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.
Abstract
Using the Heart Rate Characteristics index, we have derived models predicting extubation outcomes for preterm infants, both for extubation readiness and success. These models are intended as a decision support tool for clinicians. https://bit.ly/2LKNEKk
Footnotes
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Author contributions: M. Chakraborty conceptualised the study, analysed and interpreted data, wrote and revised drafts of the manuscript, and approved the final draft. W.J. Watkins analysed and interpreted data, wrote code for online applications, revised drafts of the manuscript, and approved the final draft. K. Tansey analysed data, wrote code for online applications and approved the final draft. W.E. King collected original data, revised drafts of the manuscript and approved the final draft. S. Banerjee conceptualised the study, interpreted data, revised drafts of the manuscript and approved the final draft; this author had full access to all the data in the study and had final responsibility for the decision to submit for publication.
Conflict of interest: W.J. Watkins has nothing to disclose.
Conflict of interest: K. Tansey has nothing to disclose.
Conflict of interest: W.E. King reports personal fees from Medical Predictive Science Corporation (MPSC) outside the submitted work and receives salary and stock as CEO of MPSC, manufacturer of HeRO.
Conflict of interest: S. Banerjee has nothing to disclose.
Conflict of interest: M. Chakraborty has nothing to disclose.
Support statement: M. Chakraborty was supported by a personal research grant (Clinical Research Time Award CTRA-16–04) from Health and Care Research Wales (HCRW). HCRW had no role in the study design, analysis or interpretation of data. Funding information for this article has been deposited with the Crossref Funder Registry.
- Received April 28, 2019.
- Accepted May 15, 2020.
- Copyright ©ERS 2020