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STUDY PROTOCOL article

Front. Pediatr.

Sec. Neonatology

This article is part of the Research TopicRefining Precision Medicine through AI and Multi-omics IntegrationView all 8 articles

The Shenzhen Neonatal ARDS Cohort Study (SZ-NARDS): A Multi-Omics Approach to Elucidating Regional Epidemiology, Refined Phenotypes, and Long-Term Outcomes

Provisionally accepted
Ruolin  ZhangRuolin Zhang1Jie  ShenJie Shen1Linying  YangLinying Yang2Yanzhen  XuYanzhen Xu3Yanping  GuoYanping Guo4Lichun  BaiLichun Bai5Hanni  LinHanni Lin6Xianhong  ChenXianhong Chen7Yan  HuangYan Huang8Xin  GuoXin Guo9Zhangbin  YuZhangbin Yu3Jinxing  FengJinxing Feng10*Jun  ChenJun Chen11*
  • 1Department of Neonatology, Nanshan Maternity and Child Health Care Hospital, Shenzhen, China
  • 2Department of Neonatology, Shenzhen Children's Hospital, Shenzhen, China
  • 3Department of Neonatology, Shenzhen People’s Hospital (The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, China
  • 4Department of Neonatology, Peking University Shenzhen Hospital, Shenzhen, China
  • 5Department of Neonatology, Shenzhen Guangming District People's Hospital, Shenzhen, China
  • 6Department of Neonatology, Shenzhen Luohu Hospital Group Luohu People's Hospital, Shenzhen, China
  • 7Department of Neonatology, Longgang District Central Hospital of Shenzhen, Shenzhen, China
  • 8Neonatology, Shenzhen Baoan Women's and Children's Hospital, Jinan University, Shenzhen, China
  • 9Department of Neonatology, Longgang District Maternity and Child Healthcare Hospital of Shenzhen City, Shenzhen, China
  • 10Department of Neonatology, Shenzhen Children's Hospital, Shenzhen, Shenzhen, China
  • 11Department of Neonatology, Shenzhen Nanshan Maternity and Child Healthcare Hospital, Shenzhen, China

The final, formatted version of the article will be published soon.

Neonatal Acute Respiratory Distress Syndrome (NARDS) is a critical contributor to neonatal morbidity and mortality, with a global health burden that varies significantly by region. The Montreux definition provides a unified diagnostic framework; however, a significant clinical paradox exists. A prospective cohort in China reported a NARDS mortality rate of 12.6%, which is notably lower than the 17-24% reported in a large-scale international prospective study. The underlying reasons for this discrepancy remain to be elucidated, whether due to differences in etiology, clinical practice, or patient demographics. Methods: The Shenzhen Neonatal ARDS Cohort Study (SZ-NARDS) is a prospective, multicenter observational cohort study spanning from 2025 to 2028, designed to address this knowledge gap. We will enroll more than 1,000 neonates who meet the Montreux criteria across nine tertiary neonatal intensive care units (NICUs) in Shenzhen, China. Longitudinal data collection includes granular clinical parameters, respiratory support metrics, and multi-modal biospecimens for deep phenotyping and multi-omics profiling. Survivors will undergo rigorous follow-up until 36 months' corrected age, with standardized neurodevelopmental, pulmonary, and growth assessments. Results: The primary objective of this study is to characterize the epidemiology of NARDS in this regional population and to test the following hypotheses: (1) The true incidence, etiology, and mortality rates of NARDS in Shenzhen will differ from existing international and Chinese cohorts, and these differences can be systematically explained by specific clinical and demographic factors. A multimodal predictive model that integrates early clinical variables with multi-omics biomarkers has the potential to accurately identify neonates at high risk for severe NARDS (oxygenation index (OI) ≥ 16) and long-term adverse outcomes (Area Under the Receiver Operating Characteristic Curve (AUROC) > 0.85). Conclusions: The SZ-NARDS cohort is uniquely positioned to resolve a major clinical contradiction in NARDS epidemiology. By integrating deep phenotyping with a longitudinal biobank and advanced machine learning algorithms, this initiative will generate a comprehensive dataset. This dataset will serve to refine existing prognostic models, identify regional disparities in disease biology, and inform the development of precision medicine interventions for this vulnerable population. Trial Registration: Chinese Clinical Trial Registry, ChiCTR2400093854.

Keywords: neonatal acute respiratory distress syndrome, Montreux definition, prospective cohort, Epidemiology, precision medicine, bioinformatics, machine learning, Long-term outcomes

Received: 12 Aug 2025; Accepted: 27 Oct 2025.

Copyright: © 2025 Zhang, Shen, Yang, Xu, Guo, Bai, Lin, Chen, Huang, Guo, Yu, Feng and Chen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence:
Jinxing Feng, szfjx2013@hotmail.com
Jun Chen, 75chj@163.com

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