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J Pediatr Infect Dis
DOI: 10.1055/s-0044-1791522
Original Article

Deregulated Expression of Long Non-coding RNA ZFAS1 as a Predictive Biomarker for Respiratory Failure in Severe Pneumonia Children and Its Impact on Clinical Outcome

Huaying Zhu
1   Department of Radiological Nursing, Shaoxing Second Hospital Medical Community General Hospital, Shaoxing, China
,
Shandan Wang 
2   Department of Neonatology, Zhuji Maternal and Child Health Hospital, Zhuji, China
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Abstract

Objective Severe pneumonia is a common pediatric disease, often complicated by respiratory failure (RF). The expression changes of long non-coding RNA zinc finger antisense 1 (ZFAS1) were examined in children with severe pneumonia, as well as their predictive value in the occurrence of RF and poor outcomes.

Methods A total of 120 children with severe pneumonia were included, 60 of whom had RF. RT-qPCR was used to detect mRNA levels. Deaths during the follow-up period were recorded through a 28-day follow-up survey. Receiver operating characteristic (ROC) curve and Kaplan Meier (K-M) plot were drawn to display diagnostic and predictive values, with the help of multiple logistic and Cox regression analysis.

Results A sharp rise of serum ZFAS1 was tested in severe pneumonia children, providing a area under the receiver operator characteristic curve (AUC-ROC) of 0.920, with a sensitivity of 81.67% and a specificity of 90.00%. Serum ZFAS1 (OR = 5.832, 95% CI = 2.283–14.899) was found to be associated with the occurrence of RF after adjusting other clinical indexes, with an AUC-ROC of 0.843. ZFAS1 (HR = 4.624, 95% CI = 1.318–16.217, P < 0.05) was an independent influence factor for the poor prognosis. Cases with high ZFAS1 levels had worse clinical outcomes.

Conclusion Monitoring serum ZFAS1 levels is helpful in assessing severe pneumonia in children, especially for early identification of cases with RF. High serum ZFAS1 levels have a certain predictive value for poor prognosis in patients.

Keywords

ZFAS1 - severe pneumonia - respiratory failure - predictive value


Publication History

Received: 12 May 2024

Accepted: 01 September 2024

Article published online:
04 October 2024

© 2024. Thieme. All rights reserved.

Georg Thieme Verlag KG
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