Introduction
Respiratory distress syndrome (RDS) is a common cause of respiratory
morbidity in premature infants due structural and functional immaturity
of the lungs together with deficiency of alveolar surfactant, resulting
in microatelectasis and low lung volumes 1,2. The
mainstay in the management of RDS is appropriate respiratory support and
surfactant replacement as needed to achieve and maintain functional
residual capacity (FRC) for better gaseous exchange.
Bronchopulmonary dysplasia (BPD) is the most common complication of
prematurity, leading to many long-term morbidities and adverse
neurodevelopmental outcomes 3. The Neonatal Research
Network collected data on over 34,000 infants born at 22–28 weeks’
gestation between 1993–2012 and demonstrated significant increases in
survival among infants born at 23- 25 weeks gestational age4. These infants are at a high risk of developing BPD
– with an incidence of 60–80% 5. Despite the
increased use of antenatal corticosteroids, surfactant, and improved
ventilation techniques, the incidence of BPD remains unchanged and may
be increasing 6,7. Several treatment strategies remain
in place to prevent BPD, with one such strategy being avoidance of
mechanical ventilation altogether to prevent ventilator-induced lung
injury and use of early noninvasive continuous positive airway pressure
(CPAP). Over the past decade, trials have demonstrated the feasibility
and apparent benefits of providing early CPAP, including a reduced need
for intubation and surfactant therapy 6,8,9. The
clinical outcomes for infants who succeed on CPAP are excellent, with
low rates of BPD, mortality, intraventricular hemorrhage, retinopathy of
prematurity, and lower risk for adverse neurodevelopmental sequelae in
school-age 10.
However, despite best efforts, CPAP failure rates remain high and is
associated with mortality and significant morbidity such as pneumothorax
and BPD 5,11. Identification of these infants in
attempt to predict failure will allow early proactive intervention in
the course of the disease, in turn reducing the complications of RDS and
CPAP failure.
Previous research studies have evaluated variables that might predict
CPAP failure. Several factors to predict treatment failure have been
studied including demographics, perinatal and neonatal care variables,
and was shown that increased fraction of inspired oxygen (FiO2)
requirement in the first few hours of life (HOL) was most consistent to
predict CPAP failure 11–13. These studies focused on
perinatal and neonatal variables, known to be risk factors of severe
RDS.
In this study, we assess whether the use of machine learning and
electronic decision support systems may prove valuable to predict CPAP
failure in preterm infants with RDS and allow early proactive
intervention to minimize CPAP failure burden.