Evidence map›Paper›PMID 37088841›Full record

ArticleCJEM2023

Application of the Modified Early Obstetrical Warning System (MEOWS) in postpartum patients in the emergency department.

Jeeventh Kaur, Cameron Thompson, Shelley McLeod, Catherine Varner

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Article in CJEM, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
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2citing papers in PubMed
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1 · What the graph read from it

What it found

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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

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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

2 citing papers in PubMed.

  1. Article
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4 · The record

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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

4 authors.

Jeeventh KaurSchwartz/Reisman Emergency Medicine Institute, Toronto, ON, Canada.
Cameron ThompsonSchwartz/Reisman Emergency Medicine Institute, Toronto, ON, Canada.
Shelley McLeodSchwartz/Reisman Emergency Medicine Institute, Toronto, ON, Canada.
Catherine VarnerSchwartz/Reisman Emergency Medicine Institute, Toronto, ON, Canada. catherine.varner@sinaihealth.ca.ORCID 0000-0002-9696-1329

Funding

Department of Family and Community Medicine, University of Toronto Clinician Investigator Award
6 · The paper itself

Abstract

introductionOver the last two decades, there has been a steady rise in severe maternal morbidity and pregnancy-related deaths in Canada and the USA. The Modified Early Obstetric Warning System (MEOWS) is a risk stratification tool designed to predict severe maternal morbidity and mortality and has been validated for use in obstetrical wards. The objective of this study was to determine if MEOWS could accurately identify patients at risk of severe maternal morbidity and mortality in the ED setting.

methodsThis was a chart review of patients presenting to an academic tertiary care centre (annual ED census 65,000) with a postpartum complaint within 6 weeks of delivery between May 2020 and March 2022. The exposure was the presence of a trigger, defined as one severely abnormal (red) or two mildly abnormal (yellow) physiological measurements during the ED visit. The diagnostic accuracy of the tool to identify patients at risk of severe maternal morbidity severe maternal morbidity or mortality was estimated by calculating the sensitivity, specificity, positive predictive value and negative predictive value.

resultsTwo hundred and sixty-seven patients were included, of which 21 (7.9%) met the criteria for severe maternal morbidity. There were no maternal deaths. Overall, the sensitivity of the MEOWS tool was 85.7% (95% CI 63.7-97.0%), specificity was 67.9% (95% CI 61.7-73.7%), positive predictive value was 18.6% (95% CI 15.1-22.7%), and negative predictive value was 98.2% (95% CI 95.1-99.4%). Severe preeclampsia and eclampsia were the most common outcome category of severe maternal morbidity, representing 16 (76.2%) cases. Most patients (86.5%) were discharged directly from the ED, and 90.0% did not return within 30 days.

conclusionsThis study is the first to explore the utility of MEOWS in postpartum ED patients. The presence of a MEOWS trigger at ED triage or the first ED nursing assessment was associated with severe maternal morbidity. Thus, MEOWS may be a valuable tool to identify postpartum patients at risk of severe outcomes early in an ED visit.

Indexed as

Emergency Service, HospitalPostpartum PeriodCanadaFemaleHumansPredictive Value of TestsPregnancyEarly warning scoring systemsMaternal morbidityObstetrical emergenciesPostpartumPreeclampsia

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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.