Critical care medicine · 2012
Acute respiratory distress syndrome after trauma: development and validation of a predictive model.
СтатьяПоддержка NIH: внешняя программаНазвание и аннотация: Перевод в очереди
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Watkins Timothy R, Nathens Avery B, Cooke Colin R, Psaty Bruce M, Maier Ronald V, Cuschieri Joseph, Rubenfeld Gordon D
Аннотация
OBJECTIVE: To determine early clinical predictors of acute respiratory distress syndrome after major traumatic injury and characterize the performance of this acute respiratory distress syndrome prediction model, and two previously published acute respiratory distress syndrome prediction models, in an independent cohort of severely injured patients.
DESIGN: Prospective cohort study.
SETTING: University-affiliated level I trauma center in Seattle, WA, and nine hospitals participating in the Inflammation and Host Response to Injury Consortium.
PATIENTS: Model derivation utilized data from 224 patients participating in a randomized controlled trial. All models were validated in an independent cohort of 1,762 trauma patients.
MEASUREMENTS AND MAIN RESULTS: Variables strongly associated with acute respiratory distress syndrome in bivariate analysis (p<.01) were entered into a multiple logistic regression equation to generate an acute respiratory distress syndrome predictive model. We evaluated the performance of all models using the area under the receiver operator characteristic curve. Acute respiratory distress syndrome occurred in 79 subjects (35%) belonging to the development cohort and in 423 subjects (24%) from the validation cohort. Multivariable predictors of acute respiratory distress syndrome after trauma included subject age, Acute Physiology and Chronic Health Evaluation II Score, injury severity score, and the presence of blunt traumatic injury, pulmonary contusion, massive transfusion, and flail chest injury (area under the receiver operator characteristic curve 0.79 [95% confidence interval 0.73, 0.85]). Validation of the prediction model resulted in an area under the receiver operator characteristic curve of 0.71 (95% confidence interval 0.68, 0.74). Our model's performance in the validation cohort was superior to that of two other published acute respiratory distress syndrome prediction models (0.65 [95% confidence interval 0.63, 0.68] and 0.66 [95% confidence interval 0.64, 0.69], p<.01 for all comparisons).
CONCLUSIONS: Using routinely available clinical data, our prediction model identifies patients at high risk for acute respiratory distress syndrome early after severe traumatic injury. This predictive model could facilitate enrollment of subjects into future clinical trials designed to prevent this serious complication.
DESIGN: Prospective cohort study.
SETTING: University-affiliated level I trauma center in Seattle, WA, and nine hospitals participating in the Inflammation and Host Response to Injury Consortium.
PATIENTS: Model derivation utilized data from 224 patients participating in a randomized controlled trial. All models were validated in an independent cohort of 1,762 trauma patients.
MEASUREMENTS AND MAIN RESULTS: Variables strongly associated with acute respiratory distress syndrome in bivariate analysis (p<.01) were entered into a multiple logistic regression equation to generate an acute respiratory distress syndrome predictive model. We evaluated the performance of all models using the area under the receiver operator characteristic curve. Acute respiratory distress syndrome occurred in 79 subjects (35%) belonging to the development cohort and in 423 subjects (24%) from the validation cohort. Multivariable predictors of acute respiratory distress syndrome after trauma included subject age, Acute Physiology and Chronic Health Evaluation II Score, injury severity score, and the presence of blunt traumatic injury, pulmonary contusion, massive transfusion, and flail chest injury (area under the receiver operator characteristic curve 0.79 [95% confidence interval 0.73, 0.85]). Validation of the prediction model resulted in an area under the receiver operator characteristic curve of 0.71 (95% confidence interval 0.68, 0.74). Our model's performance in the validation cohort was superior to that of two other published acute respiratory distress syndrome prediction models (0.65 [95% confidence interval 0.63, 0.68] and 0.66 [95% confidence interval 0.64, 0.69], p<.01 for all comparisons).
CONCLUSIONS: Using routinely available clinical data, our prediction model identifies patients at high risk for acute respiratory distress syndrome early after severe traumatic injury. This predictive model could facilitate enrollment of subjects into future clinical trials designed to prevent this serious complication.