Prediction of Extubation ReadinessNCT01909947
Prediction of Extubation Readiness in Extreme Preterm Infants by the Automated Analysis of CardioRespiratory Behavior
An observational study of Prediction of Extubation Readiness, run by McGill University Health Centre/Research Institute of the McGill University Health Centre at 5 sites in 2 countries.
Trial 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. Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Prediction of Extubation Readiness
- What is being tested?
- Cardiorespiratory signal acquisition
- How many people
- 266 participants
- Where
- 5 sitesin 2 countries
- Study dates
- Sep 2013to Dec 2018
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Any age
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Study population: Extreme preterm infants who are requiring endotracheal tube mechanical ventilation (ETT-MV).
Who may be able to join
- All infants admitted to the NICU with a birth weight ≤ 1250 grams AND
- Need for endotracheal tube mechanical ventilation
Who cannot join
- 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
Trial locations and contacts
5 sites in 2 countries.
5 sites in 2 countries
Canada3 sites
Royal Victoria Hospital
Montreal, Quebec, H3A 1A1
Montreal Children's Hospital
Montreal, Quebec, H3H 1P3
Jewish General Hospital
Montreal, Quebec, H3T 1E2
United States2 sites
Wayne State University
Detroit, Michigan, 48201
Women and Infants Hospital of Rhode Island
Providence, Rhode Island, 02905
About this study
Official title: Prediction of Extubation Readiness in Extreme Preterm Infants by the Automated Analysis of CardioRespiratory Behavior: the APEX Study
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.
Read the 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.
- Conditions
- Prediction of Extubation Readiness
- Being tested
- Cardiorespiratory signal acquisition
- Keywords
- Respiratory rate variability, Heart rate variability, Extubation readiness, Extubation failure, Clinical predictors
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Cardiorespiratory signal acquisitionOther
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
| Study group | Role | What they receive |
|---|---|---|
| Intubated extreme preterm infants | Infants with a birth weight ≤ 1250 grams and requiring endotracheal tube and mechanical ventilation Receives: Other: Cardiorespiratory signal acquisition |
What is being measured
The primary outcome is the main question the study is designed to answer.
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).
Measured: Within 72 hours of extubation
Secondary outcomes (2)
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.
Measured: 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
Measured: Anytime from the first planned extubation until discharge from the neonatal intensive care unit
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted29 Jul 2013 (estimated)
- Study startSep 2013
- Primary completionOct 2018
- Study completionDec 2018
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (2)Papers the sponsor has linked to this study.
- Other trials for Prediction of Extubation ReadinessStudies of the same condition, open and under way.
- More trials run by McGill University Health Centre/Research Institute of the McGill University Health CentreThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 266 participants (actual)
Sponsor and investigators
- Lead sponsor
- McGill University Health Centre/Research Institute of the McGill University Health Centre
- Sponsor type
- University, hospital or other
- Collaborators
- Canadian Institutes of Health Research (CIHR), Wayne State University, Brown University
- Responsible party
- Principal investigator, Guilherme Sant'Anna, MD, McGill University Health Centre/Research Institute of the McGill University Health Centre
- Investigators
- Guilherme M Sant'Anna, MD, Study chair, McGill UniversityGuilherme M Sant'Anna, MD, Principal investigator, McGill UniversityRobert E Kearney, PhD, Principal investigator, McGill UniversityWissam Shalish, MD, Principal investigator, McGill UniversityKaren A Brown, MD, Principal investigator, McGill UniversityDoina Precup, Principal investigator, McGill UniversitySanjay Chawla, MD, Principal investigator, Wayne State UniversityMartin Keszler, MD, Principal investigator, Brown University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT01909947
- Sponsor's protocol ID
- APEX 01
- Other IDs
- 288299, 12-387-PED
- Status last verified
- Mar 2019
- First submitted
- 17 Jul 2013
- Last updated
- 1 Apr 2019
- Results posted
- No
- Data monitoring committee
- No
Publications
Alarcon-Martinez T, Latremouille S, Kovacs L, Kearney RE, Sant'Anna GM, Shalish W. Clinical usefulness of reintubation criteria in extremely preterm infants: a cohort study. Arch Dis Child Fetal Neonatal Ed. 2023 Nov;108(6):643-648. doi: 10.1136/archdischild-2022-325245. Epub 2023 May 16.
PubMed 37193586Shalish 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.
PubMed 28716018
Study at a glance
From the registry record, last updated by the sponsor on 1 Apr 2019. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Prediction of Extubation Readiness in Extreme Preterm Infants by the Automated Analysis of CardioRespiratory Behavior: the APEX Study
- ClinicalTrials.gov ID
- NCT01909947
- Acronym / sponsor ID
- APEX 01
- Conditions
- Prediction of Extubation Readiness
- Keywords
- Respiratory rate variability, Heart rate variability, Extubation readiness, Extubation failure, Clinical predictors
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 266 participants (actual)
- Lead sponsor
- McGill University Health Centre/Research Institute of the McGill University Health CentreUniversity, hospital or other
- Collaborators
- Canadian Institutes of Health Research (CIHR), Wayne State University, Brown University
- Study start
- Sep 2013 (actual)
- Primary completion
- Oct 2018 (actual)
- Study completion
- Dec 2018 (actual)
- First posted
- 29 Jul 2013 (estimated)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Intubated extreme preterm infants | – | Other: Cardiorespiratory signal acquisitionInfants with a birth weight ≤ 1250 grams and requiring endotracheal tube and mechanical ventilation |
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- Extreme preterm infants who are requiring endotracheal tube mechanical ventilation (ETT-MV).
Key inclusion criteria (2 listed)
- All infants admitted to the NICU with a birth weight ≤ 1250 grams AND
- Need for endotracheal tube mechanical ventilation
Key exclusion criteria (5 listed)
- 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
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Cardiorespiratory signal acquisitionCardiorespiratory 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 | Other | Intubated extreme preterm infants |
Primary outcome
Extubation Failure
Time frame: Within 72 hours of extubation
Infants will be considered to have failed extubation if they meet one or more of the following criteria within 72 hours of extubation: 1. Fraction of inspired oxygen (FiO2) > 0.5 in order to maintain oxygen saturation (SpO2) > 88% or PaO2 > 45 mmHg (for 2 consecutive hours) 2. PaCO2 > 55-60 mmHg with a pH < 7.25 in two consecutive blood gases done 1-2 hours apart 3. 1 episode of apnea requiring positive pressure ventilation with bag and mask 4. Multiple episodes of apnea (≥ 6 episodes / 6 hours).
2 secondary outcomes
The need for reintubation within 72h of the first planned extubation
Time frame: Within 72 hours of extubation
The need for reintubation
Time frame: Anytime from the first planned extubation until discharge from the neonatal intensive care unit
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 1 Apr 2019.
- Derived from this study: PubMed 37193586Alarcon-Martinez T, Latremouille S, Kovacs L, Kearney RE, Sant'Anna GM, Shalish W. Clinical usefulness of reintubation criteria in extremely preterm infants: a cohort study. Arch Dis Child Fetal Neonatal Ed. 2023 Nov;108(6):643-648. doi: 10.1136/archdischild-2022-325245. Epub 2023 May 16.
- Derived from this study: PubMed 28716018Shalish 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.
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Guilherme M Sant'Anna, MD study chair
McGill University
Guilherme M Sant'Anna, MD principal investigator
McGill University
Robert E Kearney, PhD principal investigator
McGill University
5 sites in 2 countries: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Prediction of Extubation Readiness, run by McGill University Health Centre/Research Institute of the McGill University Health Centre at 5 sites in 2 countries.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 2 inclusion and 5 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 1 Apr 2019. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 5 sites in Canada, United States. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Extubation Failure (time frame: Within 72 hours of extubation). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT01909947, which also lists 2 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 1 Apr 2019.