Achaiah Andrew, Fraser Emily, Saunders Peter, Hoyles Rachel, Benamore Rachel, Ho Ling-Pei
MRC Translational Immune Discovery Unit, Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK.
Gloucestershire Hospitals NHS Foundation Trust, Cheltenham, United Kingdom.
BMC Pulm Med. 2025 Jul 28;25(1):358. doi: 10.1186/s12890-025-03825-4.
Idiopathic pulmonary fibrosis (IPF) is a progressive fibrotic condition. Serial FVC monitoring is most commonly used to assess progression of disease but FVC does not always reflect regional CT change in IPF. Recently there has been growing interest in quantitative CT (qCT) assessment of IPF. In this study, we compared different physiological and qCT measurements of disease progression in predicting mortality in IPF.
We question if a composite measure of disease progression using qCT and FVC is more predictive of mortality than individual measurements, and if addition of blood leukocyte levels further enhance predictive ability of these measurements of disease progression.
We conducted a retrospective analysis of an IPF cohort (n = 71). Annualised change (∆) in CT-measured lung volume (CTvol) and total lung fibrosis score (TLF) were calculated (using the computer software CALIPER) together with annualised change in FVC and blood leukocyte levels within 4 months of first CT. These were modelled against mortality using multivariate Cox regression. Concordance indexes (C-statistic) of different Cox regression models were used to determine the most predictive and discriminative combination for mortality.
65 cases (91.5%) were male. Median (IQR) age 73.6 years (68.4-79.3). Death was reported in 24 cases (33.8%). The median annualised change in (∆)FVC was - 4.4% (-9.6-0.0), ∆TLF; + 2.9% (0.2-7.0), and ∆CTvol; -4.3% (0.0-10.9). Combined measurements of disease progression (∆CTvol, ∆FVC and ∆TLF%) out-performed single-variable measurements in predicting all-cause mortality in IPF. The composite variable of [ΔFVC >10%, ΔCTvol >10% or ΔTLF% >10%] was most predictive of mortality [HR 7.14 (2.45-20.79), p <0.001]. Inclusion of blood leukocytes improved C-statistic scores for each multivariate model.
Composite end points of ∆CTvol, ∆FVC and ∆TLF% were more predictive of mortality than single-variable measurements in this cohort. Inclusion of blood leukocytes into risk stratification models further improved mortality prediction for all measures of disease progression.
特发性肺纤维化（IPF）是一种进行性纤维化疾病。连续监测用力肺活量（FVC）是评估疾病进展最常用的方法，但FVC并不总能反映IPF患者的局部CT变化。近年来，定量CT（qCT）评估IPF的方法越来越受到关注。在本研究中，我们比较了不同的生理学和qCT测量方法在预测IPF患者死亡率方面的疾病进展情况。
我们探讨使用qCT和FVC的疾病进展综合测量方法是否比单独测量更能预测死亡率，以及加入血白细胞水平是否能进一步提高这些疾病进展测量方法的预测能力。
我们对一个IPF队列（n = 71）进行了回顾性分析。计算首次CT检查后4个月内CT测量的肺容积（CTvol）和全肺纤维化评分（TLF）的年化变化（∆），以及FVC和血白细胞水平的年化变化。使用多变量Cox回归对这些指标与死亡率进行建模。使用不同Cox回归模型的一致性指数（C统计量）来确定预测死亡率最具预测性和区分性的组合。
65例（91.5%）为男性。年龄中位数（四分位间距）为73.6岁（68.4 - 79.3岁）。24例（33.8%）报告死亡。FVC的年化变化（∆）中位数为 -4.4%（-9.6 - 0.0），∆TLF为 +2.9%（0.2 - 7.0），∆CTvol为 -4.3%（0.0 - 10.9）。在预测IPF患者的全因死亡率方面，疾病进展的综合测量（∆CTvol、∆FVC和∆TLF%）优于单变量测量。[∆FVC >10%、∆CTvol >10%或∆TLF% >10%]的综合变量对死亡率的预测性最强[风险比（HR）7.14（2.45 - 20.79），p <0.001]。纳入血白细胞可提高每个多变量模型的C统计量得分。
在该队列中，∆CTvol、∆FVC和∆TLF%的综合终点比单变量测量更能预测死亡率。将血白细胞纳入风险分层模型可进一步改善所有疾病进展测量方法的死亡率预测。
