Acute lung injury in patients with traumatic injuries: utility of a panel of biomarkers for diagnosis and pathogenesis

Abstract

Background: The diagnosis of acute lung injury (ALI) is based on a consensus clinical definition. Despite the simplicity of this definition, ALI remains underdiagnosed and undertreated. Severe trauma is a well-described cause of ALI that represents a relatively homogeneous subset of patients with ALI. The aims of this study were to develop a panel of plasma biomarkers to facilitate diagnosis of trauma-induced ALI and to enhance our understanding of the pathogenesis of human ALI.

Methods: A retrospective nested case control of 192 patients admitted to the trauma intensive care unit at a university hospital between 2002 and 2006. We compared 107 patients with ALI to 85 patients without ALI. Plasma was collected within 72 hours of intensive care unit admission. Twenty-one plasma biomarkers were measured in duplicate in each plasma sample.

Results: Patients with ALI had higher severity of illness scores, more days of mechanical ventilation, longer hospital stays, and higher mortality versus controls. Seven biomarkers (receptor for advanced glycation end products, procollagen peptide III, brain natriuretic peptide, angiopoietin-2, interleukin-10, tumor necrosis factor alpha, and interleukin-8) had a high diagnostic accuracy as reflected by the area under the receiver operating characteristic curve of 0.86 (95% confidence interval, 0.82-0.92) in differentiating ALI from controls.

Conclusions: A model using seven plasma biomarkers had a high diagnostic accuracy in differentiating patients with trauma-induced ALI from trauma patients without ALI. In addition, use of a panel of biomarkers provides insight into the likely importance of alveolar epithelial injury in the pathogenesis of early ALI.

Figure 1

Receiver operator characteristic (ROC) curve analysis of the plasma biomarker panels for differentiating ALI/ARDS (cases) from controls. Predicted probability of recurrence of ALI/ARDS for each subject was computed from a logistic regression model that includes the 7 biomarkers (RAGE, PCPIII, BNP, ANG2, IL10, TNF-α, and IL8). Specificity and sensitivity were computed at each possible cutoff of the predicted probability. The area under the curve (AUC) is 0.86 (95% CI 0.87 – 0.95). The dashed line shows the ROC analysis using only the 3 most discriminatory biomarkers (RAGE, PCPIII and BNP). The area under the curve for this model is 0.83 (95% CI 0.84 – 0.94).

Figure 2

The multi-variable logistic regression model was used to create a prediction model for the probability of ALI. A value in each predictor variable corresponds to a point scale at the top. The sum of the individual predictor variable points corresponds to the total points and the probability of ALI shown at the bottom. For each predictor variable, the shown values are approximately 1^st, 5^th, 25th, 50^th, 75^th, 95^th, and 99^th percentiles.