Change in the manifestations of asthma and asthma related

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PAEDIATRIC PNEUMOLOGY F L GARDEN ET AL,Introduction. Heterogeneity in the manifestations of asthma and related respiratory diseases in childhood is likely to. have important implications for the investigation of disease aetiology mechanisms and management as. well as for population monitoring So far attempts to explore this heterogeneity and define distinct. phenotypes of asthma in children have focused on either the time course of wheeze 1 4 and other. symptoms 5 7 during childhood or on cross sectional classification of disease manifestations at specified. ages 8 12 Recently the use of unsupervised data driven statistical approaches such as cluster analysis. 9 11 12 and latent class analysis 2 3 8 10 13 has emerged as complementary to approaches based. on the application of a priori definitions 1 14 to aid in identifying and defining objective novel or. previously unrecognised phenotypes Phenotypes previously identified have varied due to differences in the. populations and attributes included in the models However the application of data driven approaches has. yielded phenotypic classifications that are clinically meaningful and interpretable 12 15 and that are. relevant to prognosis 6, Despite these advances the application of data driven approaches to define longitudinal phenotypes to the. multiple manifestations of asthma and related disorders during childhood remains largely unexplored. This is important because these manifestations are not independent Indeed the various manifestations of. asthma such as cough wheeze atopy impaired lung function airway hyperresponsiveness AHR and. airway inflammation are known to be associated with each other and to vary with age 16 Two studies. have examined transitions between phenotypes in preschool children that were defined based on wheeze. alone 17 or with multiple clinical features including wheeze triggers 18 Data from the Isle of Wight. study have been used to describe transitions during childhood in sex specific phenotypes that were defined. based on two measures physician diagnosed asthma and wheeze 19 In adults latent transition analysis. LTA has been used to define asthma phenotypes longitudinally 20 However to the best of our. knowledge there has been no previous attempt to incorporate the longitudinal pattern of several disease. manifestations into one statistical model to simultaneously define phenotypes and to examine transitions. in asthma and related disorders in children, We propose that during childhood asthma consists of several underlying traits with time varying. manifestations Our primary objective was to examine changes in the manifestation of asthma and. asthma related traits in childhood by quantitatively describing and classifying these underlying traits as. defined by phenotypes and examining their transitions over time A secondary objective was to explore the. relationship of various predictive factors to these phenotypes This was done using observations of several. manifestations of asthma and related diseases that were made at various ages during early and. mid childhood in a birth cohort of children at risk of asthma. Additional details are provided in the online supplementary material. Design and population, We used data from the first 11 5 years of life in the Childhood Asthma Prevention Study CAPS cohort. CAPS began as a randomised controlled trial investigating the effectiveness of a house dust mite HDM. avoidance and an 3 fatty acid supplementation intervention from birth to 5 years for the primary. prevention of asthma 21 Pregnant women whose unborn children were at increased risk of developing. asthma because one or more parents or siblings had current asthma or wheezing were recruited from. antenatal clinics in western and south western Sydney Australia from 1997 to 2000 Babies from multiple. births whose gestational age was 36 weeks or birthweight 2 5 kg who were hospitalised for 1 week or. had serious illness those with a pet cat at home and those whose families were strict vegetarians were. excluded 22 Among 7171 subjects screened for inclusion 29 2095 met the eligibility criteria of. whom 29 616 consented to enrolment and randomisation 23 Further details of the study design. interventions population and results of the trial at ages 5 8 and 11 5 years have been described previously. 21 22 24 25 Here we present a longitudinal analysis of the data We restricted this analysis to subjects. who completed the 11 5 year assessment 370 616, Details of the assessments conducted on this cohort have been described previously 21 22 24 25 and.
relevant details are summarised here To define the phenotypes we used data collected during clinical. assessments conducted at ages 1 5 3 5 8 and 11 5 years including information on respiratory symptoms. wheeze cough and sneezing or running nose healthcare utilisation visited an emergency department. or hospital admission for wheeze asthma bronchitis or bronchiolitis treatment preventer controller or. bronchodilator medication lung function percent predicted forced expiratory volume in 1 s FEV1. non specific AHR exhaled nitric oxide concentration eNO and atopy inhalant and ingested allergens. 500 DOI 10 1183 13993003 00284 2015,PAEDIATRIC PNEUMOLOGY F L GARDEN ET AL. Data on the following risk factors were obtained and used as predictors of phenotypes participant s sex. CAPS randomised intervention groups active control HDM intervention group and diet intervention. group 22 maternal smoking in pregnancy exposure to household tobacco smoke before 1 5 years. breastfeeding 6 months eczema at 1 5 years 26 28 daycare attendance before 1 5 years older siblings. and parental education attainment,Statistical analysis. To define the phenotypes and examine changes over time we used LTA estimated by the full information. maximum likelihood method to handle the presence of missing data implemented in Mplus version 7 20. 29 30 The model input variables were the measures described above Percent predicted FEV1 AHR and. eNO were only available at ages 8 and 11 5 years, LTA simultaneously defines classes which were interpreted as phenotypes at each assessment time 1 5 3. 5 8 and 11 5 years and models transitions between the classes We used the model to simultaneously. estimate class prevalence and item response probabilities i e proportion of children in each class in whom. the binary variable is positive at each age and probability of transition between classes at subsequent. ages We used a likelihood ratio test to determine whether the best fitting model was obtained by allowing. class structures to vary at all times unconstrained model or between early 1 5 5 years and. mid childhood 8 11 5 years constrained model 29 31 To determine the optimal number of classes. within the selected level of constraint we ran a series of models with incrementing class size and. examined model fit statistics Bayesian information criteria BIC adjusted BIC Akaike s information. criteria and entropy and interpretability of the resulting class structure 29 We examined the item. response probabilities to select an appropriate interpretation and label for each class phenotype. We tested potential predictors of class membership phenotype using multinomial logistic regression. implemented in SAS version 9 3 SAS Institute Cary NC USA In this analysis individuals were assigned. to the class with the highest probability of membership for that individual We also examined the. frequency distribution of other symptom variables current eczema and questionnaire defined current. asthma within the classes at each time point Questionnaire defined current asthma was defined as wheeze. in the last 12 months at that age and ever been diagnosed with asthma by a doctor reported at ages. 18 months and 3 5 8 and 11 5 years,Participant characteristics. Of the 616 subjects recruited into CAPS at birth 370 60 who participated in the 11 5 year assessment. were included in this analysis Characteristics of the sample are described in table 1 Compared with those. lost to follow up those included in this analysis were more likely to have mothers who did not smoke. during pregnancy were older better educated or in full time employment and also were more likely to. have fathers who were older better educated or in full time employment 25. A model that was allowed to have a different class structure during early childhood 1 5 5 years and. mid childhood 8 11 5 years but was constrained to have the same class structure within these two. periods was chosen as the optimal LTA model table 2 Four class models had the optimal model fit. statistics for both these periods and therefore were selected The four class LTA model had a high entropy. value 0 91 indicating there was a strong clear delineation of phenotypes 32 Further details on the. statistical basis for the choice of this model structure are provided in the online supplementary material. LTA phenotype descriptions, The structure of the four classes phenotypes is shown in table 3 and the prevalence of each phenotype at.
all ages is shown in figure 1, During early childhood 1 5 5 years the phenotypes were labelled 1A nonatopic few symptoms early. childhood prevalence range 60 at 1 5 years to 52 at 5 years due to the low probabilities 40 of. each attribute in the model 1B atopic few symptoms early childhood 3 at 1 5 years to 21 at. 5 years due to the high probability of atopy to inhalant allergens 95 moderate probability of atopy. to ingested allergens 14 and only moderate probability of respiratory symptoms wheeze 12 1C. nonatopic asthma and rhinitis symptoms early childhood 35 at 1 5 years to 13 at 5 years due to. the low probability of atopy 6 and high probability of symptoms wheeze 85 and medication use. bronchodilators 91 and 1D atopic asthma and rhinitis symptoms early childhood 2 at 1 5 years. to 14 at 5 years due to the high probability of respiratory symptoms wheeze 92 treatment. bronchodilators 97 and atopy inhalant allergens 100 The main attributes that differentiated the. phenotypes in early childhood were atopy inhalant allergens 1 100 and symptoms wheeze 12 92. DOI 10 1183 13993003 00284 2015 501,PAEDIATRIC PNEUMOLOGY F L GARDEN ET AL. TABLE 1 Characteristics of the study sample n 370 and description of the variables used in the latent transition analysis. 1 5 3 5 8 11 5, Cough lasting 1 week or more in the last 18 12 months 182 370 49 2 198 370 53 5 172 369 46 6 135 343 39 4 59 369 16 0. Wheeze in the last 18 12 months 169 370 45 7 143 370 38 6 112 369 30 3 95 343 27 7 93 370 25 1. Sneezing or running nose lasting 1 week or more in the 199 370 53 8 171 370 46 2 152 369 41 2. last 18 12 months, Sneezing or running nose in the last 12 months when 89 341 26 1 140 370 37 8. he she did not have a cold or the flu,Healthcare utilisation.
Attended a hospital emergency department or admitted 52 370 14 1 20 370 5 4 14 369 3 8 7 343 2 0 8 370 2 2. to hospital for wheeze bronchitis bronchiolitis or. asthma in the last 18 12 months,Treatment use, Preventer controller medication used in the last 42 370 11 4 55 370 14 9 63 369 17 1 62 343 18 1 65 370 17 6. Bronchodilator medication used in the last 12 months 163 370 44 0 172 370 46 5 131 369 35 5 121 343 35 2 118 369 32 0. Inhalant atopy 25 361 6 9 81 366 22 1 127 353 36 0 140 308 45 5 160 290 55 2. Ingested atopy 34 361 9 4 21 366 5 7 25 353 7 1 26 308 8 4 16 290 5 5. Spirometry 80 FEV1 predicted 14 314 4 5 37 283 13 1. Airway hyperresponsiveness PD20FEV1 6 1 mol 52 275 19 0 32 270 11 9. Exhaled nitric oxide ppb highest 20 64 314 20 4 58 290 20 0. Data are presented as n N FEV1 forced expiratory volume in 1 s PD20FEV1 provocative dose causing a 20 fall in FEV1 in the previous. 18 months was used at the 1 5 and 3 year assessments and in the previous 12 months was asked at all other assessments. During mid childhood 8 11 5 years the phenotypes were labelled 2A nonatopic no respiratory disease. mid childhood 46 at 8 years to 41 at 11 5 years 2B atopic no respiratory disease mid childhood. 23 at 8 years to 33 at 11 5 years 2C nonatopic asthma symptoms no AHR or airway inflammation. mid childhood 12 at 8 years to 8 at 11 5 years and 2D atopic asthma mid childhood 19 at. both 8 years and 11 5 years The phenotypes in mid childhood were very similar to those in early. childhood The additional attributes FEV1 AHR and eNO during this period enabled the phenotypes 2C. and 2D to be further refined The main attributes that differentiated the phenotypes were atopy inhalant. allergens 3 100 symptoms wheeze 4 79 AHR 6 54 and elevated eNO 4 70. The relative prevalence of the atopic phenotypes 1B 2B 1D and 2D increased with age during the study. period while the prevalence of the nonatopic phenotypes 1A 2A 1C and 2C decreased with age. The distribution of other characteristics among the phenotypes is shown in online supplementary tables. E2 and E3 Participants in the symptomatic phenotypes 1C 1D 2C and 2D had the highest prevalence. of the other respiratory symptoms and allergic disease symptoms at all ages. TABLE 2 Model fit statistics from the constrained and unconstrained latent transition analysis models n 370. Model Parameters n Akaike s Bayesian Adjusted Bayesian Entropy p value. information criteria information criteria information criteria. Unconstrained,Two class 101 13 585 13 981 13 660 0 884. Three class 164 13 220 13 862 13 342 0 905,Four class 235 12 913 13 833 13 087 0 907. Change in the manifestations of asthma and asthma related traits in childhood a latent transition analysis Frances L Garden1 2 3 4 Judy M Simpson1 Craig M Mellis5 and Guy B Marks2 3 for the CAPS Investigators Affiliations 1Sydney School of Public Health University of Sydney Sydney Australia 2South Western Sydney Clinical School University of New South Wales Sydney Australia

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