Evidence map›Paper›PMID 41044744›Full record

ArticleBMC pregnancy and childbirth2025

Development and validation of a predictive nomogram for antenatal depression in China.

Renjie Yu, Luyang Guan, Hongbao Chen, Jun Zhang, Xiaohong Li, Shengying Zhang, Jia Liu, Peiyun Zhang, Linghua Kong, Lanlan Zhang and 4 more

Abstract readValidation Study
In one paragraph

Article in BMC pregnancy and childbirth, 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

14 authors.

Renjie Yu *Suzhou Guangji Hospital & Affiliated Guangji Hospital of Soochow University, Soochow Univeristy, No. 11 Guangqian Road, Suzhou, 215137, Jiangsu Province, PR China.
Luyang Guan *Suzhou Guangji Hospital & Affiliated Guangji Hospital of Soochow University, Soochow Univeristy, No. 11 Guangqian Road, Suzhou, 215137, Jiangsu Province, PR China.
Hongbao Chen *Suzhou Guangji Hospital & Affiliated Guangji Hospital of Soochow University, Soochow Univeristy, No. 11 Guangqian Road, Suzhou, 215137, Jiangsu Province, PR China.
Jun Zhang *Suzhou Guangji Hospital & Affiliated Guangji Hospital of Soochow University, Soochow Univeristy, No. 11 Guangqian Road, Suzhou, 215137, Jiangsu Province, PR China.
Xiaohong LiAffiliated Hospital of Medical School, Suzhou Hospital, Nanjing University, Suzhou, 215000, Jiangsu Province, PR China.
Shengying ZhangSuzhou Wuzhong People's Hospital, No. 61, Dongwu North Road, Wuzhong District, Suzhou, 215128, Jiangsu Province, PR China.
Jia LiuSuzhou Municipal Hospital, 26 Daoqian Street, Suzhou City, 215002, Jiangsu Province, PR China.
Peiyun ZhangSuzhou Guangji Hospital & Affiliated Guangji Hospital of Soochow University, Soochow Univeristy, No. 11 Guangqian Road, Suzhou, 215137, Jiangsu Province, PR China.
Linghua KongSuzhou Guangji Hospital & Affiliated Guangji Hospital of Soochow University, Soochow Univeristy, No. 11 Guangqian Road, Suzhou, 215137, Jiangsu Province, PR China.
Lanlan ZhangSuzhou Guangji Hospital & Affiliated Guangji Hospital of Soochow University, Soochow Univeristy, No. 11 Guangqian Road, Suzhou, 215137, Jiangsu Province, PR China.
Tingting JinSuzhou Guangji Hospital & Affiliated Guangji Hospital of Soochow University, Soochow Univeristy, No. 11 Guangqian Road, Suzhou, 215137, Jiangsu Province, PR China.
Ju GaoSuzhou Guangji Hospital & Affiliated Guangji Hospital of Soochow University, Soochow Univeristy, No. 11 Guangqian Road, Suzhou, 215137, Jiangsu Province, PR China.
Xiaobin ZhangSuzhou Guangji Hospital & Affiliated Guangji Hospital of Soochow University, Soochow Univeristy, No. 11 Guangqian Road, Suzhou, 215137, Jiangsu Province, PR China. zhangxiaobim@163.com.
Yong YangSuzhou Guangji Hospital & Affiliated Guangji Hospital of Soochow University, Soochow Univeristy, No. 11 Guangqian Road, Suzhou, 215137, Jiangsu Province, PR China. szgjyy@126.com.ORCID http://orcid.org/0000-0003-2327-2028

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAntenatal depression is a significant public health concern, with detrimental effects on maternal and infant health. However, there is currently a lack of comprehensive predictive tools specifically tailored for antenatal depression in China. The present study aimed to develop and validate a predictive nomogram for antenatal depression in the Chinese population.

methodsA cross-sectional study was conducted in multiple healthcare settings in Suzhou, Jiangsu Province, China, spanning from March 2017 to December 2019. A total of 3694 pregnant women were included in the study. Demographic and clinical characteristics were collected using structured questionnaires. The Edinburgh Postnatal Depression Scale and various psychological assessments were used to assess antenatal depression and associated factors. LASSO regression and logistic regression analyses were employed to develop the predictive model, and internal validation was performed to assess its performance.

resultsAmong the 3694 participants, 473 (12.8%) pregnant women were positive for antenatal depression. The developed predictive Model incorporated neuroticism, negative coping strategies, and anxiety as significant predictors. The nomogram demonstrated accurate risk assessment capabilities, with an AUC of 0.90 in the validation set. The model exhibited good calibration and clinical utility. LIMITATIONS: Limitations of this paper include the cross-sectional design, biases like self-reporting and non-randomized sampling, highlighting the need for future longitudinal studies and diverse population validation to enhance the model's robustness in varied sociocultural contexts.

conclusionThe predictive model for antenatal depression in the Chinese population provides a valuable tool for early detection, intervention, and personalized care for pregnant women.

Indexed as

DepressionNomogramsPregnancy ComplicationsAdultAnxietyChinaCross-Sectional StudiesFemaleHumansPregnancyPsychiatric Status Rating ScalesRisk AssessmentSurveys and QuestionnairesYoung AdultAntenatal depressionEdinburgh postpartum depression scaleNomogramPrediction models

Identifiers

PMID41044744
PMCPMC12495645

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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.