Figure 3

The LASSO-identified signature based on blood and BAL proteins separated progressors and non-progressors with high accuracy and significantly outperformed analyses based on individual factors. (a) PLSDA scores plot based on blood and BAL proteins highlights strong differentiation between progressors (cyan) and non-progressors (purple); the model separated the two groups with 100% cross-validation and calibration accuracy. (b) The loadings on latent variable 1 (LV1) captured 8.75% of the total variance in the data, with negatively loaded proteins being comparatively increased in progressors and positively loaded proteins being comparatively reduced. (c) Comparison of the calibration accuracies between analyses based on data-driven signatures and univariate factors. The LASSO-selected PLSDA model based on blood and BAL proteins had significantly higher calibration accuracy than all analyses based on single proteins and a model based on the collection of all 28 significantly different proteins identified in Fig. 1 (Cochran’s Q test with McNemar’s post hoc test; *p < 0.05; ***p < 0.001). (d) Comparison of cross-validation accuracies between analyses based on data-driven signatures and univariate factors. The LASSO-selected PLSDA model based on blood and BAL proteins had significantly higher cross-validation accuracy than all analyses based on single proteins and trended towards better cross-validation accuracy than a model based on the 28 proteins identified in Fig. 1 (one-way ANOVA with Tukey’s post hoc test; *p < 0.05; ***p < 0.001). (e) Comparison of sensitivity between the LASSO-selected PLSDA model based on blood and BAL proteins and previously published models of IPF progression (serum fibulin-1³⁰, plasma MMP-7³¹, plasma SP-A³¹, and an additive combination of blood factors²⁰). (f) Comparison of specificity between the LASSO-selected PLSDA model based on blood and BAL proteins and previously published models of IPF progression.