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@article{Fainberg:2022:10.1016/S2589-7500(22)00173-X,
author = {Fainberg, HP and Oldham, JM and Molyneau, PL and Allen, RJ and Kraven, LM and Fahy, WA and Porte, J and Braybrooke, R and Saini, G and Karsdal, MA and Leeming, DJ and Sand, JMB and Triguero, I and Oballa, E and Wells, AU and Renzoni, E and Wain, LV and Noth, I and Maher, TM and Stewart, ID and Jenkins, RG},
doi = {10.1016/S2589-7500(22)00173-X},
journal = {The Lancet Digital Health},
pages = {e862--e872},
title = {Forced vital capacity trajectories in patients with idiopathic pulmonary fibrosis: a secondary analysis of a multicentre, prospective, observational cohort},
url = {http://dx.doi.org/10.1016/S2589-7500(22)00173-X},
volume = {4},
year = {2022}
}

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TY  - JOUR
AB  - 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
AU  - Fainberg,HP
AU  - Oldham,JM
AU  - Molyneau,PL
AU  - Allen,RJ
AU  - Kraven,LM
AU  - Fahy,WA
AU  - Porte,J
AU  - Braybrooke,R
AU  - Saini,G
AU  - Karsdal,MA
AU  - Leeming,DJ
AU  - Sand,JMB
AU  - Triguero,I
AU  - Oballa,E
AU  - Wells,AU
AU  - Renzoni,E
AU  - Wain,LV
AU  - Noth,I
AU  - Maher,TM
AU  - Stewart,ID
AU  - Jenkins,RG
DO  - 10.1016/S2589-7500(22)00173-X
EP  - 872
PY  - 2022///
SN  - 2589-7500
SP  - 862
TI  - Forced vital capacity trajectories in patients with idiopathic pulmonary fibrosis: a secondary analysis of a multicentre, prospective, observational cohort
T2  - The Lancet Digital Health
UR  - http://dx.doi.org/10.1016/S2589-7500(22)00173-X
UR  - https://www.ncbi.nlm.nih.gov/pubmed/36333179
UR  - http://hdl.handle.net/10044/1/101285
VL  - 4
ER  -

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