Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, California, USA.
Department of Biostatistics, David Geffen School of Medicine at UCLA, Los Angeles, California, USA.
Ann Rheum Dis. 2016 Feb;75(2):374-81. doi: 10.1136/annrheumdis-2014-206076. Epub 2014 Dec 1.
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.
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).
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.
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.
SLS I: www.clinicaltrials.gov NCT 00000-4563; SLS II: www.clinicaltrials.gov NCT 00883129.
通过胸部高分辨率 CT（HRCT）评估系统性硬化症（SSc）相关间质性肺病（ILD）的程度可预测疾病病程、死亡率和治疗反应。虽然定量 HRCT 分析肺纤维化（QLFib）或总间质性肺病（QILD）的程度比视觉 HRCT 评估 SSc-ILD 更敏感和更具可重复性，但这些分析并不广泛可用。本研究评估了临床疾病参数与 QLFib 和 QILD 评分之间的关系，以确定ILD 放射程度的潜在替代指标。
使用 Scleroderma Lung Study I（SLS I；N=158）的基线数据，使用最佳子集选择方法进行多变量回归分析，以确定一个到五个变量模型，这些模型在整个肺（WL）和最大受累区（ZM）中与 QLFib 和 QILD 评分最佳相关。随后使用 SLS II（N=142）的基线数据验证这些模型。还使用合并数据对放射学和临床变量进行了二元分析。SLS I 和 II 不包括有临床意义的肺动脉高压（PH）的患者。
一氧化碳弥散量（DLCO）是所有最佳子集模型中 WL 和 ZM 中 QLF 和 QILD 的最佳单一预测因子。向模型中添加其他疾病参数并没有显著提高模型性能。用力肺活量（FVC）在任何模型中都不能预测 QLF 或 QILD 评分。
在没有 PH 的情况下，DLCO 为来自两个大型 SSc 队列的患者提供了 HRCT 测量的肺部疾病的最佳总体估计。FVC 虽然常用，但可能不是 SSc-ILD 程度的最佳替代指标。
SLS I：www.clinicaltrials.gov NCT 00000-4563；SLS II：www.clinicaltrials.gov NCT 00883129。
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