Predicting extubation outcomes using the Heart Rate Characteristics index in preterm infants: a cohort study.

Abstract

A strategy of early extubation to noninvasive 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 eight neonatal centres, where clinicians were blinded to the HRCi scores. The data were 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 re-intubation in the period after extubation.Of the 577 eligible infants, data from 397 infants (69%) were used to derive the pre-extubation model and 180 infants (31%) for validation. The model was also fitted and validated using all combinations of training (five centres) and test (three centres) centres. The estimated probability for the validation episodes showed discrimination with high statistical significance, with an area under the curve of 0.72 (95% CI 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 available at www.heroscore.com/extubation, and have the potential to inform and supplement the confidence of the clinician considering extubation in preterm infants.