Prediction of Extubation readiness in extremely preterm infants by the automated analysis of cardiorespiratory behavior: Study protocol

Wissam Shalish, Lara J. Kanbar, Smita Rao, Carlos A. Robles-Rubio, Lajos Kovacs, Sanjay Chawla, Martin Keszler, Doina Precup, Karen Brown, Robert E. Kearney, Guilherme M. Sant'Anna

Research output: Contribution to journalArticlepeer-review

30 Scopus citations


Background: Extremely preterm infants (≤ 28 weeks gestation) commonly require endotracheal intubation and mechanical ventilation (MV) to maintain adequate oxygenation and gas exchange. Given that MV is independently associated with important adverse outcomes, efforts should be made to limit its duration. However, current methods for determining extubation readiness are inaccurate and a significant number of infants fail extubation and require reintubation, an intervention that may be associated with increased morbidities. A variety of objective measures have been proposed to better define the optimal time for extubation, but none have proven clinically useful. In a pilot study, investigators from this group have shown promising results from sophisticated, automated analyses of cardiorespiratory signals as a predictor of extubation readiness. The aim of this study is to develop an automated predictor of extubation readiness using a combination of clinical tools along with novel and automated measures of cardiorespiratory behavior, to assist clinicians in determining when extremely preterm infants are ready for extubation. Methods: In this prospective, multicenter observational study, cardiorespiratory signals will be recorded from 250 eligible extremely preterm infants with birth weights ≤1250 g immediately prior to their first planned extubation. Automated signal analysis algorithms will compute a variety of metrics for each infant, and machine learning methods will then be used to find the optimal combination of these metrics together with clinical variables that provide the best overall prediction of extubation readiness. Using these results, investigators will develop an Automated system for Prediction of EXtubation (APEX) readiness that will integrate the software for data acquisition, signal analysis, and outcome prediction into a single application suitable for use by medical personnel in the neonatal intensive care unit. The performance of APEX will later be prospectively validated in 50 additional infants. Discussion: The results of this research will provide the quantitative evidence needed to assist clinicians in determining when to extubate a preterm infant with the highest probability of success, and could produce significant improvements in extubation outcomes in this population. Trial registration: identifier: NCT01909947. Registered on July 17 2013. Trial sponsor: Canadian Institutes of Health Research (CIHR).

Original languageEnglish
Article number167
JournalBMC Pediatrics
Issue number1
StatePublished - Jul 17 2017
Externally publishedYes


  • Biomedical signal processing
  • Cardiorespiratory behavior
  • Clinical predictors
  • Extubation readiness
  • Heart rate variability
  • Respiratory variability


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