scholarly journals Prevalence, potential risk factors and mortality rates of acute respiratory distress syndrome in Chinese patients with sepsis

2020 ◽  
Vol 48 (2) ◽  
pp. 030006051989565 ◽  
Author(s):  
Shilei Li ◽  
Danna Zhao ◽  
Jie Cui ◽  
Lizeng Wang ◽  
Xiaohua Ma ◽  
...  
2021 ◽  
pp. 2100857
Author(s):  
Alexandre Tran ◽  
Shannon M. Fernando ◽  
Laurent J. Brochard ◽  
Eddy Fan ◽  
Kenji Inaba ◽  
...  

PurposeTo summarise the prognostic associations between various clinical risk factors and the development of the acute respiratory distress syndrome (ARDS) following traumatic injury.MethodsWe conducted this review in accordance with the PRISMA and CHARMS guidelines. We searched six databases from inception through December 2020. We included English language studies describing the clinical risk factors associated with the development of post-traumatic ARDS, as defined by either the American-European Consensus Conference or the Berlin definition. We pooled adjusted odds ratios for prognostic factors using the random effects method. We assessed risk of bias using the QUIPS tool and certainty of findings using GRADE methodology.ResultsWe included 39 studies involving 5 350 927 patients. We identified the amount of crystalloid resuscitation as a potentially modifiable prognostic factor associated with the development of post-traumatic ARDS (adjusted odds ratio [aOR] 1.19 for each additional liter of crystalloid administered within first 6 h after injury, 95% CI 1.15 to 1.24, high certainty). Non-modifiable prognostic factors with a moderate or high certainty of association with post-traumatic ARDS included increasing age, non-Hispanic white race, blunt mechanism of injury, presence of head injury, pulmonary contusion, or rib fracture; and increasing chest injury severity.ConclusionWe identified one important modifiable factor, the amount of crystalloid resuscitation within the first 24 h of injury, and several non-modifiable factors associated with development of post-traumatic ARDS. This information should support the judicious use of crystalloid resuscitation in trauma patients and may inform the development of a risk-stratification tools.


2019 ◽  
Vol 17 (1) ◽  
Author(s):  
Xian-Fei Ding ◽  
Jin-Bo Li ◽  
Huo-Yan Liang ◽  
Zong-Yu Wang ◽  
Ting-Ting Jiao ◽  
...  

Abstract Background To develop a machine learning model for predicting acute respiratory distress syndrome (ARDS) events through commonly available parameters, including baseline characteristics and clinical and laboratory parameters. Methods A secondary analysis of a multi-centre prospective observational cohort study from five hospitals in Beijing, China, was conducted from January 1, 2011, to August 31, 2014. A total of 296 patients at risk for developing ARDS admitted to medical intensive care units (ICUs) were included. We applied a random forest approach to identify the best set of predictors out of 42 variables measured on day 1 of admission. Results All patients were randomly divided into training (80%) and testing (20%) sets. Additionally, these patients were followed daily and assessed according to the Berlin definition. The model obtained an average area under the receiver operating characteristic (ROC) curve (AUC) of 0.82 and yielded a predictive accuracy of 83%. For the first time, four new biomarkers were included in the model: decreased minimum haematocrit, glucose, and sodium and increased minimum white blood cell (WBC) count. Conclusions This newly established machine learning-based model shows good predictive ability in Chinese patients with ARDS. External validation studies are necessary to confirm the generalisability of our approach across populations and treatment practices.


2017 ◽  
Vol 42 ◽  
pp. 390
Author(s):  
Eduardo Mantovani Cardoso ◽  
Aniele Tomadon ◽  
Keli Lovison ◽  
Péricles Almeida Delfino Duarte

2015 ◽  
Vol 42 (2) ◽  
pp. 164-172 ◽  
Author(s):  
Aude Gibelin ◽  
Antoine Parrot ◽  
Bernard Maitre ◽  
Christian Brun-Buisson ◽  
Armand Mekontso Dessap ◽  
...  

2019 ◽  
Vol 54 (7) ◽  
pp. 1405-1410 ◽  
Author(s):  
Amory de Roulet ◽  
Rita V. Burke ◽  
Joanna Lim ◽  
Stephanie Papillon ◽  
David W. Bliss ◽  
...  

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