4.6 Article

Unsupervised phenotypic clustering for determining clinical status in children with cystic fibrosis

Journal

EUROPEAN RESPIRATORY JOURNAL
Volume 58, Issue 2, Pages -

Publisher

EUROPEAN RESPIRATORY SOC JOURNALS LTD
DOI: 10.1183/13993003.02881-2020

Keywords

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Funding

  1. UCL's Wellcome Institutional Strategic Support Fund 3 [204841/Z/16/Z]
  2. Program for Individualized Cystic Fibrosis Therapy Synergy Grant
  3. European Respiratory Society
  4. UCL
  5. GOSH
  6. Toronto SickKids studentship
  7. Wellcome Trust [204841/Z/16/Z] Funding Source: Wellcome Trust

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A comprehensive machine-learning algorithm was developed to assess clinical status independent of lung function in children with cystic fibrosis. Phenotypic clusters were identified based on clinical data, showing different disease severities and risks of hospitalization and exacerbation. The algorithm showed low error rates in cluster allocation and could help predict future disease trajectories in CF patients.
Background Cystic fibrosis (CF) is a multisystem disease in which the assessment of 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 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 12200 encounters from 530 individuals. Two clusters (A and B) consistent with mild disease were identified with high forced expiratory volume in 1 s (FEV1), and low risk of both hospitalisation and pulmonary exacerbation treated with oral antibiotics. Two clusters (C and D) consistent with severe disease were also identified with low FEV1. Cluster D had the shortest time to both hospitalisation and pulmonary exacerbation 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.

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