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Updated: Feb 26, 2026
A Structured Approach to Extubation in Mechanically Ventilated Rats
Published on: July 18, 2025
Prediction of Extubation readiness in extremely preterm infants by the automated analysis of cardiorespiratory
Wissam Shalish1, Lara J Kanbar2, Smita Rao1
1Department of Pediatrics, Division of Neonatology, Montreal Children's Hospital, McGill University, 1001 Boul. Décarie, room B05.2714. Montreal, Quebec, H4A 3J1, Canada.
Insights
This study develops an automated system (APEX) to predict extubation readiness in extremely preterm infants. APEX uses cardiorespiratory signals and clinical data to improve successful extubation rates and reduce reintubation risks.
Area of Science:
- Neonatal Medicine
- Medical Technology
- Data Science in Healthcare
Background:
- Extremely preterm infants often require mechanical ventilation (MV), which is linked to adverse outcomes.
- Current methods for determining extubation readiness are inaccurate, leading to high rates of extubation failure and reintubation in neonates.
- Automated analysis of cardiorespiratory signals shows promise in predicting extubation readiness.
Purpose of the Study:
- To develop an automated predictor of extubation readiness for extremely preterm infants.
- To combine clinical tools with novel, automated cardiorespiratory measures for improved prediction accuracy.
- To assist clinicians in identifying the optimal time for extubation, thereby reducing morbidities.
Main Methods:
- A prospective, multicenter observational study involving 250 extremely preterm infants (birth weight ≤1250 g).
- Recording and automated analysis of cardiorespiratory signals prior to planned extubation.
- Utilizing machine learning to combine signal metrics with clinical variables for prediction model development (APEX).
Main Results:
- Development of an Automated system for Prediction of EXtubation (APEX) readiness.
- APEX integrates data acquisition, signal analysis, and outcome prediction into a single application.
- Prospective validation of APEX performance in 50 additional infants is planned.
Conclusions:
- This research will provide quantitative evidence to guide clinical decisions on extubation timing for preterm infants.
- The developed system has the potential to significantly improve extubation outcomes in extremely preterm populations.
- Successful extubation prediction can lead to reduced infant morbidities associated with mechanical ventilation and reintubation.
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:
Clinicaltrials.gov identifier: NCT01909947 . Registered on July 17 2013. Trial sponsor: Canadian Institutes of Health Research (CIHR).
Related Concept Videos
01:24Endotracheal Tube Extubation
Procedure
Extubation removes the endotracheal tube (ETT) from the patient on mechanical ventilation. It requires a well-coordinated, multidisciplinary approach involving physicians, nurses, respiratory therapists, and other healthcare professionals....
01:29Assessment of Ventilation II: Respiratory Depth and Rhythm
Respiratory depth measures the volume of air inhaled or exhaled during a breath. It can vary from shallow to deep and typically remains consistent when a person is at rest or asleep. Occasionally, individuals will automatically inhale deeply, known as sighing, which inflates the lungs with more air than normal breathing.
To assess respiratory depth, observe the degree of chest excursion or movement: