Integrating Clinical Probability into the Diagnostic Approach to Idiopathic Pulmonary Fibrosis An International Working Group Perspective

Background: When considering the diagnosis of idiopathic pulmonary fibrosis (IPF), experienced clinicians integrate clinical features that help to differentiate IPF from other fibrosing interstitial lung diseases, thus generating a "pre-test" probability of IPF. The aim of this international working group perspective was to summarize these features using a tabulated approach similar to chest HRCT and histopathologic patterns reported in the international guidelines for the diagnosis of IPF, and to help formally incorporate these clinical likelihoods into diagnostic reasoning to facilitate the diagnosis of IPF. Methods: The committee group identified factors that influence the clinical likelihood of a diagnosis of IPF, which was categorized as a pre-test clinical probability of IPF into "high" (70-100%), "intermediate" (30-70%), or "low" (0-30%). After integration of radiological and histopathological features, the post-test probability of diagnosis was categorized into "definite" (90-100%), "high confidence" (70-89%), "low confidence" (51-69%), or "low" (0-50%) probability of IPF. Findings: A conceptual Bayesian framework was created, integrating the clinical likelihood of IPF ("pre-test probability of IPF") with the HRCT pattern, the histopathology pattern when available, and/or the pattern of observed disease behavior, into a "post-test probability of IPF." The diagnostic probability of IPF was expressed using an adapted diagnostic ontology for fibrotic interstitial lung diseases. Interpretation: The present approach will help incorporate the clinical judgment into the diagnosis of IPF, thus facilitating the application of IPF diagnostic guidelines and, ultimately improving diagnostic confidence and reducing the need for invasive diagnostic techniques.

Publication year:2022

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