Background
Lung ultrasound (LUS) has been becoming a widely used bedside
examination technique in neonate intensive care unit(NICU), because it
is radiationless and can be easily and immediately performed by the
frontline neonatologists. A comprehensive and standardized LUS guideline
has been built up1-2 and validated by other
studies3-5. Most neonatal lung diseases can be
diagnosed using LUS, including respiratory distress
syndrome(RDS)6, transient tachypnea of the
neonate(TTN)7, meconium aspiration
syndrome(MAS)8,
pneumothorax(PTX)9.Some image patterns such as
”compact B-line”, ”white lung” and ”consolidation” are considered to
relate to those diseases.
The most common neonatal respiratory conditions in late preterms and
terms are TTN, and they usually have good outcomes with treatment
continuous positive airway pressure (CPAP) or hood oxygen
support10. However, some degree of surfactant damage,
which can cause secondary RDS11, may happen in severe
or long-lasting TTN. Some late preterm and term infants with RDS also
seem to have a more unfavorable prognosis even if PS
applied12-13. Besides, other issues(e.g., MAS, PTX,
pneumonia) that may lead to severe outcomes are common in these infants
and may only manifest slightly right after birth. Thus, identifying
those potential patients is important for neonatologists. And LUS, as it
is radiationless and convenient, is a promising predictive tool to
realize this goal.
Roselyne Brat et al14. described the usefulness of LUS
in predicting pulmonary surfactant in preterm infants. They used a
relatively precise score system and testified its relation to
oxygenation. Others did similar research and confirmed the Brat’s
findings15-16. However, they did not provide
information to predict other respiratory support need, and calculating
scores according to different images may be complicated or challenging
in some cases. By contrast, Raimondi et al17-18 .
using only three straightforward LUS patterns to predict NICU admission
or need for intubation. Nevertheless, we believe using a
semiquantitative method would be more precise to predict respiratory
support need.
Our goal is to test whether a simplified semiquantitative evaluating
method, based on the high-risk LUS image patterns, can predict
respiratory support need. We hypothesized that the number of scanning
regions with ”high-risk” patterns has high predictive value for
respiratory supoort need in the preterms and terms, and is more reliable
compared to the assessment based on respiratory symptoms.