Work

High-risk pregnancies according to five risk assessment tools in Northwestern China: Detection and parameters for diagnostic accuracy

Year: 2025

Type: article

Abstract: Problem considered High-risk pregnancy can be identified using various risk assessment tools. This study aimed to evaluate the detection and diagnostic accuracy parameters of high-risk pregnancies using five tools. Methods A prospective cohort study was conducted in Northwestern China with a sample of 1,211 pregnant women from three tertiary hospitals in Ningxia. Data on demographic characteristics, medical and surgical histories, obstetric and gynecological histories were collected at their first antenatal booking to classify participants into high-risk or non-high-risk groups based on five different risk schemes/tools; they are (i) color coding, (ii) modified Nesbitt, (iii) high-risk scoring standard, (iv) antenatal risk assessment, and (v) Coopland's high-risk pregnancy scoring. Maternal or neonatal mortality and morbidity outcomes were obtained from hospital information systems to serve as the gold standard for comparison. Results Among the 1,211 women, 615 (50.8%, 95% CI: 48.0 - 53.6) experienced at least one adverse maternal or neonatal outcome. The detection of high-risk pregnancies ranged from 17.6% to 46.8%. Sensitivity ranged from 23.6% to 52.4%, while specificity varied between 58.1% and 88.6%. All tools demonstrated accuracy levels above 55%, with the color coding tool showing the highest validity. Nearly half of the women experienced maternal or neonatal complications. Conclusion These findings highlight the importance of integrating pregnancy risk assessment using the color coding tool into routine antenatal care, alongside laboratory and ultrasound investigations at the first booking, to enhance targeted risk management and improve maternal-fetal outcomes.

Source: Clinical Epidemiology and Global Health

DOI: 10.1016/j.cegh.2025.102191

Authors: Xiali Yang, Nadzratulaiman Wan Nordin, Yanhua Ning, Bahiyah Abdullah, Nyi Nyi Naing, Xin Wee Chen

Institutions: Universiti Teknologi MARA, Sultan Zainal Abidin University

Language: English

Topic: Maternal and Neonatal Healthcare

Subfield: General Health Professions

Field: Health Professions

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,000