Individual risk assessment tool for school‐age asthma prediction in UK birth cohort

Abstract

Summary Background Current published asthma predictive tools have moderate positive likelihood ratios (+LR) but high negative likelihood ratios (−LR) based on their recommended cut‐offs, which limit their clinical usefulness. Objective To develop a simple clinically applicable asthma prediction tool within a population‐based birth cohort. Method Children from the Manchester Asthma and Allergy Study (MAAS) attended follow‐up at ages 3, 8 and 11 years. Data on preschool wheeze were extracted from primary‐care records. Parents completed validated respiratory questionnaires. Children were skin prick tested (SPT). Asthma at 8/11 years (school‐age) was defined as parentally reported (a) physician‐diagnosed asthma and wheeze in the previous 12 months or (b) ≥3 wheeze attacks in the previous 12 months. An asthma prediction tool (MAAS APT) was developed using logistic regression of characteristics at age 3 years to predict school‐age asthma. Results Of 336 children with physician‐confirmed wheeze by age 3 years, 117(35%) had school‐age asthma. Logistic regression selected 5 significant risk factors which formed the basis of the MAAS APT: wheeze after exercise; wheeze causing breathlessness; cough on exertion; current eczema and SPT sensitisation(maximum score 5). A total of 281(84%) children had complete data at age 3 years and were used to test the MAAS APT. Children scoring ≥3 were at high risk of having asthma at school‐age (PPV > 75%; +LR 6.3, −LR 0.6), whereas children who had a score of 0 had very low risk(PPV 9.3%; LR 0.2). Conclusion MAAS APT is a simple asthma prediction tool which could easily be applied in clinical and research settings.

Prediction models for childhood asthma: A systematic review

“…Of the 21 models carried forward for qualitative analysis (Table ), six were developed in the general population 24,31,36‐38 and 15 within high‐risk populations, the latter restricting inclusion to children with a parental history of allergy/asthma (four models) 25,28‐29,39 or asthma‐like symptoms (11 models, 30,40 with nine specifically targeting children experiencing wheeze 26‐27,41‐47 ). Only one model was derived based on predictors initially associated with childhood asthma within a low‐income, Puerto Rican population 37 …”

“…Upon critical appraisal, at least eight studies were identified as lacking sufficient power to develop stable prediction models; these studies had a ratio of candidate predictors to total number of cases lower than recommended (at least 20 cases per candidate predictor) to achieve sufficient power 30,38,41‐42,44‐45,47 . Underpowered studies risk important predictors not being selected (under‐fitting—Type II error), the incorrect selection of predictors (overfitting—Type I error) as well as the misrepresentation of the associated directionality between predictors and the outcome 59…”

Severe wheeze, aspergillus and a new approach for anaphylaxis

“…1 They used a data-driven methodology to derive exacerbation trajectories in the Manchester Asthma and Allergy Study cohort. 2 Amongst the 160 participants with at least one severe exacerbation, they found two clusters: "Infrequent exacerbations" (93.7%) and "Early-onset frequent exacerbations" (6.3%) ( Figure 1). Limited breastfeeding was associated with the more severe phenotype.…”

“…In this issue of the Journal, Deliu et al look at the longitudinal trajectories of medical record confirmed severe exacerbations of wheeze from infancy into childhood . They used a data‐driven methodology to derive exacerbation trajectories in the Manchester Asthma and Allergy Study cohort . Amongst the 160 participants with at least one severe exacerbation, they found two clusters: “Infrequent exacerbations” (93.7%) and “Early‐onset frequent exacerbations” (6.3%) (Figure ).…”

Circulating Chemokine Levels and the Development of Allergic Phenotypes from Infancy to Adolescence: A Population-Based Birth Cohort Study

“…These variables were chosen on the basis of differences seen in the demographic data and previous knowledge from the cohort. 45,46 As not to over-t the models, we chose to merge the maternal and paternal variables on atopy and asthma into one parental variable each. We did not correct for pet ownership separately or as a merged variable, as only cat-ownership was signi cantly different, and as not to over-t the models.…”