Evidence map›Paper›PMID 40398687›Full record

SynthesisJournal of clinical epidemiology2025

Maternal early warning scores shown to be methodologically weak and at high risk of bias.

Mae Chester-Jones, Shona Kirtley, Fema Er, Vidoushee Jogarah, Yu Qiao, Ruth Tunn, Naomi Vides, Peter J Watkinson, Marian Knight, Stephen Gerry and 1 more

Abstract readSystematic Review
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in Journal of clinical epidemiology, 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. Review
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.

Mae Chester-JonesCentre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre, University of Oxford, Oxford, UK. Electronic address: mae.chester-jones@ndorms.ox.ac.uk.
Shona KirtleyCentre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre, University of Oxford, Oxford, UK.
Fema ErCentre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre, University of Oxford, Oxford, UK.
Vidoushee JogarahCentre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre, University of Oxford, Oxford, UK.
Yu QiaoCentre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre, University of Oxford, Oxford, UK.
Ruth TunnCentre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre, University of Oxford, Oxford, UK; National Perinatal Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK.
Naomi VidesCentre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre, University of Oxford, Oxford, UK.
Peter J WatkinsonKadoorie Centre for Critical Care Research and Education, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford University Hospital NHS Trust, NIHR Biomedical Research Centre Oxford, Oxford, UK.
Marian KnightNational Perinatal Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK.
Stephen GerryCentre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre, University of Oxford, Oxford, UK.
Gary S CollinsCentre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre, University of Oxford, Oxford, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesTo systematically review and critically appraise the methodology of developing modified obstetric early warning scores (MOEWSs). STUDY DESIGN AND

settingWe searched Medline, CINAHL, EMBASE, and the Web of Science for MOEWS studies published between January 1, 2000, and December 31, 2022. Eligible studies included models predicting maternal death, intensive care unit (ICU) admission, and/or a composite of two or more maternal morbidities occurring in a hospital setting in women of any gestational age and up to 1 week after the end of pregnancy. Models were critically appraised using the Prediction Model Risk of Bias Assessment Tool (PROBAST) and adherence to the transparent reporting of prediction models (TRIPOD).

results20 studies were included: five (25%) were model development studies, five (25%) were model development and validation, and ten (50%) were validation only. Four development studies used statistical methods, and the remaining six studies used clinical consensus (ie, expert opinion). The four data-driven model development studies did not address key statistical challenges, such as repeated measures or missing data, nor did they assess the performance adequately or dataset characteristics clearly. All but one study (95%) were rated at high risk of bias due to data sources, poor reporting, and analysis limitations. The fifteen validation studies were poorly reported and eleven (73%) were at high risk of bias. None of the data-driven models were independently validated, a key step toward implementation.

conclusionThere is a lack of MOEWSs developed using methods that follow recommended statistical guidelines. Substantial problems with the methodological quality of included development and validation studies, along with high risk of bias,indicating published scores could perform poorly or be potentially harmful if used in clinical practice. Future work should address handling missing data and repeated measures and consider how an MOEWS will perform in different populations and key subgroups.

Indexed as

Early Warning ScoreMaternal MortalityPregnancy ComplicationsBiasFemaleHumansPregnancyClinical prediction modelMaternal early warning scoreMaternal morbidityMaternal mortalityMissing dataObstetricsPregnancyPrognosis studySystematic review

Identifiers

PMID40398687
PMCPMC7618941

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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.