Evidence map›Paper›PMID 30060450›Full record

ArticleJournal of clinical medicine2018

Predictions of Preterm Birth from Early Pregnancy Characteristics: Born in Guangzhou Cohort Study.

Jian-Rong He, Rema Ramakrishnan, Yu-Mian Lai, Wei-Dong Li, Xuan Zhao, Yan Hu, Nian-Nian Chen, Fang Hu, Jin-Hua Lu, Xue-Ling Wei and 9 more

Abstract read
In one paragraph

Article in Journal of clinical medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 2 pooled it
–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

12 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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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

19 authors.

Jian-Rong HeDivision of Birth Cohort Study, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou 510623, China. jianrong.he@bigcs.org.ORCID 0000-0003-3837-5126
Rema RamakrishnanNuffield Department of Women's & Reproductive Health, University of Oxford, Oxford OX3 9DU, UK. rema.ramakrishnan@wrh.ox.ac.uk.
Yu-Mian LaiDepartment of Obstetrics and Gynecology, Guangzhou Women and Children Medical Center, Guangzhou Medical University, Guangzhou 510623, China. yumian.lai@bigcs.org.
Wei-Dong LiDivision of Birth Cohort Study, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou 510623, China. weidong.li@bigcs.org.
Xuan ZhaoDivision of Birth Cohort Study, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou 510623, China. xuan.zhao@bigcs.org.
Yan HuDivision of Birth Cohort Study, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou 510623, China. yan.hu@bigcs.org.
Nian-Nian ChenDivision of Birth Cohort Study, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou 510623, China. niannian.chen@bigcs.org.
Fang HuDivision of Birth Cohort Study, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou 510623, China. fang.hu@bigcs.org.
Jin-Hua LuDivision of Birth Cohort Study, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou 510623, China. jinhua.lu@bigcs.org.
Xue-Ling WeiDivision of Birth Cohort Study, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou 510623, China. xueling.wei@bigcs.org.
Ming-Yang YuanDivision of Birth Cohort Study, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou 510623, China. mingyang.yuan@bigcs.org.
Song-Ying ShenDivision of Birth Cohort Study, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou 510623, China. songying.shen@bigcs.org.
Lan QiuDivision of Birth Cohort Study, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou 510623, China. lan.qiu@bigcs.org.
Qiao-Zhu ChenDepartment of Obstetrics and Gynecology, Guangzhou Women and Children Medical Center, Guangzhou Medical University, Guangzhou 510623, China. qiaozhu.chen@bigcs.org.
Cui-Yue HuDivision of Birth Cohort Study, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou 510623, China. cuiyue.hu@gwcmc.org.
Kar Keung ChengInstitute of Applied Health Research, University of Birmingham, Birmingham B15 2TT, UK. k.k.cheng@bham.ac.uk.
Ben Willem J MolDepartment of Obstetrics and Gynecology, Monash University, Clayton, Victoria 3204, Australia. ben.mol@monash.edu.
Hui-Min XiaDivision of Birth Cohort Study, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou 510623, China. huimin.xia@bigcs.org.
Xiu QiuDivision of Birth Cohort Study, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou 510623, China. xiu.qiu@bigcs.org.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Preterm birth (PTB, <37 weeks) is the leading cause of death in children <5 years of age. Early risk prediction for PTB would enable early monitoring and intervention. However, such prediction models have been rarely reported, especially in low- and middle-income areas. We used data on a number of easily accessible predictors during early pregnancy from 9044 women in Born in Guangzhou Cohort Study, China to generate prediction models for overall PTB and spontaneous, iatrogenic, late (34⁻36 weeks), and early (<34 weeks) PTB. Models were constructed using the Cox proportional hazard model, and their performance was evaluated by Harrell's c and D statistics and calibration plot. We further performed a systematic review to identify published models and validated them in our population. Our new prediction models had moderate discrimination, with Harrell's c statistics ranging from 0.60⁻0.66 for overall and subtypes of PTB. Significant predictors included maternal age, height, history of preterm delivery, amount of vaginal bleeding, folic acid intake before pregnancy, and passive smoking during pregnancy. Calibration plots showed good fit for all models except for early PTB. We validated three published models, all of which were from studies conducted in high-income countries; the area under receiver operating characteristic for these models ranged from 0.50 to 0.56. Based on early pregnancy characteristics, our models have moderate predictive ability for PTB. Future studies should consider inclusion of laboratory markers for the prediction of PTB.

Indexed as

Chineseearly pregnancyexternal validationpredictionpreterm birth

Identifiers

PMID30060450
PMCPMC6111770

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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.