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

Age Range:Any
Start Date:September 2013
End Date:December 2018

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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

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.

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
We found this trial at
101 Dudley St
Providence, Rhode Island 02905
(401) 274-1100
Women and Infants Hospital of Rhode Island Women & Infants Hospital of Rhode Island, a...
Providence, RI
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5050 Anthony Wayne Dr
Detroit, Michigan 48201
(313) 577-2424
Principal Investigator: Sanjay Chawla, MD
Phone: 313-745-5638
Wayne State University Founded in 1868, Wayne State University is a nationally recognized metropolitan research...
Detroit, MI
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Montreal, Quebec
Principal Investigator: Guilherme M Sant'Anna, MD
Phone: 1-514-412-4400
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