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.