0000000000754472
AUTHOR
G. Dempsey
Correction to: Potentially modifiable factors contributing to outcome from acute respiratory distress syndrome: the LUNG SAFE study (Intensive Care Medicine, (2016), 42, 12, (1865-1876), 10.1007/s00134-016-4571-5)
The members of the LUNG SAFE Investigators and the ESICM Trials Group were provided in such a way that they could not be indexed as collaborators on PubMed. The publisher apologizes for this error.
Resolved versus confirmed ARDS after 24 h: insights from the LUNG SAFE study
Purpose: To evaluate patients with resolved versus confirmed ARDS, identify subgroups with substantial mortality risk, and to determine the utility of day 2 ARDS reclassification. Methods: Our primary objective, in this secondary LUNG SAFE analysis, was to compare outcome in patients with resolved versus confirmed ARDS after 24 h. Secondary objectives included identifying factors associated with ARDS persistence and mortality, and the utility of day 2 ARDS reclassification. Results: Of 2377 patients fulfilling the ARDS definition on the first day of ARDS (day 1) and receiving invasive mechanical ventilation, 503 (24%) no longer fulfilled the ARDS definition the next day, 52% of whom initial…
Relationship between the Clinical Frailty Scale and short-term mortality in patients ≥ 80 years old acutely admitted to the ICU: a prospective cohort study.
Abstract Background The Clinical Frailty Scale (CFS) is frequently used to measure frailty in critically ill adults. There is wide variation in the approach to analysing the relationship between the CFS score and mortality after admission to the ICU. This study aimed to evaluate the influence of modelling approach on the association between the CFS score and short-term mortality and quantify the prognostic value of frailty in this context. Methods We analysed data from two multicentre prospective cohort studies which enrolled intensive care unit patients ≥ 80 years old in 26 countries. The primary outcome was mortality within 30-days from admission to the ICU. Logistic regression models for…