• Jesús Villar, Lina Pérez-Méndez, Santiago Basaldúa, Jesús Blanco, Gerardo Aguilar, Darío Toral, Elizabeth Zavala, Miguel A Romera, Gumersindo González-Díaz, Frutos Del Nogal, Antonio Santos-Bouza, Luís Ramos, Santiago Macías, Robert M Kacmarek, and Hospitales Españoles Para el Estudio de la Lesión Pulmonar (HELP) Network*.
• Centro de Investigación Biomédica en Red (CIBER) de Enfermedades Respiratorias, Instituto de Salud Carlos III, Madrid, Spain. jesus.villar54@gmail.com
• Resp Care. 2011 Apr 1;56(4):420-8.

BackgroundPredicting 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.MethodsWe 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).ResultsTertile 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).ConclusionsA 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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