![]() ![]() However, the mathematical equation for computing MP may not fully capture the predictive information from ventilator variables. MP is a ventilator parameter with concrete meaning in physics. Our third hypothesis was that the MP is more harmful in severe ARDS (e.g., severe ARDS has smaller functional lung size than mild ARDS) than mild ones. Thus, we needed to take the severity of ARDS into the consideration when we examined the association of MP with mortality. ![]() For example, the severely injured lung might have smaller functional lung size regardless of the body height of the patient. Additionally, the functional lung size is not always proportional to the PBW in ARDS patients. Our second goal in this paper was to investigate whether the discrimination power of MP normalized to PBW was better than the absolute value of MP. This is in line with the fact that tidal volume when normalized to predicted body weight (PBW) showed greater accuracy than the absolute value. In other words, the discrimination power of MP normalized to lung size would theoretically be improved as compared with the absolute MP value. We hypothesized that MP-based prediction can be significantly better than other ventilator variables.įurthermore, we know that the effect of MP might be influenced by the functional lung size. The first aim of the study is to empirically compare the discrimination power of using MP in predicting ARDS mortality versus using other individual ventilator variables. However, we are not sure whether MP can better stratify risk than other individual ventilator variables. There was empirical evidence showing that MP was able to predict risk of mortality in mechanically ventilated patients. Since MP integrated many aspects of mechanical ventilation, it is theoretically superior to each of the individual ventilator variables. Mechanical power (MP), as calculated by the combination of tidal volume, PEEP, plateau pressure, peak inspiratory pressure (PIP), and respiratory rate, was proposed to better capture the total energy delivered to the lung parenchyma. Thus, we needed to monitor a number of key parameters to ensure that mechanical ventilation do not lead to VILI, such as low tidal volume, high positive end-expiratory pressure (PEEP), limited plateau pressure and driving pressure. For example, the severity of VILI was demonstrated to be dependent on respiratory rate at a given level of tidal volume. There was experimental evidence showing that VILI was influenced by every aspect of ventilator settings. Mechanical ventilation is strongly recommended for ARDS patients to avert life-threatening hypoxia and hypercapnia in respiratory failure however, it is also associated with ventilator-induced lung injury (VILI). Further experimental trials are needed to investigate whether adjusting ventilator variables according to norMP will significantly improve clinical outcomes. The study showed that norMP was a good ventilator variable associated with mortality, and its predictive discrimination cannot be further improved with a sophisticated machine learning method. While the norMP was not significantly associated with mortality outcome (OR 0.99 95% CI 0.91–1.07 p = 0.862) in patients with mild ARDS, it was associated with increased risk of mortality in moderate (OR 1.11 95% CI 1.02–1.23 p = 0.021) and severe (OR 1.13 95% CI 1.03–1.24 p < 0.008) ARDS. The multivariable regression model showed a significant interaction between norMP and ARDS severity ( p < 0.05). The gradient boosting machine was not able to improve the discrimination as compared to norMP ( p = 0.913 for DeLong’s test). The discrimination of norMP in predicting mortality was significantly better than the absolute MP ( p = 0.011 for DeLong’s test). ResultsĪ total of 5159 patients with acute onset ARDS were included for analysis. ![]() The gradient boosting machine was used to examine whether the discrimination could be further improved. The discrimination of each ventilator variable was calculated in the testing subsample using the area under receiver operating characteristic curve. ![]() The data was split 3:1 into training and testing subsamples. The study included individual patient data from eight randomized controlled trials conducted by the ARDSNet. The study aimed to investigate whether MP normalized to predicted body weight (norMP) was superior to other ventilator variables and to prove that the discrimination power cannot be further improved with a sophisticated machine learning method. More recently, mechanical power (MP) was found to be associated with mortality. Protective mechanical ventilation based on multiple ventilator parameters such as tidal volume, plateau pressure, and driving pressure has been widely used in acute respiratory distress syndrome (ARDS). ![]()
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