Forced vital capacity trajectories in patients with idiopathic pulmonary fibrosis: a secondary analysis of a multicentre, prospective, observational cohort
Metadatos
Mostrar el registro completo del ítemEditorial
Elsevier
Fecha
2022-11-01Referencia bibliográfica
Hernan P Fainberg... [et al.]. Forced vital capacity trajectories in patients with idiopathic pulmonary fibrosis: a secondary analysis of a multicentre, prospective, observational cohort, The Lancet Digital Health, Volume 4, Issue 12, 2022, Pages e862-e872, ISSN 2589-7500, [https://doi.org/10.1016/S2589-7500(22)00173-X]
Patrocinador
National Institute for Health and Care Research, Medical Research Council; GlaxoSmithKlineResumen
Background Idiopathic pulmonary fibrosis is a progressive fibrotic lung disease with a variable clinical trajectory.
Decline in forced vital capacity (FVC) is the main indicator of progression; however, missingness prevents long-term
analysis of patterns in lung function. We aimed to identify distinct clusters of lung function trajectory among patients
with idiopathic pulmonary fibrosis using machine learning techniques.
Methods We did a secondary analysis of longitudinal data on FVC collected from a cohort of patients with idiopathic
pulmonary fibrosis from the PROFILE study; a multicentre, prospective, observational cohort study. We evaluated the
imputation performance of conventional and machine learning techniques to impute missing data and then analysed
the fully imputed dataset by unsupervised clustering using self-organising maps. We compared anthropometric
features, genomic associations, serum biomarkers, and clinical outcomes between clusters. We also performed a
replication of the analysis on data from a cohort of patients with idiopathic pulmonary fibrosis from an independent
dataset, obtained from the Chicago Consortium.
Findings 415 (71%) of 581 participants recruited into the PROFILE study were eligible for further analysis. An
unsupervised machine learning algorithm had the lowest imputation error among tested methods, and self-organising
maps identified four distinct clusters (1–4), which was confirmed by sensitivity analysis. Cluster 1 comprised 140 (34%)
participants and was associated with a disease trajectory showing a linear decline in FVC over 3 years. Cluster 2
comprised 100 (24%) participants and was associated with a trajectory showing an initial improvement in FVC before
subsequently decreasing. Cluster 3 comprised 113 (27%) participants and was associated with a trajectory showing an
initial decline in FVC before subsequent stabilisation. Cluster 4 comprised 62 (15%) participants and was associated
with a trajectory showing stable lung function. Median survival was shortest in cluster 1 (2·87 years [IQR 2·29–3·40])
and cluster 3 (2·23 years [1·75–3·84]), followed by cluster 2 (4·74 years [3·96–5·73]), and was longest in cluster 4
(5·56 years [5·18–6·62]). Baseline FEV1 to FVC ratio and concentrations of the biomarker SP-D were significantly
higher in clusters 1 and 3. Similar lung function clusters with some shared anthropometric features were identified
in the replication cohort.
Interpretation Using a data-driven unsupervised approach, we identified four clusters of lung function trajectory with
distinct clinical and biochemical features. Enriching or stratifying longitudinal spirometric data into clusters might
optimise evaluation of intervention efficacy during clinical trials and patient management.