Evidence map›Paper›PMID 39720608›Full record

ArticleEClinicalMedicine2025

Early warning systems for identifying severe maternal outcomes: findings from the WHO global maternal sepsis study.

Yamikani Chimwaza, Alexandra Hunt, Livia Oliveira-Ciabati, Laura Bonnett, Edgardo Abalos, Cristina Cuesta, João Paulo Souza, Mercedes Bonet, Vanessa Brizuela, David Lissauer and 1 more

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
  4. Article
  5. Review
  6. 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 · 2025
    Article
  7. Article
  8. 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

11 authors.

Yamikani ChimwazaMalawi-Liverpool Wellcome Programme, Blantyre, Malawi.
Alexandra HuntDepartment of Health Data Science, University of Liverpool, Liverpool, United Kingdom.
Livia Oliveira-CiabatiHealth Innovation Techcenter (HIT), Hospital Israelita Albert Einstein, São Paulo, Brazil.
Laura BonnettDepartment of Health Data Science, University of Liverpool, Liverpool, United Kingdom.
Edgardo AbalosCentro Rosarino de Estudios Perinatales (CREP), Rosario, Argentina.
Cristina CuestaCentro Rosarino de Estudios Perinatales (CREP), Rosario, Argentina.
João Paulo SouzaDepartment of Social Medicine, Ribeirão Preto Medical School, University of Sao Paulo, Ribeirão Preto, Brazil.
Mercedes BonetUNDP-UNFPA-UNICEF-WHO-World Bank Special Programme of Research, Development and Research Training in Human Reproduction (HRP), Department of Sexual and Reproductive Health and Research, World Health Organization, Geneva, Switzerland.
Vanessa BrizuelaUNDP-UNFPA-UNICEF-WHO-World Bank Special Programme of Research, Development and Research Training in Human Reproduction (HRP), Department of Sexual and Reproductive Health and Research, World Health Organization, Geneva, Switzerland.
David LissauerMalawi-Liverpool Wellcome Programme, Blantyre, Malawi.
World Health Organization Global Maternal Sepsis Study Research Group

Funding

Wellcome TrustWorld Health Organization 001
6 · The paper itself

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.

Indexed as

Early identificationEarly warning systemsMaternal sepsisSepsisSevere maternal outcome

Identifiers

PMID39720608
PMCPMC11667637

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LicenceCC BY
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Registered trials

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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.