TY - JOUR
T1 - Predictors of idiopathic pulmonary fibrosis in absence of radiologic honeycombing
T2 - A cross sectional analysis in ILD patients undergoing lung tissue sampling
AU - Salisbury, Margaret L.
AU - Xia, Meng
AU - Murray, Susan
AU - Bartholmai, Brian J.
AU - Kazerooni, Ella A.
AU - Meldrum, Catherine A.
AU - Martinez, Fernando J.
AU - Flaherty, Kevin R.
N1 - Funding Information:
Dr. Salisbury takes responsibility for the content of the manuscript, including the data and analysis. This study utilized data provided by the Lung Tissue Research Consortium (LTRC) supported by the National Heart, Lung, and Blood Institute (NHLBI).
Funding Information:
Dr. Martinez reports grants from National Institutes of Health, non-financial support from Bayer, non-financial support from Centocor, non-financial support from Gilead, non-financial support from Promedior, personal fees from Ikaria, personal fees from Genentech, personal fees from Nycomed/Takeda, personal fees from Pfizer, personal fees from Vertex, personal fees from American Thoracic Society, personal fees from Inova Health System, personal fees from MedScape, personal fees from Spectrum Health System, personal fees from University of Texas Southwestern, personal fees from Stromedix/Biogen, personal fees from Axon Communications, from Johnson & Johnson, from Genzyme, personal fees from National Association for Continuing Education, personal fees from Boehringer Ingelheim, personal fees from Veracyte, during the conduct of the study; personal fees from Forest, personal fees from Janssens, personal fees from GSK, personal fees from Nycomed/Takeda, personal fees from Actelion, personal fees from Amgen, personal fees from Astra Zeneca, personal fees from CSA Medical, personal fees from Ikaria/Bellerophon, personal fees from Forest, personal fees from Genentech, personal fees from GSK, personal fees from Janssens, personal fees from Merck, personal fees from Pearl, personal fees from Nycomed/Takeda, personal fees from Pfizer, personal fees from Roche, personal fees from Sudler & Hennessey, personal fees from American College of Chest Physicians, personal fees from CME Incite, personal fees from Center for Healthcare Education, personal fees from Inova Health System, personal fees from MedScape, personal fees from Miller Medical, personal fees from National Association for Continuing Education, personal fees from Paradigm, personal fees from Peer Voice, personal fees from Projects in Knowledge, personal fees from St. John's Hospital, personal fees from St. Mary's Hospital, personal fees from University of Illinois Chicago, personal fees from UpToDate, personal fees from Wayne State University, personal fees from GSK, personal fees from Boehringer Ingelheim, personal fees from GSK, personal fees from Ikaria, personal fees from Bayer, personal fees from Nycomed/Takeda, personal fees from Grey Healthcare, personal fees from Merion, personal fees from Informa, personal fees from Annenberg, personal fees from GSK, personal fees from Forest, outside the submitted work.
Funding Information:
This study was funded by National Institutes of Health/National Heart, Lung, and Blood Institute HHSN26820118C (Lung Tissue Research Consortium) and T32 HL00749-21 (Multidisciplinary Training Program in Lung Disease), and National Institutes of Health K24 HL111316 (Kevin R. Flaherty).
Funding Information:
Dr. Flaherty reports grants from NIH, during the conduct of the study; personal fees from Boehringer Ingelheim, personal fees from Fibrogen, personal fees from Genentech, personal fees from Gilead, personal fees from Ikaria, personal fees from ImmuneWorks, personal fees from MedImmune, personal fees from Novartis, personal fees from Takeda, personal fees from Vertex, personal fees from Veracyte, personal fees from Roche, personal fees from Pulmonary Fibrosis Foundation, grants from ImmuneWorks, grants and personal fees from Intermune, grants from Bristol-Myers Squibb, outside the submitted work.
Publisher Copyright:
© 2016 Elsevier Ltd
PY - 2016/9/1
Y1 - 2016/9/1
N2 - 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.
AB - 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.
KW - Diagnosis
KW - High resolution computed tomography
KW - Idiopathic pulmonary fibrosis
KW - Interstitial lung disease
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U2 - 10.1016/j.rmed.2016.07.016
DO - 10.1016/j.rmed.2016.07.016
M3 - Article
C2 - 27578476
AN - SCOPUS:84984807297
VL - 118
SP - 88
EP - 95
JO - British Journal of Tuberculosis and Diseases of the Chest
JF - British Journal of Tuberculosis and Diseases of the Chest
SN - 0954-6111
ER -