PT - JOURNAL ARTICLE AU - Jesús Villar AU - Lina Pérez-Méndez AU - Santiago Basaldúa AU - Jesús Blanco AU - Gerardo Aguilar AU - Darío Toral AU - Elizabeth Zavala AU - Miguel A Romera AU - Gumersindo González-Díaz AU - Frutos del Nogal AU - Antonio Santos-Bouza AU - Luís Ramos AU - Santiago Macías AU - Robert M Kacmarek AU - Hospitales Españoles Para el Estudio de la Lesión Pulmonar (HELP) Network TI - A Risk Tertiles Model for Predicting Mortality in Patients With Acute Respiratory Distress Syndrome: Age, Plateau Pressure, and P<sub>aO<sub>2</sub></sub>/F<sub>IO<sub>2</sub></sub> at ARDS Onset Can Predict Mortality AID - 10.4187/respcare.00811 DP - 2011 Apr 01 TA - Respiratory Care PG - 420--428 VI - 56 IP - 4 4099 - http://rc.rcjournal.com/content/56/4/420.short 4100 - http://rc.rcjournal.com/content/56/4/420.full AB - 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 (Pplat), and PaO2/FIO2 at ARDS onset identified subgroups with different mortalities, particularly for the highest-risk tertiles: age (&gt; 62 years), Pplat (&gt; 29 cm H2O), and PaO2/FIO2 (&lt; 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 &lt; .001). CONCLUSIONS: A prediction model based on tertiles of patient age, Pplat, and PaO2/FIO2 at the time the patient meets ARDS criteria identifies patients with the lowest and highest risk of intensive care unit death.