loading page

Ultrasound performed right after birth can predict the respiratory support needs of neonates----A diagnostic accuracy study
  • +4
  • Guannan Xi,
  • Jiale Dai,
  • Wang Xuefeng,
  • Chengqiu Lu,
  • Fei Luo,
  • Yun Yang,
  • Jimei Wang
Guannan Xi
Obstetrics and Gynecology Hospital of Fudan University

Corresponding Author:[email protected]

Author Profile
Jiale Dai
Obstetrics and Gynecology Hospital of Fudan University
Author Profile
Wang Xuefeng
Obstetrics and Gynecology Hospital of Fudan University
Author Profile
Chengqiu Lu
Obstetrics and Gynecology Hospital of Fudan University
Author Profile
Fei Luo
Obstetrics and Gynecology Hospital of Fudan University
Author Profile
Yun Yang
Obstetrics and Gynecology Hospital of Fudan University
Author Profile
Jimei Wang
Obstetrics and Gynecology Hospital of Fudan University
Author Profile

Abstract

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.
10 Dec 2020Submitted to Pediatric Pulmonology
11 Dec 2020Submission Checks Completed
11 Dec 2020Assigned to Editor
13 Dec 2020Reviewer(s) Assigned
25 Jan 2021Review(s) Completed, Editorial Evaluation Pending
26 Jan 2021Editorial Decision: Revise Major
04 Feb 20211st Revision Received
05 Feb 2021Submission Checks Completed
05 Feb 2021Assigned to Editor
05 Feb 2021Reviewer(s) Assigned
16 Feb 2021Review(s) Completed, Editorial Evaluation Pending
16 Feb 2021Editorial Decision: Revise Minor
24 Feb 20212nd Revision Received
25 Feb 2021Assigned to Editor
25 Feb 2021Submission Checks Completed
25 Feb 2021Reviewer(s) Assigned
18 Mar 2021Review(s) Completed, Editorial Evaluation Pending
18 Mar 2021Editorial Decision: Accept