BACKGROUND: Existing predictive scores for early identification of children at high risk of developing asthma include invasive procedures, and hence have limited utility in a primary care setting. The Leicestershire respiratory cohort (LRC) has developed a noninvasive asthma prediction tool (APT) for children with promising results. We aimed to perform its external validation in the French general population Etude Longitudinale Francaise depuis l'Enfance (ELFE) cohort. METHODS: Predictive scores were determined at Age 1 and the primary outcome of asthma was defined as parental reporting of "asthma ever or "wheezing in the past 12 months" at Age 5. Logistic regression was used to calculate the odds ratio (OR) and performance measures, and discriminative performance was reported using the receiver operating curve and area under curve (AUC). Calibration was assessed using Hosmer-Lemeshow goodness-of-fit test and visualized with a calibration plot. Overall performance was determined using Brier scores. RESULTS: Of the 10,689 children analyzed: 84.9% were at low, 13.1% medium, and 2% at high risk of developing asthma at Age 5. Children in the medium-risk category were three times more likely to develop asthma (OR = 3.3, 95% confidence interval [CI] = 2.97-3.78) whereas 13 times more likely in the high-risk category (OR = 13.8, 95% CI = 10.2-18.8). The tool's AUC was comparable: LRC 0.74 versus ELFE 0.68; as were the Brier scores LRC 0.16 versus ELFE 0.14. The tool's performance was robust to changes in inclusion criteria and outcome definitions. CONCLUSIONS AND RELEVANCE: Results of the present study and previous validation studies performed in high-risk populations provide a comprehensive measure of the effectiveness of the APT, providing encouragement for its application by general practitioners.