4.5 Article

Possible UIP pattern on high-resolution computed tomography is associated with better survival than definite UIP in IPF patients

Journal

RESPIRATORY MEDICINE
Volume 131, Issue -, Pages 229-235

Publisher

W B SAUNDERS CO LTD
DOI: 10.1016/j.rmed.2017.08.025

Keywords

Pulmonary fibrosis; Prognosis; High resolution computed tomography; Usual interstitial pneumonia

Funding

  1. [T32 HL007749-21]
  2. [K24 HL111316]

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Background: Idiopathic pulmonary fibrosis (IPF) is a progressive fibrosing lung disease of unknown etiology. Inter-society consensus guidelines on IPF diagnosis and management outline radiologic patterns including definite usual interstitial pneumonia (UIP), possible UIP, and inconsistent with UIP. We evaluate these diagnostic categories as prognostic markers among patients with IPF. Methods: Included subjects had biopsy-proven UIP, a multidisciplinary team diagnosis of IPF, and a baseline high-resolution computed tomography (HRCT). Thoracic radiologists assigned the radiologic pattern and documented the presence and extent of specific radiologic findings. The outcome of interest was lung transplant-free survival. Results: IPF patients with a possible UIP pattern on HRCT had significantly longer Kaplan-Meier event free survival compared to those with definite UIP pattern (5.21 and 3.57 years, respectively, p = 0.002). In a multivariable Cox proportional hazards model adjusted for baseline age, gender, %-predicted FVC, and %-predicted DLCO via the GAP Stage, extent of fibrosis (via the traction bronchiectasis score) and ever-smoker status, possible UIP pattern on HRCT (versus definite UIP) was associated with reduced hazard of death or lung transplant (HR = 0.42, CI 95% 0.23-0.78, p = 0.006). Conclusions: Radiologic diagnosis categories outlined by inter-society consensus guidelines is a widely reported and potentially useful prognostic marker in IPF patients, with possible UIP pattern on HRCT associated with a favorable prognosis compared to definite UIP pattern, after adjusting for relevant covariates. (C) 2017 Elsevier Ltd. All rights reserved.

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