Evidence map›Paper›PMID 36104673›Full record

ArticleBMC pediatrics2022

Maternal preterm birth prediction in the United States: a case-control database study.

Yan Li, Xiaoyu Fu, Xinmeng Guo, Huili Liang, Dongru Cao, Junmei Shi

Open access · goldAbstract read
In one paragraph

Article in BMC pediatrics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
1.6field-weighted citation impact, top 17% of its field
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

7 citing papers in PubMed, 11 citations in OpenAlex.

  1. Article
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  5. Prediction of Preterm Birth among Infants with Orofacial Cleft Defects.The Cleft palate-craniofacial journal : official publication of the American Cleft Palate-Craniofacial Association · 2025
    Article
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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

6 authors at 3 institutions in 1 country.

Yan LiDepartment of Obstetrics and Gynecology, Beijing Haidian Maternal and Child Healthcare Hospital, NO.33 Haidian South Road, Haidian District, Beijing, 100080, China.
Xiaoyu FuDepartment of Obstetrics and Gynecology, the Seventh Medical Centre, Chinese PLA General Hospital, Beijing, 100853, China.
Xinmeng GuoDepartment of Obstetrics and Gynecology, College of Medicine, Nankai University, Tianjin, 300071, China.
Huili LiangDepartment of Obstetrics and Gynecology, Beijing Haidian Maternal and Child Healthcare Hospital, NO.33 Haidian South Road, Haidian District, Beijing, 100080, China.
Dongru CaoDepartment of Obstetrics and Gynecology, Beijing Haidian Maternal and Child Healthcare Hospital, NO.33 Haidian South Road, Haidian District, Beijing, 100080, China.
Junmei ShiDepartment of Obstetrics and Gynecology, Beijing Haidian Maternal and Child Healthcare Hospital, NO.33 Haidian South Road, Haidian District, Beijing, 100080, China. shijunmeibj@outlook.com.
Beijing Haidian Hospital · CNChinese PLA General Hospital · CNNankai University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPreterm birth is serious public health worldwide, and early prediction of preterm birth in pregnant women may provide assistance for timely intervention and reduction of preterm birth. This study aimed to develop a preterm birth prediction model that is readily available and convenient for clinical application.

methodsData used in this case-control study were extracted from the National Vital Statistics System (NVSS) database between 2018 and 2019. Univariate and multivariate logistic regression analyses were utilized to find factors associated with preterm birth. Odds ratio (OR) and 95% confidence interval (CI) were used as effect measures. The area under the curve (AUC), accuracy, sensitivity, and specificity were utilized as model performance evaluation metrics.

resultsData from 3,006,989 pregnant women in 2019 and 3,039,922 pregnant women in 2018 were used for the model establishment and external validation, respectively. Of these 3,006,989 pregnant women, 324,700 (10.8%) had a preterm birth. Higher education level of pregnant women [bachelor (OR = 0.82; 95%CI, 0.81-0.84); master or above (OR = 0.82; 95%CI, 0.81-0.83)], pre-pregnancy overweight (OR = 0.96; 95%CI, 0.95-0.98) and obesity (OR = 0.94; 95%CI, 0.93-0.96), and prenatal care (OR = 0.48; 95%CI, 0.47-0.50) were associated with a reduced risk of preterm birth, while age ≥ 35 years (OR = 1.27; 95%CI, 1.26-1.29), black race (OR = 1.26; 95%CI, 1.23-1.29), pre-pregnancy underweight (OR = 1.26; 95%CI, 1.22-1.30), pregnancy smoking (OR = 1.27; 95%CI, 1.24-1.30), pre-pregnancy diabetes (OR = 2.08; 95%CI, 1.99-2.16), pre-pregnancy hypertension (OR = 2.22; 95%CI, 2.16-2.29), previous preterm birth (OR = 2.95; 95%CI, 2.88-3.01), and plurality (OR = 12.99; 95%CI, 12.73-13.24) were related to an increased risk of preterm birth. The AUC and accuracy of the prediction model in the testing set were 0.688 (95%CI, 0.686-0.689) and 0.762 (95%CI, 0.762-0.763), respectively. In addition, a nomogram based on information on pregnant women and their spouses was established to predict the risk of preterm birth in pregnant women.

conclusionsThe nomogram for predicting the risk of preterm birth in pregnant women had a good performance and the relevant predictors are readily available clinically, which may provide a simple tool for the prediction of preterm birth.

Indexed as

Premature BirthAdultCase-Control StudiesFemaleHumansInfant, NewbornOdds RatioPregnancyRisk FactorsThinnessUnited StatesArea under the curveNomogramPredictionPreterm birth

Identifiers

PMID36104673
PMCPMC9472432
OpenAlexW4295899663

What Socratic holds

Textmetadata
LicenceCC BY
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.