Abstract
Background Lung ultrasound (LUS) is widely used to diagnose
neonatal respiratory diseases. However, to our knowledge, few
straightforward method was reported to predict respiratory support need
precisely. Our aim is to determine the diagnostic accuracy of a
semiquantitative LUS assessent method predicting the need for
respiratory support.
Methods We conducted a prospective diagnostic accuracy study
following STARD (Standards for the Reporting of Diagnostic Accuracy
Studies) guidelines at a tertiary level academic hospital between 2019
to 2020. 310 late preterm and term infants enrolled. They were delivered
in the obstetric department and transferred to a monitoring room to
determine whether they need NICU treatment. The LUS assessment was
performed for each participant at one of following timings–0.5h, 1h,
2h, 4h, 6h after birth. Reliability was tested by ROC analysis.
Surfactant administration and other respiratory support were based on
2019 European guidelines as well as their clinical condition.
Results 74 were confirmed to need respiratory support and 236
were healthy according to a 3-day follow up. Six LUS image patterns can
be seen in these infants right after birth. Two ”high-risk” patterns
well relate to respiratory support need(area under the curve(AUC) =
0.95; 95% CI, 0.92-0.98, p<0.001). This reliability can be
supported by AUC of ”low-risk” patterns(AUC = 0.89, 95%CI, 0.85-0.93,
p<0.001). Predictive value of LUS is much greater than that of
using respiratory symptoms(e.g.respiratory rate)(AUC of LUS vs AUC of
respiratory rate, p<0.01).
Conclusions LUS can predict respiratory support need and is
more reliable than the assessment based on respiratory symptoms.
Key words Lung ultrasound; respiratory support; predictive
value; ROC