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

NCT ID: NCT01909947

Last Updated: 2019-04-01

Basic Information

• Recruitment Status: COMPLETED
• Total Enrollment: 266 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2013-09-30
• Study Completion Date: 2018-12-31

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.

Conditions

Prediction of Extubation Readiness

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: PROSPECTIVE

Study Groups

Intubated extreme preterm infants

Infants with a birth weight ≤ 1250 grams and requiring endotracheal tube and mechanical ventilation

Cardiorespiratory signal acquisition

Intervention Type: OTHER

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

Interventions

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

Intervention Type: OTHER

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

• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Canadian Institutes of Health Research (CIHR)

OTHER_GOV

Sponsor Role: collaborator

Wayne State University

OTHER

Sponsor Role: collaborator

Brown University

OTHER

Sponsor Role: collaborator

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

OTHER

Sponsor Role: lead

Responsible Party

Guilherme Sant'Anna, MD

Associate Professor of Pediatrics

Responsibility Role: PRINCIPAL_INVESTIGATOR

Principal Investigators

Guilherme M Sant'Anna, MD

Role: STUDY_CHAIR

McGill University

Guilherme M Sant'Anna, MD

Role: PRINCIPAL_INVESTIGATOR

McGill University

Robert E Kearney, PhD

Role: PRINCIPAL_INVESTIGATOR

McGill University

Wissam Shalish, MD

Role: PRINCIPAL_INVESTIGATOR

McGill University

Karen A Brown, MD

Role: PRINCIPAL_INVESTIGATOR

McGill University

Doina Precup

Role: PRINCIPAL_INVESTIGATOR

McGill University

Sanjay Chawla, MD

Role: PRINCIPAL_INVESTIGATOR

Wayne State University

Martin Keszler, MD

Role: PRINCIPAL_INVESTIGATOR

Brown University

Locations

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

Countries

United States; Canada

References

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.

Reference Type: DERIVED

PMID: 37193586 (View on PubMed)

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.

Reference Type: DERIVED

PMID: 28716018 (View on PubMed)

Other Identifiers

288299

Identifier Type: OTHER_GRANT

Identifier Source: secondary_id

12-387-PED

Identifier Type: OTHER

Identifier Source: secondary_id

APEX 01

Identifier Type: -

Identifier Source: org_study_id