%0 Journal Article %A Nicole Filipow %A Gwyneth Davies %A Eleanor Main %A Neil J. Sebire %A Colin Wallis %A Felix Ratjen %A Sanja Stanojevic %T Unsupervised Phenotypic Clustering for Determining Clinical Status in Children with Cystic Fibrosis %D 2021 %R 10.1183/13993003.02881-2020 %J European Respiratory Journal %P 2002881 %X Background Cystic Fibrosis (CF) is a multisystem disease in which assessing disease severity based on lung function alone may not be appropriate. The aim of the study was to develop a comprehensive machine-learning algorithm to assess clinical status independent of lung function in children.Methods A comprehensive prospectively collected clinical database (Toronto, Canada) was used to apply unsupervised cluster analysis. The defined clusters were then compared by current and future lung function, risk of future hospitalisation, and risk of future pulmonary exacerbation (PEx) treated with oral antibiotics. A K-Nearest Neighbours (KNN) algorithm was used to prospectively assign clusters. The methods were validated in a paediatric clinical CF dataset from Great Ormond Street Hospital (GOSH).Results The optimal cluster model identified four (A-D) phenotypic clusters based on 12 200 encounters from 530 individuals. Two clusters (A,B) consistent with mild disease were identified with high FEV1, and low risk of both hospitalisation and PEx treated with oral antibiotics. Two clusters (C,D) consistent with severe disease were also identified with low FEV1. Cluster D had the shortest time to both hospitalisation and PEx treated with oral antibiotics. The outcomes were consistent in 3124 encounters from 171 children at GOSH. The KNN cluster allocation error rate was low, at 2.5% (Toronto), and 3.5% (GOSH).Conclusion Machine learning derived phenotypic clusters can predict disease severity independent of lung function and could be used in conjunction with functional measures to predict future disease trajectories in CF patients.FootnotesThis 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: Ms. Filipow has nothing to disclose.Conflict of interest: Dr. Davies reports personal fees from Chiesi Limited, outside the submitted work.Conflict of interest: Professor Main has nothing to disclose.Conflict of interest: Dr. sebire has nothing to disclose.Conflict of interest: Dr. Wallis has nothing to disclose.Conflict of interest: Dr. Ratjen reports grants and personal fees from Vertex, Calithera, Proteostasis, TranslateBio, Genentech, Bayer and Boehringer Ingelheim, outside the submitted work.Conflict of interest: Dr. Stanojevic reports grants from SickKids Foundation, grants from European Respiratory Society, during the conduct of the study. %U https://erj.ersjournals.com/content/erj/early/2021/01/08/13993003.02881-2020.full.pdf