Evidence map›Paper›PMID 33831933›Full record

ArticleObstetrics and gynecology2021

Improved Recognition of Maternal Deaths Using Modern Data Analytics.

Stefanie J Hollenbach, Kathryn Drennan, Eva K Pressman, Lisa Gray

Abstract read
PubMed Publisher
In one paragraph

Article in Obstetrics and gynecology, 2021. 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
0.7field-weighted citation impact, top 29% of its field
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, 2 citations in OpenAlex.

  1. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors at 1 institution in 1 country.

Stefanie J HollenbachDivision of Maternal Fetal Medicine, Department of Obstetrics and Gynecology, University of Rochester Medical Center, Rochester, New York.
Kathryn Drennan
Eva K Pressman
Lisa Gray
University of Rochester Medical Center · US

Funding

The University of Rochester's Clinical and Translational Science InstituteUL1TR002001 · NCATS · UNIVERSITY OF ROCHESTER · PI WILSON, KAREN M., ZAND, MARTIN S · 2016 to 2024
$34.6M
NCATS NIH HHS UL1 TR002001
6 · The paper itself

Abstract

objectiveTo use a data-fusion approach to improve ascertainment of maternal deaths not detected with standard surveillance strategies.

methodsWe conducted a retrospective cohort study from the electronic health records of a tertiary medical center from 2011 to 2018. Cases of maternal death were identified in two ways: 1) using a standard medical informatics service query of hospital data and 2) using the TriNetX discovery tool as patients with a vital status of "deceased" and evidence of antecedent pregnancy exposure based on such factors as obstetric diagnostic codes or obstetric-related procedures. Potential cases of maternal death identified by the latter method underwent chart review to confirm timing of death compared with timing of last appreciable pregnancy, and to characterize the details of these deaths. The primary outcome was pregnancy-associated mortality during pregnancy or within the first postpartum year in the discovery cohort compared with the hospital-identified cohort. Secondary outcomes included causes of death and comorbidities.

resultsDuring the study period, the standard service query identified 23 maternal deaths. The discovery tool identified 18 additional patients confirmed on subsequent chart review to represent pregnancy-associated deaths, a 78% increase in ascertainment of which a greater proportion represented postpartum deaths. The majority (61%) of newly ascertained mortalities were related to cardiac causes or other medical comorbidities. Although many hospital-ascertained cases were associated with deaths after delivery of a living newborn, more deaths after early pregnancy loss or termination were identified through the discovery tool.

conclusionImproved recognition of pregnancy-associated deaths can be achieved with modern data analytics.

Indexed as

AdolescentAdultCohort StudiesElectronic Health RecordsFemaleHumansMaternal MortalityNew YorkPopulation SurveillancePregnancyPregnancy ComplicationsRetrospective StudiesYoung Adult

Identifiers

PMID33831933
OpenAlexW3150054531

What OpenQuestion holds

Textmetadata
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Registered trials

None linked

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.