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

Completed

Conditions studied: Prediction of Extubation Readiness

In brief

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.

Key facts

Study ID

NCT01909947

Run by

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

People needed

266

Starts

2013-09-01

Expected to finish

2018-12-01

Last updated by the study team

2019-04-01

Who can join

Age: any. Sex: any. Healthy volunteers: not accepted.

You may qualify if…

• All infants admitted to the NICU with a birth weight ≤ 1250 grams AND
• Need for endotracheal tube mechanical ventilation

You may not qualify if…

• 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

Where it is running

• Wayne State University — Detroit, Michigan, United States
• Women and Infants Hospital of Rhode Island — Providence, Rhode Island, United States
• Royal Victoria Hospital — Montreal, Quebec, Canada
• Montreal Children's Hospital — Montreal, Quebec, Canada
• Jewish General Hospital — Montreal, Quebec, Canada

Full record on ClinicalTrials.gov

Trial information comes from ClinicalTrials.gov and is refreshed daily. TrialsForMe does not provide medical care and does not run the studies it lists.