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Comparative Study
Serum surfactant protein-A is a strong predictor of early mortality in idiopathic pulmonary fibrosis.
- Brent W Kinder, Kevin K Brown, Francis X McCormack, Joachim H Ix, Alma Kervitsky, Marvin I Schwarz, and Talmadge E King.
- University of Cincinnati College of Medicine, Division of Pulmonary, Critical Care and Sleep Medicine, 231 Albert Sabin Way, ML 0564, Cincinnati, OH 45267, USA. brent.kinder@uc.edu
- Chest. 2009 Jun 1;135(6):1557-63.
BackgroundSerum surfactant protein (SP) A and SP-D had prognostic value for mortality in patients with idiopathic pulmonary fibrosis (IPF) in prior studies before the reclassification of the idiopathic interstitial pneumonias. We hypothesized that baseline serum SP-A and SP-D concentrations would be independently associated with mortality among patients with biopsy-proven IPF and would improve a prediction model for mortality.MethodsWe evaluated the association between serum SP-A and SP-D concentrations and mortality in 82 patients with surgical lung biopsy-proven IPF. Regression models with clinical predictors alone and clinical and biomarker predictors were used to predict mortality at 1 year.ResultsAfter controlling for known clinical predictors of mortality, we found that each increase of 49 ng/mL (1 SD) in baseline SP-A level was associated with a 3.3-fold increased risk of mortality (adjusted hazard ratio, 3.27; 95% confidence interval, 1.49 to 7.17; adjusted p = 0.003) in the first year after presentation. We did not observe a statistically significant association between serum SP-D and mortality (adjusted hazard ratio, 2.04; p = 0.053). Regression models demonstrated a significant improvement in the 1-year mortality prediction model when serum SP-A and SP-D (area under the receiving operator curve [AROC], 0.89) were added to the clinical predictors alone (AROC, 0.79; p = 0.03).ConclusionsIncreased serum SP-A level is a strong and independent predictor of early mortality among patients with IPF. A prediction model containing SP-A and SP-D was substantially superior to a model with clinical predictors alone.
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