AI Article Synopsis

  • The study focused on predicting mortality in patients with acute respiratory distress syndrome (ARDS) by examining respiratory and ventilatory variables at the onset of the condition.
  • Researchers analyzed data from 220 mechanically ventilated ARDS patients across two trials and developed a simple prediction model that categorized patients into low, middle, and high-risk groups based on key measurements.
  • Results showed that specific factors like age, plateau airway pressure, and oxygenation levels could effectively stratify patients by their risk of death in the ICU, with a notable difference in mortality rates between those with no high-risk factors and those with multiple.

Article Abstract

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.

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http://dx.doi.org/10.4187/respcare.00811DOI Listing

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