Discussion
We modified the LUS scanning method and developed a simplified assessment system (based on ”high-risk” imaging patterns) to predict respiratory support needs for the first time. To the best of our knowledge, this system is practical and useful in obstetrics and gynecology hospitals that need to identify infants with potential lung diseases in the several hours immediately after birth. This approach can help physicians identify these patients before their respiratory symptoms deteriorate, and chest X-ray can be applied so that physicians can implement NICU care earlier.
Findings and interpretation: This study has two clinically relevant findings. (1) Four ”low-risk” patterns, and two ”high-risk” patterns can be found in late preterm or term infants immediately after birth. While two ”low-risk” patterns, the ”moderate discrete B-line” and ”abundant discrete B-line” patterns, were reported to be pathological in previous studies27-28, our study shows that they can be seen as strong evidence of healthy infants immediately after birth. This discrepancy may be because of different extent of delay in lung fluid clearance. This delay results in a small number of alveoli that are uninflated and full of fluid29-31 but not enough to cause TTN. (2) Two ”high-risk” patterns have high predictive accuracy for respiratory support needs. These two patterns are also regarded as evidence of other diseases, such RDS15, MAS8 and pneumonia32. This concordance indicates that our findings of ”high-risk” patterns are highly likely to be an early stage of RDS or MAS, especially when infants have only mild respiratory symptoms.
Because a total of ten scanning regions were evaluated, the number of regions with ”high-risk” patterns was inversely related to those with ”low-risk” patterns. Thus, the ROC curve of ”low-risk” patterns can support the predictive accuracy of ”high-risk” patterns.
Strengths and Limitations of our study: To our knowledge, this is the most straightforward semiquantitative method to predict lung diseases in late preterm and term infants. The assessment requires only a count of the number of scanning regions on the chest wall with ”high-risk” patterns, and these patterns are easy to discern. More importantly, finding more than two regions with ”high-risk” patterns provides 87.10% sensitivity and 88.02% specificity. Coupled with LUS’s radiationless and convenience, this method can be used as an effective lung disease screening tool between the delivery room and NICU.
Nevertheless, there are some limitations to our study. Most significantly, there was an insufficient number of patients who received CPAP (23/74), MV (18/74), and PS (29/74). This insufficiency made it difficult to draw a convincing conclusion to predict these individual modes of advanced respiratory support. But considering our goal is to discern potential lung diseases patients, and nearly all of their treatment starts with a hood oxygen support need, it’s not necessary to predict every advanced respiratory support need at such an early stage of life. Besides, this limitation, if needed, can be improved in later research containing more patients with severe respiratory diseases. The second limitation is the possible inconsistency of image interpretation. Our study used only one LUS interpreter due to the limited budget, so we did not test consistency between interpreters. This may lead to a variance in predictive accuracy. This drawback may be corrected in later research by us or others. The last limitation is the concern about the overuse of LUS. Our participants were all late preterm and term infants, which means that they had a lower incidence of lung diseases than smaller preterm infants. Of all infants who undergo LUS, only a small part may develop lung diseases and need respiratory support. However, because LUS is radiationless, easy to perform, and economical, we think it is reasonable to perform LUS on every late preterm and term infant with respiratory symptoms. Also, as smaller preterm infants will receive more attention from physicians from birth, their respiratory issues are less likely to be ignored than those in late preterm and term infants.
Comparison with other studies: Many studies have verified the diagnostic value of LUS for neonatal lung diseases2, 28, 33-34. The difference between prior studies and ours is that we focused on predictive value. As we found that these abnormal LUS patterns identified immediately after birth do not have significant specificity for certain diseases, we can use LUS to predict respiratory needs for all kinds of severe lung diseases. Recently, some studies have also paid attention to the predictive value of LUS in neonatology14-16, 35. They evaluated the predictive value of LUS for PS need in preterm infants, whereas we studied the need for all kinds of respiratory support, including CPAP and MV, which are closely related to severe lung disease. Therefore, we believe our study is a good complement to current studies. Moreover, it is very useful since it provides evidence to support early interventions in high-risk infants.
Conclusion: Our assessment method allows a straightforward semiquantitative use of LUS to discern infants with potential lung diseases right after their birth. The LUS “high-risk” patterns show good accuracy to predict respiratory support needs in late preterm and term infants who manifest mild respiratory difficulty.
List of abbreviations
LUS: Lung ultrasound
STARD: Standards for the Reporting of Diagnostic Accuracy Studies
AUC: Area under the curve
ROC: Receiver operating characteristic curve
NICU: Neonate intensive care unit
RDS: Respiratory distress syndrome
TTN: Transient tachypnea of the neonate
MAS: Meconium aspiration syndrome
PTX: Pneumothorax
CPAP: Continuous positive airway pressure
SN: Serial number
MV: Mechanical ventilation
PS: Pulmonary surfactant
PEEP: Positive end-expiratory pressure
TcSO2: Transcutaneous oxygen saturation
GA: Gestational age
BW: Birth weight;
SGA: Small for gestational age;
SD: Standard deviation;
CI: Confidence interval;
DB: Discrete B-line
RR: Respiratory rate
HR: Heart rate;