There is abundant literature on how to select and statistically deal with predictors in prediction models. Less attention has been paid to the choice of the outcome. We assessed the impact of different asthma definitions on prevalence estimates and on the prediction model's performances.We searched PubMed and extracted data of definitions used to diagnose childhood asthma (between 6 and 18 yrs) in cohort studies. Next, using data from an ongoing cohort study (n5186), we constructed and compared four prediction models which all predict asthma at age 6 yrs, using a fixed set of predictors and four different definitions in turn. We defined an area of clinical indecision (posterior probability between 25% and 60%) and calculated the number of children who remained inside this area.122 papers yielded 60 different definitions. Prevalence estimates varied between 15.1% and 51.1% depending on the asthma definition used. The percentage of children whose posterior asthma probability was in the area of clinical indecision varied from 14.9% to 65.3%.Variation in definitions and its effect on the performance of prediction models may be another source of otherwise inexplicable variation in daily clinical decision making. More uniformity of operational asthma definitions seems needed.

Stable multiple-trigger and stable episodic viral wheeze are relatively uncommon. However, 1- to 3-year-olds with stable MTW are at much increased risk of asthma.

Background: A setting-specific asthma prediction score for preschool children with wheezing and/or dyspnoea presenting in primary healthcare is needed since existing indices are mainly based on general populations. Aims: To find an optimally informative yet practical set of predictors for the prediction of asthma in preschool children at high risk who present in primary healthcare. Methods: A total of 771 Dutch preschool children at high risk of asthma were followed prospectively until the age of six years. Data on asthma symptoms and environmental conditions were obtained using validated questionnaires and specific IgE was measured. At the age of six years the presence of asthma was assessed based on asthma symptoms, medication, and bronchial hyper-responsiveness. A clinical asthma prediction score (CAPS) was developed using bootstrapped multivariable regression methods. Results: In all, 438 children (56.8%) completed the study; the asthma prevalence at six years was 42.7%. Five parameters optimally predicted asthma: age, family history of asthma or allergy, wheezing-induced sleep disturbances, wheezing in the absence of common colds, and specific IgE. CAPS scores range from 0 to 11 points; scores <3 signified a negative predictive value of 78.4% while scores of ≥7 signified a positive predictive value of 74.3%. Conclusions: We have developed an easy-to-use CAPS for preschool children with symptoms suggesting asthma who present in primary healthcare. After suitable validation, the CAPS may assist in guiding shared decision-making to tailor the need for medical or non-medical interventions. External validation of the CAPS is needed.

Background: Asthma is a difficult diagnosis to establish in preschool children. A few years ago, our group presented a prediction rule for young children at risk for asthma in general practice. Before this prediction rule can safely be used in practice, cross-validation is required. In addition, general practitioners face many therapeutic management decisions in children at risk for asthma. The objectives of the study are: (1) identification of predictors for asthma in preschool children at risk for asthma with the aim of cross-validating an earlier derived prediction rule; (2) compare the effects of different treatment strategies in preschool children.

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