A strategy of early extubation to non-invasive respiratory support in preterm infants could be boosted by the availability of a decision-support tool for clinicians. Using the Heart Rate Characteristics index (HRCi) with clinical parameters, we derived and validated predictive models for extubation readiness and success.Peri-extubation demographic, clinical and HRCi data for up to 96 h were collected from mechanically ventilated infants in the control arm of a randomised trial involving 8 neonatal centres, where clinicians were blinded to the HRCi scores. The data was used to produce a multivariable regression model for the probability of subsequent re-intubation. Additionally, a survival model was produced to estimate the probability of reintubation in the period after extubation.Of the 577 eligible infants, data from 397 infants (2/3rd) were used to derive the pre-extubation model and 180 infants for validation. The model was also fitted and validated using all combinations of training (5-centres) and test (3-centres) centres. The estimated probability for the validation episodes showed discrimination with high statistical significance, with the area under the curve of 0.72 (0.71, 0.74; p<0.001). Data from all infants were used to derive models of the predictive instantaneous hazard of re-intubation adjusted for clinical parameters.Predictive models of extubation readiness and success in real-time can be derived using physiological and clinical variables. The models from our analyses can be accessed using an online tool at www.heroscore.com/extubation/ and have the potential to inform and supplement the confidence of the clinician considering extubation in preterm infants.