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 (n=186), 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.