Evidence map›Paper›PMID 35787798›Full record

ArticleBMC pregnancy and childbirth2022

Predicting risk of severe neonatal outcomes in preterm infants born from mother with prelabor rupture of membranes.

Lu Zhuang, Zhan-Kui Li, Yuan-Fang Zhu, Rong Ju, Shao-Dong Hua, Chun-Zhi Yu, Xing Li, Yan-Ping Zhang, Lei Li, Yan Yu and 6 more

Abstract read
In one paragraph

Article in BMC pregnancy and childbirth, 2022. 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

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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

16 authors.

Lu Zhuang *Senior Department of Pediatrics, the Seventh Medical Center of PLA General Hospital, Beijing, China.
Zhan-Kui Li *Northwest Women's and Children's Hospital, Xi'an, Shanxi province, China.
Yuan-Fang Zhu *Shenzhen Baoan Women's and Children's Hospital, Jinan University, Shenzhen, Guangdong province, China.
Rong Ju *School of Medicine, Chengdu Women's and Children's Central Hospital, University of Electronic Science and Technology of China, Chengdu, China.
Shao-Dong HuaSenior Department of Pediatrics, the Seventh Medical Center of PLA General Hospital, Beijing, China.
Chun-Zhi YuNorthwest Women's and Children's Hospital, Xi'an, Shanxi province, China.
Xing LiSenior Department of Pediatrics, the Seventh Medical Center of PLA General Hospital, Beijing, China.
Yan-Ping ZhangSenior Department of Pediatrics, the Seventh Medical Center of PLA General Hospital, Beijing, China.
Lei LiSenior Department of Pediatrics, the Seventh Medical Center of PLA General Hospital, Beijing, China.
Yan YuShenzhen Baoan Women's and Children's Hospital, Jinan University, Shenzhen, Guangdong province, China.
Wen ZengSchool of Medicine, Chengdu Women's and Children's Central Hospital, University of Electronic Science and Technology of China, Chengdu, China.
Jie CuiSenior Department of Pediatrics, the Seventh Medical Center of PLA General Hospital, Beijing, China.
Xin-Yu ChenSenior Department of Pediatrics, the Seventh Medical Center of PLA General Hospital, Beijing, China.
Jing-Ya PengSenior Department of Pediatrics, the Seventh Medical Center of PLA General Hospital, Beijing, China.
Ting LiSenior Department of Pediatrics, the Seventh Medical Center of PLA General Hospital, Beijing, China.
Zhi-Chun FengSenior Department of Pediatrics, the Seventh Medical Center of PLA General Hospital, Beijing, China. zhichunfeng81@163.com.

Funding

Capital Medical Development Research Fund of Beijing 20053044National Natural Science Foundation of China 81170602
6 · The paper itself

Abstract

backgroundPerinatal complications are common burdens for neonates born from mother with pPROM. Physicians and parents sometimes need to make critical decisions about neonatal care with short- and long-term implications on infant's health and families and it is important to predict severe neonatal outcomes with high accuracy.

methodsThe study was based on our prospective study on 1001 preterm infants born from mother with pPROM from August 1, 2017, to March 31, 2018 in three hospitals in China. Multivariable logistic regression analysis was applied to build a predicting model incorporating obstetric and neonatal characteristics available within the first day of NICU admission. We used enhanced bootstrap resampling for internal validation.

resultsOne thousand one-hundred pregnancies with PROM at preterm with a single fetus were included in our study. SNO was diagnosed in 180 (17.98%) neonates. On multivariate analysis of the primary cohort, independent factors for SNO were respiratory support on the first day,, surfactant on day 1, and birth weight, which were selected into the nomogram. The model displayed good discrimination with a C-index of 0.838 (95%CI, 0.802-0.874) and good calibration performance. High C-index value of 0.835 could still be reached in the internal validation and the calibration curve showed good agreement. Decision curve analysis showed if the threshold is > 15%, using our model would achieve higher net benefit than model with birthweight as the only one predictor.

conclusionVariables available on the first day in NICU including respiratory support on the first day, the use of surfactant on the first day and birthweight could be used to predict the risk of SNO in infants born from mother with pPROM with good discrimination and calibration performance.

Indexed as

Infant, PrematureMothersBirth WeightFemaleHumansInfantInfant, NewbornPregnancyPremature Rupture of Fetal MembranesProspective StudiesSurface-Active AgentsSurface-Active AgentsPreterm prelabor rupture of membranesPrognostic nomogramSevere neonatal outcomes

Identifiers

PMID35787798
PMCPMC9252037

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Registered trials

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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 OpenQuestion graph.