Risk assessment and early prediction of intraventricular hemorrhage in extremely preterm infants

This study aimed to identify the risk factors associated with intraventricular hemorrhage (IVH) in extremely preterm infants (EPIs), focusing on early-stage prediction to improve clinical outcomes. A retrospective cohort study was conducted at Guangzhou Women and Children's Medical Center, including 189 EPIs born between January 2019 and December 2023. Infants were categorized into IVH and non-IVH groups based on head ultrasound findings. Risk factors were assessed using univariate and multivariate analyses, and a predictive model for IVH was developed. Of the 189 EPIs, 80 (42.3%) developed IVH, with 26 (13.8%) experiencing severe IVH. Gestational age was identified as a significant protective factor (OR = 0.565, p = 0.023), while invasive mechanical ventilation (IMV) was a key risk factor (OR = 2.718, p = 0.012). The predictive model demonstrated good performance, with an AUC of 0.753 (95% CI: 0.681-0.825). Gestational age and IMV are critical factors in the development of IVH in EPIs. Early identification of high-risk infants based on these factors can aid in timely interventions to reduce IVH incidence and improve outcomes.

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Publication Details

Journal
Scientific Reports
Published
2025-05-19
DOI
https://doi.org/10.1038/s41598-025-02061-4
Citations
4
Primary Topic
Neonatal Respiratory Health Research
Type
article
Field-Weighted Citation Impact
6.07

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article

Risk assessment and early prediction of intraventricular hemorrhage in extremely preterm infants

Xiaopeng Zhao, Wei Zhou, Yueju Cai, Yanyan Song et al.
4 citations
Scientific Reports
Neonatal Respiratory Health Research
6.07
article

Risk assessment and early prediction of intraventricular hemorrhage in extremely preterm infants

Xiaopeng Zhao, Wei Zhou, Yueju Cai, Yanyan Song, Xiaolan Li
article en
4 citations

Abstract

This study aimed to identify the risk factors associated with intraventricular hemorrhage (IVH) in extremely preterm infants (EPIs), focusing on early-stage prediction to improve clinical outcomes. A retrospective cohort study was conducted at Guangzhou Women and Children's Medical Center, including 189 EPIs born between January 2019 and December 2023. Infants were categorized into IVH and non-IVH groups based on head ultrasound findings. Risk factors were assessed using univariate and multivariate analyses, and a predictive model for IVH was developed. Of the 189 EPIs, 80 (42.3%) developed IVH, with 26 (13.8%) experiencing severe IVH. Gestational age was identified as a significant protective factor (OR = 0.565, p = 0.023), while invasive mechanical ventilation (IMV) was a key risk factor (OR = 2.718, p = 0.012). The predictive model demonstrated good performance, with an AUC of 0.753 (95% CI: 0.681-0.825). Gestational age and IMV are critical factors in the development of IVH in EPIs. Early identification of high-risk infants based on these factors can aid in timely interventions to reduce IVH incidence and improve outcomes.

Scientific ReportsVol. 15(1)
Guangzhou Women and Children Medical Center (CN), Guangzhou Medical University (CN)
Wu Jieping Medical Foundation
Openalex Percentile: Top 4%
Neonatal Respiratory Health Research
6.07
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Risk assessment and early prediction of intraventricular hemorrhage in extremely preterm infants — Xiaopeng Zhao, Wei Zhou, et al. · Scientific Reports (2025) | TGRS Research Map | TGRS