Margaret L Salisbury1, Meng Xia2, Susan Murray3, Brian J Bartholmai4, Ella A Kazerooni5, Catherine A Meldrum6, Fernando J Martinez7, Kevin R Flaherty8. 1. Division of Pulmonary and Critical Care Medicine, Department of Medicine, University of Michigan, 1500 E Medical Center Drive 3916, Taubman Center, Ann Arbor, MI, 48109, United States. Electronic address: msalisbu@med.umich.edu. 2. Department of Biostatistics, University of Michigan, M4515 SPH II 1415 Washington Heights, Ann Arbor, MI, 48109, United States. Electronic address: summerx@umich.edu. 3. Department of Biostatistics, University of Michigan, M4515 SPH II 1415 Washington Heights, Ann Arbor, MI, 48109, United States. Electronic address: skmurray@med.umich.edu. 4. Department of Radiology, Mayo Clinic, 200 1st St SW, Rochester, MN, 55905, United States. Electronic address: Bartholmai.Brian@mayo.edu. 5. Department of Radiology, University of Michigan, 1500 E Medical Center Drive SPC 5868, Ann Arbor, MI, 48109, United States. Electronic address: ellakaz@med.umich.edu. 6. Division of Pulmonary and Critical Care Medicine, Department of Medicine, University of Michigan, 1500 E Medical Center Drive 3916, Taubman Center, Ann Arbor, MI, 48109, United States. Electronic address: cathymel@med.umich.edu. 7. Division of Pulmonary and Critical Care Medicine, Department of Medicine, Cornell Medical College, 525 East 68th Street, Box 130, New York, NY, 10065, United States. Electronic address: fjm2003@med.umich.edu. 8. Division of Pulmonary and Critical Care Medicine, Department of Medicine, University of Michigan, 1500 E Medical Center Drive 3916, Taubman Center, Ann Arbor, MI, 48109, United States. Electronic address: flaherty@med.umich.edu.
Abstract
BACKGROUND: Idiopathic pulmonary fibrosis (IPF) can be diagnosed confidently and non-invasively when clinical and computed tomography (CT) criteria are met. Many do not meet these criteria due to absence of CT honeycombing. We investigated predictors of IPF and combinations allowing accurate diagnosis in individuals without honeycombing. METHODS: We utilized prospectively collected clinical and CT data from patients enrolled in the Lung Tissue Research Consortium. Included patients had no honeycombing, no connective tissue disease, underwent diagnostic lung biopsy, and had CT pattern consistent with fibrosing ILD (n = 200). Logistic regression identified clinical and CT variables predictive of IPF. The probability of IPF was assessed at various cut-points of important clinical and CT variables. RESULTS: A multivariable model adjusted for age and gender found increasingly extensive reticular densities (OR 2.93, CI 95% 1.55-5.56, p = 0.001) predicted IPF, while increasing ground glass densities predicted a diagnosis other than IPF (OR 0.55, CI 95% 0.34-0.89, p = 0.02). The model-based probability of IPF was 80% or greater in patients with age at least 60 years and extent of reticular density one-third or more of total lung volume; for patients meeting or exceeding these clinical thresholds the specificity for IPF is 96% (CI 95% 91-100%) with 21 of 134 (16%) biopsies avoided. CONCLUSIONS: In patients with suspected fibrotic ILD and absence of CT honeycombing, extent of reticular and ground glass densities predict a diagnosis of IPF. The probability of IPF exceeds 80% in subjects over age 60 years with one-third of total lung having reticular densities.
BACKGROUND:Idiopathic pulmonary fibrosis (IPF) can be diagnosed confidently and non-invasively when clinical and computed tomography (CT) criteria are met. Many do not meet these criteria due to absence of CT honeycombing. We investigated predictors of IPF and combinations allowing accurate diagnosis in individuals without honeycombing. METHODS: We utilized prospectively collected clinical and CT data from patients enrolled in the Lung Tissue Research Consortium. Included patients had no honeycombing, no connective tissue disease, underwent diagnostic lung biopsy, and had CT pattern consistent with fibrosing ILD (n = 200). Logistic regression identified clinical and CT variables predictive of IPF. The probability of IPF was assessed at various cut-points of important clinical and CT variables. RESULTS: A multivariable model adjusted for age and gender found increasingly extensive reticular densities (OR 2.93, CI 95% 1.55-5.56, p = 0.001) predicted IPF, while increasing ground glass densities predicted a diagnosis other than IPF (OR 0.55, CI 95% 0.34-0.89, p = 0.02). The model-based probability of IPF was 80% or greater in patients with age at least 60 years and extent of reticular density one-third or more of total lung volume; for patients meeting or exceeding these clinical thresholds the specificity for IPF is 96% (CI 95% 91-100%) with 21 of 134 (16%) biopsies avoided. CONCLUSIONS: In patients with suspected fibrotic ILD and absence of CT honeycombing, extent of reticular and ground glass densities predict a diagnosis of IPF. The probability of IPF exceeds 80% in subjects over age 60 years with one-third of total lung having reticular densities.
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