Abstract
Background Lung ultrasound (LUS) has been used to diagnose
neonatal respiratory diseases. However, few simple method has been
reported to predict respiratory support needs(RSN). Our aim was to
determine the diagnostic accuracy of a semiquantitative LUS assessment
method predicting respiratory support need.
Methods We conducted a prospective diagnostic accuracy study
following the STARD (Standards for the Reporting of Diagnostic Accuracy
Studies) guidelines at a tertiary level academic hospital between 2019
and 2020. After birth, infants were transferred to a monitoring room to
determine NICU treatment need. 310 late preterm and term infants with
respiratory symptoms enrolled. The LUS assessment was performed for each
participant at one of the following times: 0.5 h, 1 h, 2 h, 4 h, and 6 h
after birth. Reliability was tested by ROC analysis. Surfactant
administration and other RSNs were based on the 2019 European guidelines
as well as the infant’s clinical condition.
Results 74 have RSN, and 236 were healthy according to a 3-day
follow-up confirmation. Six LUS imaging patterns were found. Two
”high-risk” patterns were highly correlated with RSN(area under the
curve (AUC) = 0.95; 95% CI, 0.92-0.98, p<0.001). This
accuracy is supported by the AUC of ”low-risk” patterns (0.89, 95% CI,
0.85-0.93, p<0.001). The predictive value of LUS is greater
than that of only using respiratory symptoms (e.g., respiratory rate)
(AUC of LUS vs AUC of respiratory rate, p<0.01).
Conclusions LUS is a useful tool to predict RSN and is more
reliable than assessments based on respiratory symptoms alone.
Key words Lung ultrasound; respiratory support; predictive
value; ROC