Evidence map›Paper›PMID 39025934›Full record

Observational studyPediatric research2025

Two risk assessment models for predicting white matter injury in extremely preterm infants.

Shuting Song, Zhicheng Zhu, Ke Zhang, Mili Xiao, Ruiwei Gao, Qingping Li, Xiao Chen, Hua Mei, Lingkong Zeng, Yi Wei and 10 more

Abstract readMulticenter StudyObservational Study
PubMed Publisher
In one paragraph

Observational study in Pediatric research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

20 authors.

Shuting SongDepartment of Neonatology, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, China.
Zhicheng ZhuDepartment of Neonatology, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, China.
Ke ZhangDepartment of Neonatology, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, China.
Mili XiaoDepartment of Neonatology, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, China.
Ruiwei GaoDepartment of Neonatology, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, China.
Qingping LiDepartment of Neonatology, The Affiliated Hospital of Southwest Medical University, Sichuan, China.
Xiao ChenDepartment of Neonatology, The First Affiliated Hospital of Nanchang University, Jiangxi, China.
Hua MeiDepartment of Neonatology, The Affiliated Hospital Inner Mongolia Medical University, Inner Mongolia, China.
Lingkong ZengDepartment of Neonatology, Wuhan Woman and Children Medical Care Center, Hubei, China.
Yi WeiDepartment of Neonatology, Guilin Maternal and Child Health Hospital, Guangxi, China.
Yanpin ZhuDepartment of Neonatology, The First Affiliated Hospital of Xinjiang Medical University, Xinjiang, China.
Ya NuerDepartment of Neonatology, Xinjiang Uygur Autonomous Region People's Hospital, Xinjiang, China.
Ling YangDepartment of Neonatology, Hainan Women and Children's Medical Center, Hainan, China.
Wen LiDepartment of Neonatology, Qilu Hospital of Shandong University, Shandong, China.
Ting LiDepartment of Neonatology, Hunan Maternal and Child Health Care Hospital, Hunan, China.
Rong JuDepartment of Neonatology, Chengdu Woman's and Children's Center Hospital, Sichuan, China.
Yangfang LiDepartment of Neonatology, Kunming Children's Hospital, Yunnan, China.
Lian JiangDepartment of Neonatology, Fourth Hospital of Hebei Medical University, Hebei, China.
Chao ChenDepartment of Neonatology, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, China.
Li ZhuDepartment of Neonatology, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, China. zhuli_2023@126.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundExtremely preterm infants (EPIs) are at high-risk of white matter injury (WMI), leading to long-term neurodevelopmental impairments. We aimed to develop nomograms for WMI.

methodsThe study included patients from 31 provinces, spanning ten years. 6074 patients before 2018 were randomly divided into a training and internal validation group (7:3). The external validation group comprised 1492 patients from 2019. Predictors were identified using the least absolute shrinkage and selection operator (LASSO) and multivariable logistic regression and nomograms were constructed. Models' performance was evaluated using receiver operating characteristic (ROC), decision curve analysis (DCA) and calibration curves.

resultsThe prenatal nomogram included multiple gestation, premature rupture of membranes (PROM), chorioamnionitis, prenatal glucocorticoids, hypertensive disorder complicating pregnancy (HDCP) and Apgar 1 min, with area under the curve (AUC) of 0.805, 0.816 and 0.799 in the training, internal validation and external validation group, respectively. Days of mechanical ventilation (MV), shock, patent ductus arteriosus (PDA) ligation, intraventricular hemorrhage (IVH) grade III-IV, septicemia, hypothermia and necrotizing enterocolitis (NEC) stage II-III were identified as postpartum predictors. The AUCs were 0.791, 0.813 and 0.823 in the three groups, respectively. DCA and calibration curves showed good clinical utility and consistency.

conclusionThe two nomograms provide clinicians with precise and efficient tools for prediction of WMI. IMPACT: This study is a large-sample multicenter study, spanning 10 years. The two nomograms are convenient for identifying high-risk infants early, allowing for reducing poor prognosis.

Indexed as

Infant, Extremely PrematureNomogramsWhite MatterFemaleGestational AgeHumansInfant, NewbornMalePregnancyRisk AssessmentRisk FactorsROC Curve

Identifiers

PMID39025934

What Socratic holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.