Prediction of Extubation Readiness in Extreme Preterm Infants by the Automated Analysis of CardioRespiratory Behavior

Study Overview
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Brief Summary
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The investigators hypothesize that machine learning methods using a combination of novel, quantitative measures of cardio-respiratory variability can accurately predict the optimal time to extubate extreme preterm infants. In this multicenter prospective study, cardiorespiratory signals will be recorded from 250 extreme preterm infants who are eligible for extubation. Automated signal analysis algorithms will compute a variety of metrics for each infant describing the cardiorespiratory state. Machine learning methods will then be used to find the optimal combination of these statistical measures and clinical features that provide the best overall predictor of extubation readiness. Finally, investigators will develop an Automated system for Prediction of EXtubation (APEX) 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 (NICU). The performance of APEX will later be clinically validated in 50 additional infants prospectively.

Detailed Description
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At birth, extreme preterm infants (≤28 weeks) have inconsistent respiratory drive, airway instability, surfactant deficiency and immature lungs that frequently result in respiratory failure. Management of these infants is difficult and most will require endotracheal intubation and mechanical ventilation (ETT-MV) within the first days of life to survive. ETT-MV is an invasive therapy that is associated with adverse clinical outcomes including ventilator-associated pneumonia, impaired neurodevelopment, and increased mortality. Consequently, clinicians try to remove ETT-MV as quickly as possible. However, 25 to 35% of these extubation attempts will fail and infants will require reintubation, an intervention that is also associated with increased morbidity and mortality. Therefore physicians must determine the optimal time for extubation which minimizes the duration of ETT-MV and maximizes the chances of success. A variety of objective measures have been proposed to assist with this decision but none has proven to be useful clinically. Investigators from this group have recently explored the predictive power of indices of autonomic nervous system function based on measurements of heart rate (HRV) and respiratory variability (RV). The use of sophisticated, automated algorithms to analyze those cardiorespiratory signals have shown some promising preliminary results in predicting which infants can be extubated successfully.

Official Title
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Prediction of Extubation Readiness in Extreme Preterm Infants by the Automated Analysis of CardioRespiratory Behavior: the APEX Study

Conditions
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Prediction of Extubation Readiness

Intervention / Treatment
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* Other: Cardiorespiratory signal acquisition


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: All infants admitted to the NICU with a birth weight ≤ 1250 grams AND Need for endotracheal tube mechanical ventilation Exclusion Criteria: Infants with major congenital anomalies Infants with congenital heart disease and cardiac arrhythmias Infants receiving vasopressor or sedative drugs at the time of extubation Infants extubated directly from high frequency ventilation Infants extubated to room air, oxyhood or low-flow nasal cannula
Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Intubated extreme preterm infants<br>Infants with a birth weight ≤ 1250 grams and requiring endotracheal tube and mechanical ventilation | Other: Cardiorespiratory signal acquisition<br>* Cardiorespiratory signals will measure heart rate (using electrocardiography), chest and abdominal movements (using respiratory inductance plethysmography) and oxygen saturation (using pulse oximetry). Data will be acquired during 2 recording periods: A 60-minute period while the infant receives any mode of conventional mechanical ventilation A 5-minute period prior to extubation while the mode of ventilation is switched to endotracheal tube CPAP (Continuous Positive Airway Pressure), so that the respiratory pattern will be controlled by the infant<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Extubation Failure | Infants will be considered to have failed extubation if they meet one or more of the following criteria within 72 hours of extubation: Fraction of inspired oxygen (FiO2) > 0.5 in order to maintain oxygen saturation (SpO2) > 88% or PaO2 > 45 mmHg (for 2 consecutive hours) PaCO2 > 55-60 mmHg with a pH < 7.25 in two consecutive blood gases done 1-2 hours apart 1 episode of apnea requiring positive pressure ventilation with bag and mask Multiple episodes of apnea (≥ 6 episodes / 6 hours). | Within 72 hours of extubation | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| The need for reintubation within 72h of the first planned extubation | The decision to re-intubate will be made by the responsible physician, who may not always follow the guidelines stated in the primary objective. Therefore, reintubation will be assessed as a secondary outcome. | Within 72 hours of extubation | 
| The need for reintubation | Infants will be prospectively followed from birth until discharge from the NICU. Therefore, infants who require reintubation at any time point from the first planned extubation until discharge from the neonatal intensive care unit will be documented | Anytime from the first planned extubation until discharge from the neonatal intensive care unit | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Respiratory rate variability, Heart rate variability, Extubation readiness, Extubation failure, Clinical predictors