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.