A systematic review of validated methods for identifying pulmonary fibrosis and interstitial lung disease using administrative and claims data

Abstract

The Food and Drug Administration's Mini-Sentinel pilot program initially aimed to conduct active surveillance to refine safety signals that emerge for marketed medical products. A key facet of this surveillance is to develop and understand the validity of algorithms for identifying health outcomes of interest (HOIs) from administrative and claims data. This paper summarizes the process and findings of the algorithm review of pulmonary fibrosis and interstitial lung disease. PubMed and Iowa Drug Information Service Web searches were conducted to identify citations applicable to the pulmonary fibrosis/interstitial lung disease HOI. Level 1 abstract reviews and Level 2 full-text reviews were conducted to find articles using administrative and claims data to identify pulmonary fibrosis and interstitial lung disease, including validation estimates of the coding algorithms. Our search revealed a deficiency of literature focusing on pulmonary fibrosis and interstitial lung disease algorithms and validation estimates. Only five studies provided codes; none provided validation estimates. Because interstitial lung disease includes a broad spectrum of diseases, including pulmonary fibrosis, the scope of these studies varied, as did the corresponding diagnostic codes used. Research needs to be conducted on designing validation studies to test pulmonary fibrosis and interstitial lung disease algorithms and estimating their predictive power, sensitivity, and specificity.