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Mechanical power normalized to predicted body weight as a predictor of mortality in patients with acute respiratory distress syndrome.
Pubmed ID: 31062050
Journal: Intensive care medicine
Publication Date: June 1, 2019
Affiliation: Department of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, No 3, East Qingchun Road, Hangzhou, 310016, Zhejiang, China.
MeSH Terms: Humans, Male, Adult, Female, Logistic Models, Middle Aged, Body Mass Index, ROC Curve, Multivariate Analysis, Mortality, Respiration, Artificial, Area Under Curve, Mechanical Phenomena, Respiratory Distress Syndrome
Grants: LGF18H150005, Y201737841
Authors: Hong Y, Zhang Z, Liu N, Zheng B, Ge H
Cite As: Zhang Z, Zheng B, Liu N, Ge H, Hong Y. Mechanical power normalized to predicted body weight as a predictor of mortality in patients with acute respiratory distress syndrome. Intensive Care Med 2019 Jun;45(6):856-864. Epub 2019 May 6.
Studies:
- Acute Respiratory Distress Network (ARDSNet) Studies 01 and 03 Lower versus higher tidal volume, ketoconazole treatment and lisofylline treatment (ARMA/KARMA/LARMA)
- Acute Respiratory Distress Network (ARDSNet) Studies 06 and 08 Prospective, Randomized, Multicenter Trial of Aerosolized Albuterol Versus Placebo for the Treatment of Acute Lung Injury (ALTA)
- Acute Respiratory Distress Network (ARDSNet) Studies 07 and 08 Prospective, Randomized, Blinded, Placebo-controlled, Multi-center Trial of Omega-3 Fatty Acid, Gamma-Linolenic Acid, and Anti-Oxidant Supplementation in the Management of Acute Lung Injury or Acute Respiratory Distress Syndrome (Omega)
- Acute Respiratory Distress Network (ARDSNet) Studies 10 and 12 Statins for Acutely Injured Lungs from Sepsis (SAILS)
- Acute Respiratory Distress Network (ARDSNet) Study 04 Assessment of Low tidal Volume and elevated End-expiratory volume to Obviate Lung Injury (ALVEOLI)
- Acute Respiratory Distress Network (ARDSNet) Study 05 Fluid and Catheter Treatment Trial (FACTT)
Abstract
PURPOSE: 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). More recently, mechanical power (MP) was found to be associated with mortality. 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. METHODS: The study included individual patient data from eight randomized controlled trials conducted by the ARDSNet. The data was split 3:1 into training and testing subsamples. The discrimination of each ventilator variable was calculated in the testing subsample using the area under receiver operating characteristic curve. The gradient boosting machine was used to examine whether the discrimination could be further improved. RESULTS: A total of 5159 patients with acute onset ARDS were included for analysis. The discrimination of norMP in predicting mortality was significantly better than the absolute MP (p = 0.011 for DeLong's test). The gradient boosting machine was not able to improve the discrimination as compared to norMP (p = 0.913 for DeLong's test). The multivariable regression model showed a significant interaction between norMP and ARDS severity (p < 0.05). 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. CONCLUSIONS: 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. Further experimental trials are needed to investigate whether adjusting ventilator variables according to norMP will significantly improve clinical outcomes.