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. 2020 Dec 15;202(12):1720-1724.
doi: 10.1164/rccm.202003-0724LE.

Preclinical Pulmonary Fibrosis Circulating Protein Biomarkers

Susan K Mathai  1   2 , Jonathan Cardwell  1 , Fabian Metzger  3 , Julia Powers  1 , Avram D Walts  1 , Jonathan A Kropski  4 , Oliver Eickelberg  1 , Stefanie M Hauck  3 , Ivana V Yang  1 , David A Schwartz  1
Affiliations

Preclinical Pulmonary Fibrosis Circulating Protein Biomarkers

Susan K Mathai et al. Am J Respir Crit Care Med. .
No abstract available

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Figures

Figure 1.
Figure 1.
Predictive model for preclinical pulmonary fibrosis using top plasma proteins and patient characteristics. When compared with a model utilizing age and sex alone, including the top four proteins (S100A9, LBP, CRISP3, and CRKL) in addition to age and sex in a predictive model for preclinical pulmonary fibrosis improved the receiver operating characteristic curve performance based on comparing areas under the curve (AUCs). The AUC for the model including age, sex, and the four proteins was 0.86 (95% confidence interval [CI], 0.82–0.89; sensitivity, 0.77; specificity, 0.90; blue line) versus the AUC of 0.77 (95% CI, 0.72–0.82; sensitivity, 0.68; specificity, 0.89; black line) for the model using only age and sex. The negative predictive value and the positive predictive values of the age and sex model were 0.90 and 0.66 versus 0.93 and 0.70 for the model including age, sex, and protein levels. Adding MUC5B genotype to age and sex did not significantly improve the predictive ability of the model (red line) compared with including only age and sex (black line) or including the top four proteins (blue line).
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