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

Summary

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.

Conditions

• Prediction of Extubation Readiness

Interventions

OTHER

Cardiorespiratory signal acquisition

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: 1. A 60-minute period while the infant receives any mode of conventional mechanical ventilation 2. 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

Sponsors & Collaborators

• Canadian Institutes of Health Research (CIHR)

  collaborator OTHER_GOV

• Wayne State University

  collaborator OTHER

• Brown University

  collaborator OTHER

• McGill University Health Centre/Research Institute of the McGill University Health Centre

  lead OTHER

Principal Investigators

• Guilherme M Sant'Anna, MD · McGill University

• Guilherme M Sant'Anna, MD · McGill University

• Robert E Kearney, PhD · McGill University

• Wissam Shalish, MD · McGill University

• Karen A Brown, MD · McGill University

• Doina Precup · McGill University

• Sanjay Chawla, MD · Wayne State University

• Martin Keszler, MD · Brown University

Eligibility

Sex

ALL

Healthy Volunteers

No

Timeline & Regulatory

Start

2013-09-30

Primary Completion

2018-10-31

Completion

2018-12-31

Countries

• United States
• Canada

Study Locations