Predictors of idiopathic pulmonary fibrosis in absence of radiologic honeycombing: A cross sectional analysis in ILD patients undergoing lung tissue sampling

Margaret L. Salisbury, Meng Xia, Susan Murray, Brian Jack Bartholmai, Ella A. Kazerooni, Catherine A. Meldrum, Fernando J. Martinez, Kevin R. Flaherty

Research output: Contribution to journalArticle

25 Citations (Scopus)

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.

Original languageEnglish (US)
Pages (from-to)88-95
Number of pages8
JournalRespiratory Medicine
Volume118
DOIs
StatePublished - Sep 1 2016

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Idiopathic Pulmonary Fibrosis
Cross-Sectional Studies
Lung
Tomography
Glass
Biopsy
Connective Tissue Diseases
Logistic Models

Keywords

  • Diagnosis
  • High resolution computed tomography
  • Idiopathic pulmonary fibrosis
  • Interstitial lung disease

ASJC Scopus subject areas

  • Pulmonary and Respiratory Medicine

Cite this

Predictors of idiopathic pulmonary fibrosis in absence of radiologic honeycombing : A cross sectional analysis in ILD patients undergoing lung tissue sampling. / Salisbury, Margaret L.; Xia, Meng; Murray, Susan; Bartholmai, Brian Jack; Kazerooni, Ella A.; Meldrum, Catherine A.; Martinez, Fernando J.; Flaherty, Kevin R.

In: Respiratory Medicine, Vol. 118, 01.09.2016, p. 88-95.

Research output: Contribution to journalArticle

Salisbury, Margaret L. ; Xia, Meng ; Murray, Susan ; Bartholmai, Brian Jack ; Kazerooni, Ella A. ; Meldrum, Catherine A. ; Martinez, Fernando J. ; Flaherty, Kevin R. / Predictors of idiopathic pulmonary fibrosis in absence of radiologic honeycombing : A cross sectional analysis in ILD patients undergoing lung tissue sampling. In: Respiratory Medicine. 2016 ; Vol. 118. pp. 88-95.
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title = "Predictors of idiopathic pulmonary fibrosis in absence of radiologic honeycombing: A cross sectional analysis in ILD patients undergoing lung tissue sampling",
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.",
keywords = "Diagnosis, High resolution computed tomography, Idiopathic pulmonary fibrosis, Interstitial lung disease",
author = "Salisbury, {Margaret L.} and Meng Xia and Susan Murray and Bartholmai, {Brian Jack} and Kazerooni, {Ella A.} and Meldrum, {Catherine A.} and Martinez, {Fernando J.} and Flaherty, {Kevin R.}",
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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 Jack

AU - Kazerooni, Ella A.

AU - Meldrum, Catherine A.

AU - Martinez, Fernando J.

AU - Flaherty, Kevin R.

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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