Models of disease behavior in idiopathic pulmonary fibrosis

Early risk prediction models incorporated baseline clinical and radiographic parameters to predict mortality in IPF. The composite clinical, radiological, and physiological scoring system identified age, clubbing, smoking history, lung volumes, end-exercise hypoxemia, and chest radiographic evidence of pulmonary hypertension and interstitial abnormalities to be associated with survival [3, 4]. The Composite Physiologic Index was similarly developed, incorporating three lung function parameters to predict mortality and accounting for the confounding effects of concomitant emphysema in IPF patients [5], a limitation of prior models.

More recently, du Bois et al. [6] developed a risk scoring system based on age, history of respiratory hospitalization, baseline forced vital capacity (FVC), and change in FVC over 24 weeks to predict mortality. This was subsequently modified to include a functional and longitudinal parameter, the 6-minute walk distance (6MWD) and change in this parameter over 24 weeks [7]. Ley et al. [8] derived and validated the ‘gender, age, physiology’ (GAP) model, which identified four readily available baseline parameters, namely gender, age, FVC, and diffusion capacity of the lung for carbon monoxide (D_LCO), to develop staging and risk prediction scores. An alternative model in which the extent of fibrosis on high resolution computed tomography of the chest was used in place of the D_LCO performed equally well [9]. The original du Bois and GAP models have subsequently been combined to provide an integrated baseline and longitudinal risk prediction approach [10].

These clinical models have demonstrated the impact of cohort characteristics on calibration of risk. This is most obvious in comparing risk in referral center-based cohorts and clinical trial cohorts. Models derived in center-based cohorts appear to significantly overestimate mortality risk in clinical trial cohorts, where patients are highly selected [11]. Additionally, age and gender appear to be more relevant prognostic variables in clinical cohorts, perhaps through capturing the influence of comorbidities, while adding relatively little in a clinical trial cohort. Thus, calibration of risk prediction models to the population of interest appears critical to accurate quantification of risk.

Clinical risk prediction models provide important prognostic tools for practice and clinical trial development. However, their performance remains modest, likely because clinical markers are limited in their inability to directly assess the underlying pathobiology and disease activity. Translational studies are providing novel tools in the form of molecular and genetic biomarkers to address this limitation.

Several recent studies have identified molecular and genetic biomarkers associated with clinical outcomes in IPF [12]. These can be divided into three categories: genetic, protein, and cellular.

Genetic-based biomarkers associated with worse survival in IPF include mucin 5B promoter polymorphisms [13], shorter leucocyte telomere length [14], and the toll-interacting protein single nucleotide polymorphism [15]. Protein-based biomarkers that have been associated with worse outcomes in IPF include surfactant proteins A (SP-A) [16] and D [17], Krebs von den Lungen-6 (KL-6) [18, 19], CC-chemokine-ligand-18 [20], C-X-C motif chemokine 13 [21, 22], matrix metalloproteinase (MMP)-3 [22] and MMP-7 [23], fibulin-1 [24], interleukin-8 and intercellular cell adhesion molecule-1 [23], osteopontin [25], periostin [26, 27], and collagen degradation products [28]. Cellular biomarkers associated with worse outcomes in IPF include regulatory T cells (Tregs) [29], semaphorin 7a + Tregs [30], and circulating fibrocytes [31].

Molecular and genetic biomarkers seem certain to add to the predictive abilities of currently available clinical risk prediction models. To date, few studies have examined this additive benefit and rigorous validation is lacking, but superior model performance has been suggested with certain combinations of clinical variables and biomarkers [13, 18, 23, 32]. Song et al. [18] proposed that the combination of at least three biomarkers (e.g., MMP-7, SP-A, and KL-6) improved risk prediction over clinical variables alone. Clearly, more needs to be done to clarify the additive role of molecular and genetic biomarkers.