Background
Lung ultrasound (LUS) has become a widely used bedside examination technique in neonatal intensive care units (NICUs) because it is radiationless and can be easily and immediately performed by frontline neonatologists. A comprehensive and standardized LUS guideline has been developed1-2 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, and pneumothorax (PTX)9. Some imaging patterns, such as ”compact B-line”, ”white lung” and ”consolidation”, are considered to relate to those diseases.
The most common neonatal respiratory condition in late preterm infants is TTN, and these infants usually have good outcomes with continuous positive airway pressure (CPAP) treatment or hood oxygen support10. However, some degree of surfactant damage, which can cause secondary RDS11, may occur in severe or long-lasting TTN. Some late preterm and term infants with RDS also seem to have a more unfavorable prognosis even if pulmonary surfactant(PS) is applied12-13. In addition, other issues (e.g., MAS, PTX, pneumonia) that may lead to severe outcomes are common in these infants and may only manifest immediately after birth. Thus, identifying these potential patients is important for neonatologists. As it is radiationless and convenient, LUS a promising predictive tool to realize this goal.
Roselyne Brat et al14. described the usefulness of LUS in predicting PS in preterm infants. They used a relatively precise scoring system and tested its relation to oxygenation. Others performed similar research and confirmed Brat’s findings15-16. However, they did not provide information to predict other respiratory support needs, and calculating scores according to different images may be complicated or challenging in some cases. By contrast, Raimondi et al17-18 used only three straightforward LUS patterns to predict NICU admission or the need for intubation. Nevertheless, we believe that using a semiquantitative method would be more precise to predict respiratory support needs.
Our goal was to test whether a simplified semiquantitative evaluation method based on high-risk LUS imaging patterns could predict respiratory support needs. We hypothesized that the number of scanning regions with ”high-risk” patterns has high predictive value for respiratory support needs in preterm and term infants and is more reliable than assessments based on respiratory symptoms.