A risk tertiles model for predicting mortality in patients with acute respiratory distress syndrome: age, plateau pressure, and P(aO(2))/F(IO(2)) at ARDS onset can predict mortality

Respir Care. 2011 Apr;56(4):420-8. doi: 10.4187/respcare.00811. Epub 2011 Jan 21.


Background: Predicting mortality has become a necessary step for selecting patients for clinical trials and defining outcomes. We examined whether stratification by tertiles of respiratory and ventilatory variables at the onset of acute respiratory distress syndrome (ARDS) identifies patients with different risks of death in the intensive care unit.

Methods: We performed a secondary analysis of data from 220 patients included in 2 multicenter prospective independent trials of ARDS patients mechanically ventilated with a lung-protective strategy. Using demographic, pulmonary, and ventilation data collected at ARDS onset, we derived and validated a simple prediction model based on a population-based stratification of variable values into low, middle, and high tertiles. The derivation cohort included 170 patients (all from one trial) and the validation cohort included 50 patients (all from a second trial).

Results: Tertile distribution for age, plateau airway pressure (P(plat)), and P(aO(2))/F(IO(2)) at ARDS onset identified subgroups with different mortalities, particularly for the highest-risk tertiles: age (> 62 years), P(plat) (> 29 cm H(2)O), and P(aO(2))/F(IO(2)) (< 112 mm Hg). Risk was defined by the number of coexisting high-risk tertiles: patients with no high-risk tertiles had a mortality of 12%, whereas patients with 3 high-risk tertiles had 90% mortality (P < .001).

Conclusions: A prediction model based on tertiles of patient age, P(plat), and P(aO(2))/F(IO(2)) at the time the patient meets ARDS criteria identifies patients with the lowest and highest risk of intensive care unit death.

Publication types

  • Multicenter Study
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Age Factors
  • Chi-Square Distribution
  • Female
  • Humans
  • Intensive Care Units*
  • Male
  • Middle Aged
  • Monte Carlo Method
  • Predictive Value of Tests
  • Prospective Studies
  • Respiration, Artificial
  • Respiratory Distress Syndrome / mortality*
  • Respiratory Distress Syndrome / therapy
  • Respiratory Function Tests
  • Risk Assessment
  • Statistics, Nonparametric