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Extended report
Relationship between quantitative radiographic assessments of interstitial lung disease and physiological and clinical features of systemic sclerosis
  1. Donald P Tashkin1,
  2. Elizabeth R Volkmann1,
  3. Chi-Hong Tseng2,
  4. Hyun J Kim3,
  5. Jonathan Goldin3,
  6. Philip Clements1,
  7. Daniel Furst1,
  8. Dinesh Khanna4,
  9. Eric Kleerup1,
  10. Michael D Roth1,
  11. Robert Elashoff2
  1. 1Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, California, USA
  2. 2Department of Biostatistics, David Geffen School of Medicine at UCLA, Los Angeles, California, USA
  3. 3Department of Radiology, David Geffen School of Medicine at UCLA, Los Angeles, California, USA
  4. 4Department of Medicine, University of Michigan School of Medicine, Ann Arbor, Michigan, USA
  1. Correspondence to Dr Donald P Tashkin, Department of Medicine, David Geffen School of Medicine at UCLA, 10833 Le Conte Ave., Los Angeles, CA 90095, USA; dtashkin{at}mednet.ucla.edu

Abstract

Objectives Extent of systemic sclerosis (SSc)-related interstitial lung disease (ILD) assessed from thoracic high-resolution CT (HRCT) predicts disease course, mortality and treatment response. While quantitative HRCT analyses of extent of lung fibrosis (QLFib) or total interstitial lung disease (QILD) are more sensitive and reproducible than visual HRCT assessments of SSc-ILD, these analyses are not widely available. This study evaluates the relationship between clinical disease parameters and QLFib and QILD scores to identify potential surrogate measures of radiographic extent of ILD.

Methods Using baseline data from the Scleroderma Lung Study I (SLS I; N=158), multivariate regression analyses were performed using the best subset selection method to identify one to five variable models that best correlated with QLFib and QILD scores in both whole lung (WL) and the zone of maximal involvement (ZM). These models were subsequently validated using baseline data from SLS II (N=142). Bivariate analyses of the radiographic and clinical variables were also performed using pooled data. SLS I and II did not include patients with clinically significant pulmonary hypertension (PH).

Results Diffusing capacity for carbon monoxide (DLCO) was the single best predictor of both QLF and QILD in the WL and ZM in all of the best subset models. Adding other disease parameters to the models did not substantially improve model performance. Forced vital capacity (FVC) did not predict QLF or QILD scores in any of the models.

Conclusions In the absence of PH, DLCO provides the best overall estimate of HRCT-measured lung disease in patients from two large SSc cohorts. FVC, although commonly used, may not be the best surrogate measure of extent of SSc-ILD at any point in time.

Trial registration numbers SLS I: www.clinicaltrials.gov NCT 00000-4563; SLS II: www.clinicaltrials.gov NCT 00883129.

  • Pulmonary Fibrosis
  • Systemic Sclerosis
  • Treatment

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