Lung Ultrasound Score to Predict Surfactant Administration in Premature Neonates with Respiratory Distress Syndrome: A Systematic Review and Meta-analysis

Context: The present systematic review aimed to investigate whether the lung ultrasound score (LUS) can accurately predict surfactant administration in premature neonates with respiratory distress syndrome (RDS). Methods: The systematic review was conducted according to the Cochrane collaboration or the preferred reporting items for systematic review and meta-analyses (PRISMA) guidelines. English language databases included PubMed, Ovid, Cochrane Library, Embase, Web of Science, and Scopus. The coverage date of this review was from the inception of each database to the end of May 2022. Data were extracted independently by two authors, assessed for quality using the quality assessment of diagnostic accuracy studies-2 (QUADAS-2) tool, and analyzed for heterogeneity using MetaDisc1.4 software. Results: Seven eligible articles and 653 neonates were finally selected from 176 studies for meta-analysis. Considering the LUS to predict the surfactant need in premature neonates, we extracted the variable from the studies. Also, we plotted the summary receiver operating characteristic curve with an area under the curve (AUC) of 0.94 (95% confidence interval (CI): 0.92 - 0.95). Forest plots of the LUS showed a pooled sensitivity of 0.86 (95% CI: 0.82 - 0.90) and a pooled specificity of 0.79 (95% CI: 0.75 - 0.82). The Cochrane-Q test, chi-square test, and inconsistency index confirmed the heterogeneity of the non-threshold effect (I2 > 50% or P < 0.05). The meta-regression analysis showed that the relative diagnostic odds ratio for the number of recruited cases (> 100 vs. < 100) was 0.65 (95% CI: 0.33 - 0.98, P-value < 0.05). The comparison of the cut-offs of the 4 - 6 score vs. the 8 - 12 score yielded a Z of 21.44 (P < 0.001). Conclusions: The LUS accurately predicts the onset of RDS in premature neonates and can guide surfactant administration but is subject to the cut-off effect. Variation in cut-offs is related to gestational age and disease severity.