Clinical trial NCT01909947: Prediction of Extubation Readiness in Extreme Preterm Infants by the Automated Analysis of CardioRespiratory Behavior
Official title: Prediction of Extubation Readiness in Extreme Preterm Infants by the Automated Analysis of CardioRespiratory Behavior: the APEX Study
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Prediction of Extubation Readiness
Keywords: Respiratory rate variability
Heart rate variability
Extubation readiness
Extubation failure
Clinical predictors
Lead sponsor: McGill University Health Centre/Research Institute of the McGill University Health Centre
Sponsor class: OTHER
Collaborators: name: Canadian Institutes of Health Research (CIHR)
name: Wayne State University
name: Brown University
First posted: 2013-07-29
Enrollment: 266
Sex: ALL
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Extreme preterm infants who are requiring endotracheal tube mechanical ventilation (ETT-MV).

Brief 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.

Detailed description: 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.

Eligibility criteria: 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

Primary outcomes:
measure: Extubation Failure
timeFrame: Within 72 hours of extubation
Secondary outcomes:
measure: The need for reintubation within 72h of the first planned extubation
timeFrame: Within 72 hours of extubation
measure: The need for reintubation
timeFrame: Anytime from the first planned extubation until discharge from the neonatal intensive care unit
Other outcomes:
measure: Total duration of ETT-MV
timeFrame: Participants will be followed for the duration of hospital stay, an expected average of 10 weeks
measure: Intraventricular hemorrhage
timeFrame: Participants will be followed for the duration of hospital stay, an expected average of 10 weeks
measure: Patent Ductus Arteriosus
timeFrame: Participants will be followed for the duration of hospital stay, an expected average of 10 weeks
measure: Oxygen supplementation at 28 days of life
timeFrame: This outcome will be assessed when participants have 28 days of life
measure: Bronchopulmonary Dysplasia
timeFrame: This outcome will be assessed when participants are 36 weeks post-conceptual age
measure: Retinopathy of Prematurity
timeFrame: This outcome will be assessed at the time of the first eye exam (approximately 31 weeks PCA) until the final eye exam prior to hospital discharge
measure: Necrotizing Enterocolitis
timeFrame: Participants will be followed for the duration of hospital stay, an expected average of 10 weeks
measure: Death
timeFrame: Participants will be followed for the duration of hospital stay in the NICU, an expected average of 10 weeks
Locations:
city: Detroit
country: United States
facility: Wayne State University
state: Michigan
city: Providence
country: United States
facility: Women and Infants Hospital of Rhode Island
state: Rhode Island
city: Montreal
country: Canada
facility: Royal Victoria Hospital
state: Quebec
city: Montreal
country: Canada
facility: Montreal Children's Hospital
state: Quebec
city: Montreal
country: Canada
facility: Jewish General Hospital
state: Quebec
Study officials:
affiliation: McGill University
name: Guilherme M Sant'Anna, MD
role: STUDY_CHAIR
affiliation: McGill University
name: Guilherme M Sant'Anna, MD
role: PRINCIPAL_INVESTIGATOR
affiliation: McGill University
name: Robert E Kearney, PhD
role: PRINCIPAL_INVESTIGATOR
affiliation: McGill University
name: Wissam Shalish, MD
role: PRINCIPAL_INVESTIGATOR
affiliation: McGill University
name: Karen A Brown, MD
role: PRINCIPAL_INVESTIGATOR
affiliation: McGill University
name: Doina Precup
role: PRINCIPAL_INVESTIGATOR
affiliation: Wayne State University
name: Sanjay Chawla, MD
role: PRINCIPAL_INVESTIGATOR
affiliation: Brown University
name: Martin Keszler, MD
role: PRINCIPAL_INVESTIGATOR
References:
citation: Shalish W, Kanbar LJ, Rao S, Robles-Rubio CA, Kovacs L, Chawla S, Keszler M, Precup D, Brown K, Kearney RE, Sant'Anna GM. Prediction of Extubation readiness in extremely preterm infants by the automated analysis of cardiorespiratory behavior: study protocol. BMC Pediatr. 2017 Jul 17;17(1):167. doi: 10.1186/s12887-017-0911-z.