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

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