support.

Adjustments of Ventilator Parameters during Operating Room-to-ICU Transition and 28-Day Mortality

Rationale: Lung-protective mechanical ventilation strategies have been proven beneficial in the operating room (OR) and the ICU. However, differential practices in ventilator management persist, often resulting in adjustments of ventilator parameters when transitioning patients from the OR to the ICU. Objectives: To characterize patterns of ventilator adjustments during the transition of mechanically ventilated surgical patients from the OR to the ICU and assess their impact on 28-day mortality. Methods: Hospital registry study including patients undergoing general anesthesia with continued, controlled mechanical ventilation in the ICU between 2008 and 2022. Ventilator parameters were assessed 1 hour before and 6 hours after the transition. Measurements and Main Results: Of 2,103 patients, 212 (10.1%) died within 28 days. Upon OR-to-ICU transition, VT and driving pressure decreased (-1.1 ml/kg predicted body weight [IQR, -2.0 to -0.2]; P < 0.001; and -4.3 cm H2O [-8.2 to -1.2]; P < 0.001). Concomitantly, respiratory rates increased (+5.0 breaths/min [2.0 to 7.5]; P < 0.001), resulting overall in slightly higher mechanical power (MP) in the ICU (+0.7 J/min [-1.9 to 3.0]; P < 0.001). In adjusted analysis, increases in MP were associated with a higher 28-day mortality rate (adjusted odds ratio, 1.10; 95% confidence interval, 1.06-1.14; P < 0.001; adjusted risk difference, 0.7%; 95% confidence interval, 0.4-1.0, both per 1 J/min). Conclusion: During transition of mechanically ventilated patients from the OR to the ICU, ventilator adjustments resulting in higher MP were associated with a greater risk of 28-day mortality.

Machine learning model for predicting immediate postoperative desaturation using spirometry signal data

Postoperative desaturation is a common post-surgery pulmonary complication. The real-time prediction of postoperative desaturation can become a preventive measure, and real-time changes in spirometry data can provide valuable information on respiratory mechanics. However, there is a lack of related research, specifically on using spirometry signals as inputs to machine learning (ML) models. We developed an ML model and postoperative desaturation prediction index (DPI) by analyzing intraoperative spirometry signals in patients undergoing laparoscopic surgery. We analyzed spirometry data from patients who underwent laparoscopic, robot-assisted gynecologic, or urologic surgery, identifying postoperative desaturation as a peripheral arterial oxygen saturation level below 95%, despite facial oxygen mask usage. We fitted the ML model on two separate datasets collected during different periods. (Datasets A and B). Dataset A (Normal 133, Desaturation 74) was used for the entire experimental process, including ML model fitting, statistical analysis, and DPI determination. Dataset B (Normal 20, Desaturation 4) was only used for verify the ML model and DPI. Four feature categories-signal property, inter-/intra-position correlation, peak value/interval variability, and demographics-were incorporated into the ML models via filter and wrapper feature selection methods. In experiments, the ML model achieved an adequate predictive capacity for postoperative desaturation, and the performance of the DPI was unbiased.

The Impact of Mechanical Energy Assessment on Mechanical Ventilation: A Comprehensive Review and Practical Application

Mechanical ventilation (MV) provides basic organ support for patients who have acute hypoxemic respiratory failure, with acute respiratory distress syndrome as the most severe form. The use of excessive ventilation forces can exacerbate the lung condition and lead to ventilator-induced lung injury (VILI); mechanical energy (ME) or power can characterize such forces applied during MV. The ME metric combines all MV parameters affecting the respiratory system (ie, lungs, chest, and airways) into a single value. Besides evaluating the overall ME, this parameter can be also related to patient-specific characteristics, such as lung compliance or patient weight, which can further improve the value of ME for characterizing the aggressiveness of lung ventilation. High ME is associated with poor outcomes and could be used as a prognostic parameter and indicator of the risk of VILI. ME is rarely determined in everyday practice because the calculations are complicated and based on multiple equations. Although low ME does not conclusively prevent the possibility of VILI (eg, due to the lung inhomogeneity and preexisting damage), individualization of MV settings considering ME appears to improve outcomes. This article aims to review the roles of bedside assessment of mechanical power, its relevance in mechanical ventilation, and its associations with treatment outcomes. In addition, we discuss methods for ME determination, aiming to propose the most suitable method for bedside application of the ME concept in everyday practice.