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

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McGill University

Status

Completed

Conditions

Prediction of Extubation Readiness

Treatments

Other: Cardiorespiratory signal acquisition

Study type

Observational

Funder types

Other

Identifiers

NCT01909947
288299 (Other Grant/Funding Number)
APEX 01
12-387-PED (Other Identifier)

Details and patient eligibility

About

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.

Full 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.

Enrollment

266 patients

Sex

All

Volunteers

No Healthy Volunteers

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

Trial design

266 participants in 1 patient group

Intubated extreme preterm infants
Description:
Infants with a birth weight ≤ 1250 grams and requiring endotracheal tube and mechanical ventilation
Treatment:
Other: Cardiorespiratory signal acquisition

Trial contacts and locations

5

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Data sourced from clinicaltrials.gov

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