Belgrave, DCM

Buchan, IE

Custovic, A

Granell, R

Henderson, J

Lowe, L

Simpson, A

American Journal of Respiratory and Critical Care Medicine

English

Spiral - Imperial College Digital Repository

 / Poster Discussion Session / Tuesday, May 23/9:15 AM-11:15 AM / Room 201 (South Building, Level 2) Walter E. Washington ConventionC26 ASTHMA IN INFANTS AND CHILDRENCenterLatent Profile Analysis To Identify Heterogeneous Subgroups Of Lung Function For Personalised AndTargeted Early Intervention, R. Granell , A. Simpson , L. Lowe , I. E. Buchan , J. Henderson , A. CustovicD. C.M. Belgrave1 2 3 3 3 2 1Imperial College London, London, United Kingdom, University of Bristol, Bristol, United Kingdom, University of Manchester,1 2 3Manchester, United KingdomCorresponding author's email: d.belgrave@imperial.ac.uk Current practise defines asthma as a single diagnosis characterised by impaired lung function and wheezing. WeIntroduction:hypothesised that asthma consists of distinct syndromes with heterogeneous profiles of lung function from childhood to adolescence,and that these trajectories indicate distinct asthma endotypes. Data was taken from two birth cohorts: Manchester Asthma and Allergy Study (MAAS) (n=1184 participants) and AvonMethod:Longitudinal Study of Parents and Children (ALSPAC) (n=14,541 participants). Lung function was assessed using Forced Expiratory Volume(FEV ) at ages 5, 8, 11 and 16 years in MAAS and at ages 8, 15 and 24 years in ALSPAC. Data was expressed as percent predicted FEV  using1 1standardised charts. We used a latent profile model to assign children to one of N latent trajectories (profiles) of FEV , with the number of1profiles not known a-priori. For each child, the posterior probability of class membership was calculated and children assigned to thelatent profile with the largest conditional posterior probability. We then constructed a multivariable model to identify early-life predictorsof subsequent latent profiles of FEV . We used receiver operating characteristic (ROC) curves to evaluate model predictive ability. Analysis1was repeated independently using ALSPAC data. The optimal model was a 4-class latent profile model. We labelled classes as: Class 1, Above average lung function (n=46; 4.4%),Results:mean FEV1 of 119.22% (95% CI 117.62–120.81); Class 2, Normal lung function (n=474; 45.3%), mean FEV1 104.34% (103.90–104.77); Class 3,Diminished lung function (n=490; 46.9%), mean FEV1 of 91.22% (90.77–91.67); Class 4, Poor lung function (n=36; 3.4%), mean FEV1 of76.31% (74.47–78.16). Multinomial regression analysis revealed that children who wheeze, have severe exacerbations by age 3 years, haveat least one positive skin test by age 3 years, and have exposure to smoking by age 3 years were more likely to belong to Class 4. This set ofvariables provided a good predictive ability for discriminating between children in Class 4 from children in Class 1 (Area Under the ROCCurve=90.7%, sensitivity=81.8%; specificity=80.95%). Similar profiles were identified in ALSPAC. There are distinct profiles of lung function over time and we have identified conditionally independent predictors at ageConclusion:three years which can distinguish children with poor prognosis in terms of lung function from ages 5 to 24 years. This approach tounderstand heterogeneous profiles of disease over time can elucidate complex subgroups of disease and facilitate more targeted asthmamanagement strategies.This abstract is funded by: Medical Research Council (MRC) Grants G0601361 and MR/K002449/1 DB is funded by MRC CareerDevelopment Award in Biostatistics Grant MR/M015181/1Am J Respir Crit Care Med 2017;195:A5096Internet address: www.atsjournals.org Online Abstracts Issue

Latent profile analysis to identify heterogeneous subgroups of lung function for personalised and targeted early intervention

http://spiral.imperial.ac.uk/bitstream/10044/1/52991/2/ajrccm-conference.2017.195.1_meetingabstracts.a5096.pdf

Latent profile analysis to identify heterogeneous subgroups of lung function for personalised and targeted early intervention

Abstract

Similar works

Full text

Available Versions

Spiral - Imperial College Digital Repository