Evidence map›Paper›PMID 41306507›Full record

ArticleFrontiers in medicine2025

A risk prediction model of spontaneous miscarriage in women with threatened miscarriage: a prospective cohort study.

Chee Wai Ku, Yu Bin Tan, Marie Min Tse Tan, Sze Ing Tan, Carissa Shi Tong Ng, Jyotsna Ramakrishna, Hiu Gwan Chan, Thiam Chye Tan, Jerry Kok Yen Chan, See Ling Loy

Abstract read
In one paragraph

Article in Frontiers in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

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5 · Who and what money

Authors and funding

10 authors.

Chee Wai KuDepartment of Reproductive Medicine, KK Women's and Children's Hospital, Singapore, Singapore.
Yu Bin TanDuke-NUS Medical School, Singapore, Singapore.
Marie Min Tse TanYong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
Sze Ing TanLee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore.
Carissa Shi Tong NgYong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
Jyotsna RamakrishnaLee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore.
Hiu Gwan ChanPaediatric Endocrinology Service, KK Women's and Children's Hospital, Singapore, Singapore.
Thiam Chye TanObstetrics and Gynaecology Academic Clinical Programme (OBGYN ACP), SingHealth Duke-NUS Academic Medical Centre, Singapore, Singapore.
Jerry Kok Yen ChanDepartment of Reproductive Medicine, KK Women's and Children's Hospital, Singapore, Singapore.
See Ling LoyDepartment of Reproductive Medicine, KK Women's and Children's Hospital, Singapore, Singapore.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: This study aims to develop a holistic risk prediction triage tool for women with threatened miscarriage by integrating clinical, biochemical, and radiological factors. Additionally, we aim to assess the performance of various models incorporating different factor combinations, adaptable for diverse resource settings. Methods: This prospective cohort study included 1,080 women with single intrauterine pregnancies at 5-12 weeks' gestation presenting with threatened miscarriage at KK Women's and Children's Hospital, Singapore between October 2017 and May 2023. Multivariable logistic regression and risk-score models were developed using maternal age, nausea, prior miscarriages, serum progesterone, gestational age, and fetal heart activity. Model performance was assessed via Area Under Receiver Operating Characteristic (AUROC) curve with 10-fold cross-validation. The primary outcome was miscarriage by 16 weeks. Results: The miscarriage rate was 17.3%. Low serum progesterone (< 35 nmol/L) was the strongest predictor [odds ratio (OR) 26.3; 95% confidence interval (CI): 16.6-41.5], followed by absent fetal heart detection (OR 4.05; CI: 2.37-6.92) and increasing maternal age (OR 1.05; CI: 1.00-1.11). Higher gestational age decreased miscarriage risk (OR 0.57; CI: 0.45-0.73). The integrated model demonstrated high predictive accuracy (AUROC 0.90; CI: 0.87-0.93). The risk-score model achieved AUROC 0.82, with 80% sensitivity and 84% specificity at cut-off of 2. Discussion: This clinically practical holistic prediction tool enables early identification of women at high risk of miscarriage following threatened miscarriage. It may guide personalized counseling and targeted interventions in both high- and low-resource settings.

Indexed as

clinical triage toolmiscarriage predictionrisk prediction modelserum progesteronethreatened miscarriage

Identifiers

PMID41306507
PMCPMC12644018

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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.