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Grant: Operating Grant

McGill University

$570,760.00

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

EF 2012-13

Résumé
More than half of pre-term infants, born at 28 weeks or less, have breathing problems severe enough to require mechanical ventilation for them to survive. In deciding when to remove them from the ventilator, the physician must balance conflicting issues. On one hand, it is desirable to discontinue ventilation as early as possible to avoid the adverse side effects of ventilation. On the other hand, an infant who is removed from the ventilator too early may need to be reconnected. This is not straightforward and is associated with additional adverse side effects. At present, the physician has few tools to assist in this decision and consequently more than 25% of the attempts to disconnect preterm infants from ventilation fail. Previous results have shown that infants who can be removed from ventilation successfully have different patterns of heart rate and breathing than those who fail. We will build on these results by combining non-invasive cardio-respiratory measurements, novel advanced signal processing algorithms, and machine learning methods. These will be used to create a tool that will predict the probability that mechanical ventilation can be successfully discontinued for a particular infant. This tool will provide physicians with the quantitative information needed for the informed management of ventilation of pre-term infants and hence improve patient care
Programme
Operating Grant
Bénéficiaire
Emplacement
Quebec, Canada
Type de bénéficiaire
Academia
Type
Grant
Source
CIHR Funding Decisions Database

Tel que publié

Id
268172
Name
Kearney Robert E
Region
Québec
Country
Canada
Orgtype
Institution Paid; Research Institution
Instname2
Human Development, Child and Youth Health
Theme2
Biomedical
Prcname2
Clinical Investigation - A: Reproduction, Maternal, Child and Youth Health
Projecttitle
Prediction of Extubation Readiness in Extreme Preterm Infants by the Automated Analysis of CardioRespiratory Behavior
Programtype2
Operating Grants
Orgnameinp2
McGill University
Cihramount2
$570,760
Cihrequipment2
$0
Programname2
Operating Grant
Keyworddelim
Cardio-Respiratopy; Extubation; Machine Learning; Patient Management; Premature Infants; Respiration
Abstract
More than half of pre-term infants, born at 28 weeks or less, have breathing problems severe enough to require mechanical ventilation for them to survive. In deciding when to remove them from the ventilator, the physician must balance conflicting issues. On one hand, it is desirable to discontinue ventilation as early as possible to avoid the adverse side effects of ventilation. On the other hand, an infant who is removed from the ventilator too early may need to be reconnected. This is not straightforward and is associated with additional adverse side effects. At present, the physician has few tools to assist in this decision and consequently more than 25% of the attempts to disconnect preterm infants from ventilation fail. Previous results have shown that infants who can be removed from ventilation successfully have different patterns of heart rate and breathing than those who fail. We will build on these results by combining non-invasive cardio-respiratory measurements, novel advanced signal processing algorithms, and machine learning methods. These will be used to create a tool that will predict the probability that mechanical ventilation can be successfully discontinued for a particular infant. This tool will provide physicians with the quantitative information needed for the informed management of ventilation of pre-term infants and hence improve patient care
Competitiondate
201209
Approvedterm2
5 yrs 0 mth
Pinamesdelim
Kearney, Robert E
Approvedterm
5 ans 0 mois
Cihrcontribution2
~ $570,760~ $0~