Abstract
Background LAM is a rare multisystem disease with variable clinical manifestations and differing rates of progression that make management decisions and giving prognostic advice difficult. We used machine learning to identify clusters of associated features which could be used to stratify patients and predict outcomes in individuals.
Patients and methods Using unsupervised machine learning we generated patient clusters using data from 173 women with LAM from the UK and 186 replication subjects from the NHLBI LAM registry. Prospective outcomes were associated with cluster results.
Results Two and three-cluster models were developed. A three-cluster model separated a large group of subjects presenting with dyspnoea or pneumothorax from a second cluster with a high prevalence of angiomyolipoma symptoms (p=0.0001) and TSC (p=0.041). The third cluster were older, never presented with dyspnoea or pneumothorax (p=0.0001) and had better lung function. Similar clusters were reproduced in the NHLBI cohort. Assigning patients to clusters predicted prospective outcomes: in a two-cluster model future risk of pneumothorax was 3.3 fold (95% C.I. 1.7–5.6) greater in cluster one than two (p=0.0002). Using the three-cluster model, the need for intervention for angiomyolipoma was lower in clusters two and three than cluster one (p<0.00001). In the NHLBI cohort, the incidence of death or lung transplant was much lower in clusters two and three (p=0.0045).
Conclusions Machine learning has identified clinically relevant clusters associated with complications and outcome. Assigning individuals to clusters could improve decision making and prognostic information for patients.
Footnotes
This manuscript has recently been accepted for publication in the European Respiratory Journal. It is published here in its accepted form prior to copyediting and typesetting by our production team. After these production processes are complete and the authors have approved the resulting proofs, the article will move to the latest issue of the ERJ online. Please open or download the PDF to view this article.
Conflict of interest: Dr. Chernbumroong has nothing to disclose.
Conflict of interest: Dr. Johnson has nothing to disclose.
Conflict of interest: Dr. Gupta has nothing to disclose.
Conflict of interest: Dr. Miller reports grants from British Lung Foundation, outside the submitted work;.
Conflict of interest: Dr. McCormack has nothing to disclose.
Conflict of interest: Dr. Garibaldi has nothing to disclose.
Conflict of interest: Dr. Johnson reports grants from National Institute for Health Research, grants from The LAM Foundation, grants from LAM Action, during the conduct of the study; personal fees from Pfizer, outside the submitted work;.
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- Received August 5, 2020.
- Accepted November 17, 2020.
- Copyright ©ERS 2020