A Multi-dimensional Classifier to Support Lung Transplant Referral in Patients with Pulmonary Fibrosis.

Overview

abstract

BACKGROUND: Lung transplantation remains the sole curative option for patients with idiopathic pulmonary fibrosis (IPF), but donor organs remain scarce, and many eligible patients die prior to transplant. Tools to optimize the timing of transplant referral are urgently needed. METHODS: Least absolute shrinkage and selection operator (LASSO) was applied to clinical and proteomic data generated as part of a prospective cohort study of interstitial lung disease (ILD) to derive clinical, proteomic, and multi-dimensional logit models of near-term death or lung transplant within 18 months of blood draw. Model fitted values were dichotomized at the point of maximal sensitivity and specificity and decision curve analysis used to select the best performing classifier. We then applied this classifier to independent IPF and non-IPF ILD cohorts to determine test performance characteristics. Cohorts were restricted to patients aged ≤72 years with body mass index 18-32 to increase likelihood of transplant eligibility. RESULTS: IPF derivation, IPF validation and non-IPF ILD validation cohorts consisted of 314, 105 and 295 patients, respectively. A multi-dimensional model comprising two clinical variables and 20 proteins outperformed stand-alone clinical and proteomic models. Following dichotomization, the multi-dimensional classifier predicted near-term outcome with 70% sensitivity and 92% specificity in the IPF validation cohort and 70% sensitivity and 80% specificity in the non-IPF ILD validation cohort. CONCLUSIONS: A multi-dimensional classifier of near-term outcome accurately discriminated this endpoint with good test performance across independent IPF and non-IPF ILD cohorts. These findings support refinement and prospective validation of this classifier in transplant eligible individuals.