Am J Perinatol
DOI: 10.1055/s-0044-1779500
Original Article

Substantiating and Adopting Lung Ultrasound Scores to Predict Surfactant Need in Preterm Neonates with Respiratory Distress Syndrome within an Institution

1   Neonatology Division, Department of Pediatrics, University of Utah, Salt Lake City, Utah
2   Department of Radiology and Imaging Science, University of Utah, Salt Lake City, Utah
,
Christopher Torsitano
1   Neonatology Division, Department of Pediatrics, University of Utah, Salt Lake City, Utah
2   Department of Radiology and Imaging Science, University of Utah, Salt Lake City, Utah
,
3   Department of Diagnostic Imaging, Intermountain Healthcare, Salt Lake City, Utah
,
Olive Konana
4   Neonatology Division, Intermountain Healthcare, Salt Lake City, Utah
,
5   Neonatology Division, Department of Pediatrics, Loma Linda University School of Medicine, Loma Linda, California
› Author Affiliations
Funding This work was supported by Chiesi USA. B.C. was the principal investigator of this investigator-initiated research grant. This company had no role in the study design, conduct, management, and analysis. The manuscript was prepared and submitted independently from Chiesi USA. The other authors had no grants, and no other support was received during the preparation of this manuscript.

Abstract

Objective Administering surfactant timely and appropriately is important to minimize lung injury but remains challenging in preterm neonates with respiratory distress syndrome. The published literature supports that lung ultrasound (LUS) score can predict surfactant need. Neonatal LUS scanning specification and parameter setting guidelines have been recently published for standardization. However, variations in scanning protocols and machine settings hinder its clinical implementation widely. This observational study aims to internally validate the suggested LUS protocol in a neonatal intensive care unit to establish a correlation between LUS scores and surfactant need as the first step of integrating LUS in the clinical practice.

Study Design LUS was performed on 40 eligible preterm neonates within 3 hours after birth or before surfactant administration between May 2020 and March 2021. The neonates were between 27 and 32 weeks' gestational age, and all had respiratory distress. Neonates with known congenital anomalies were excluded. A high-frequency linear probe was used to obtain LUS images from six lung zones which were scored using a 0 to 3 system, yielding a maximum of 18 points. Treating physicians were blinded to the LUS score. Receiver operating characteristic analysis determined the optimal LUS score cutoff for predicting surfactant need.

Results Fifteen of the 40 neonates (38%) required higher oxygen fraction and received surfactant. In our cohort, an LUS score ≥10 was identified as the optimal cutoff for predicting surfactant need, with a sensitivity of 80% and specificity of 84%. The area under the curve was 0.8 (p = 0.0003). LUS predicted surfactant need at a median of 3.5 hours earlier than traditional clinical decision (p < 0.0037).

Conclusion LUS is a helpful adjunct for predicting surfactant need in preterm neonates. This study describes an approach to implement the LUS protocol and score for clinical decision-making in the clinical practice.

Key Points

  • LUS is a helpful adjunct for predicting surfactant need in preterm neonates.

  • Machine setting variation and probe selection may affect LUS image and score.

  • LUS score should be validated at the local unit before clinical implementation.

Authors' Contributions

All authors contributed to the study concept and design. Material preparation, data collection and analysis were performed by B.C. and C.T. Y.S., S.G., and O.K. performed concept development and manuscript editing. The first draft of the manuscript was written by B.C., and all authors commented on previous versions of the manuscripts. All authors read and approved the final manuscript.


Supplementary Material



Publication History

Received: 09 August 2023

Accepted: 08 January 2024

Article published online:
12 February 2024

© 2024. Thieme. All rights reserved.

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