ArticleEClinicalMedicine2025
Early warning systems for identifying severe maternal outcomes: findings from the WHO global maternal sepsis study.
Article in EClinicalMedicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Critical care utilisation, and outcomes of critically ill obstetric patients in India: a multicentre ICU registry study (2019-2024).The Lancet regional health. Southeast Asia · 2026Article
- Racial Disparities and the Use of Artificial Intelligence for Predicting Maternal Mortality: A Literature Review.Epidemiologia (Basel, Switzerland) · 2026Review
- Artificial intelligence and predictive analytics in obstetric anesthesia: early warning for maternal complications.Current opinion in anaesthesiology · 2026Review
- Serum LINC01127 serves as a diagnostic biomarker for sepsis and its predictive value for clinical outcomes.Hereditas · 2026Article
- Advances and Challenges in Obstetric Intensive Care Medicine.Journal of clinical medicine · 2026Review
- Reducing post-cesarean sepsis: Current best practice in prevention and treatment.International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics · 2025Article
- LncRNA CYP1B1-AS1 as a clinical biomarker exacerbates sepsis inflammatory response via targeting miR- 18a- 5p.BMC immunology · 2025Article
- Uncovering the tacit: a qualitative study of obstetric nurses' risk recognition and decision-making in perinatal care.Frontiers in medicine · 2025Article
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Authors and funding
11 authors.
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Abstract
Background: Infections and sepsis are leading causes of morbidity and mortality in women during pregnancy and the post-pregnancy period. Using data from the 2017 WHO Global Maternal Sepsis Study, we explored the use of early warning systems (EWS) in women at risk of sepsis-related severe maternal outcomes. Methods: On April 27, 2023, we searched the literature for EWS in clinical use or research in obstetric populations. We calculated the proportion of women for whom each existing EWS identified them as at risk for developing severe maternal outcomes by infection severity (complications and severe maternal outcomes). Sensitivity, specificity, positive and negative likelihood ratios, diagnostic odds ratios, and J statistics were calculated to assess EWS performance. Machine learning was used to test the diagnostic potential of routine maternal sepsis markers. Findings: 21 EWS were assessed in 2560 women from 46 countries with suspected or confirmed infections. The NICE Risk Stratification tool, Modified Shock Index, maternity Systemic Inflammatory Response Syndrome, and Early Maternal Infection Prompts scores had high sensitivity (88.1-97.5%) for identifying sepsis-related severe maternal outcomes. The quick Sequential Organ Failure Assessment (SOFA) in Pregnancy score and Obstetrically modified SOFA had high specificity (90.4-100%) for identifying women with sepsis-related severe maternal outcomes. Furthermore, combinations of sepsis markers had very low sensitivity and high specificity using machine learning. Interpretation: No score demonstrated enough diagnostic accuracy to be used alone to identify sepsis. However, obstetric-and sepsis-specific EWS performed better for early identification of maternal sepsis than non-obstetric and non-sepsis-specific scoring systems. There are limitations to applying EWS to real-world data, mainly due to the incompleteness of medical data that hinders EWS effectiveness. There is a need to continue developing and testing criteria for early identification of maternal sepsis. Funding: UNDP-UNFPA-UNICEF-WHO-World Bank Special Programme of Research, Development and Research Training in Human Reproduction (HRP), WHO, Merck for Mothers, U.S. Agency for International Development, Wellcome Trust, and National Institute for Health and Care Research.
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