In:
Critical Care Medicine, Ovid Technologies (Wolters Kluwer Health), Vol. 44, No. 7 ( 2016-07), p. 1361-1369
Abstract:
Although there is general agreement on the characteristic features of the acute respiratory distress syndrome, we lack a scoring system that predicts acute respiratory distress syndrome outcome with high probability. Our objective was to develop an outcome score that clinicians could easily calculate at the bedside to predict the risk of death of acute respiratory distress syndrome patients 24 hours after diagnosis. Design: A prospective, multicenter, observational, descriptive, and validation study. Setting: A network of multidisciplinary ICUs. Patients: Six-hundred patients meeting Berlin criteria for moderate and severe acute respiratory distress syndrome enrolled in two independent cohorts treated with lung-protective ventilation. Interventions: None. Measurements and Main Results: Using individual demographic, pulmonary, and systemic data at 24 hours after acute respiratory distress syndrome diagnosis, we derived our prediction score in 300 acute respiratory distress syndrome patients based on stratification of variable values into tertiles, and validated in an independent cohort of 300 acute respiratory distress syndrome patients. Primary outcome was in-hospital mortality. We found that a 9-point score based on patient’s age, Pa o 2 /F io 2 ratio, and plateau pressure at 24 hours after acute respiratory distress syndrome diagnosis was associated with death. Patients with a score greater than 7 had a mortality of 83.3% (relative risk, 5.7; 95% CI, 3.0–11.0), whereas patients with scores less than 5 had a mortality of 14.5% ( p 〈 0.0000001). We confirmed the predictive validity of the score in a validation cohort. Conclusions: A simple 9-point score based on the values of age, Pa o 2 /F io 2 ratio, and plateau pressure calculated at 24 hours on protective ventilation after acute respiratory distress syndrome diagnosis could be used in real time for rating prognosis of acute respiratory distress syndrome patients with high probability.
Type of Medium:
Online Resource
ISSN:
0090-3493
DOI:
10.1097/CCM.0000000000001653
Language:
English
Publisher:
Ovid Technologies (Wolters Kluwer Health)
Publication Date:
2016
detail.hit.zdb_id:
2034247-0
