Evidence map›Paper›PMID 37295633›Full record

ArticleAmerican journal of obstetrics and gynecology2023

Maternal morbidity and mortality among patients with cancer at time of delivery.

Alexandra C Sundermann, Jennifer M Cate, Alexa K Campbell, Sarah K Dotters-Katz, Evan R Myers, Jerome J Federspiel

Open access · bronzeAbstract read
In one paragraph

Article in American journal of obstetrics and gynecology, 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
0cells of the map it votes in
2citing papers in PubMed
0.9field-weighted citation impact, top 21% 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

2 citing papers in PubMed, 4 citations in OpenAlex.

  1. Article
  2. 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

6 authors at 2 institutions in 1 country.

Alexandra C SundermannDepartment of Obstetrics and Gynecology, Duke University Medical Center, Durham, NC. Electronic address: Alex.Sundermrann@duke.edu.
Jennifer M CateDepartment of Obstetrics and Gynecology, Duke University Medical Center, Durham, NC.
Alexa K CampbellDepartment of Obstetrics and Gynecology, Duke University Medical Center, Durham, NC.
Sarah K Dotters-KatzDepartment of Obstetrics and Gynecology, Duke University Medical Center, Durham, NC.
Evan R MyersDepartment of Obstetrics and Gynecology, Duke University Medical Center, Durham, NC.
Jerome J FederspielDepartment of Obstetrics and Gynecology, Duke University Medical Center, Durham, NC.
Duke Medical Center · USDuke University · US

Funding

Support for QA/QC for Prior Approval ProcessUL1TR002553 · NCATS · DUKE UNIVERSITY · PI LI, JENNIFER S, MCNAMARA, JAMES O. · 2018 to 2023
$58.5M
Duke Women's Reproductive Health Research ScholarsK12HD103083 · NICHD · DUKE UNIVERSITY · PI Cindy Amundsen · 2020 to 2026
$2.2M
NCATS NIH HHS UL1 TR002553NICHD NIH HHS K12 HD103083
6 · The paper itself

Abstract

backgroundIndividuals with cancer during pregnancy are a medically complex patient population that is anticipated to grow. A better understanding of this population and patterns of risk at time of delivery would offer an opportunity for providers to mitigate maternal morbidity.

objectiveThis study aimed to estimate the prevalence in the United States of concurrent cancer diagnoses at time of delivery by cancer type and associated maternal morbidity and mortality. STUDY

designUsing the National Inpatient Sample, we identified delivery-associated hospitalizations between 2007 and 2018. Concurrent cancer diagnoses were classified using the Clinical Classifications Software. Main outcomes included severe maternal morbidity, as defined by the Centers for Disease Control and Prevention indicators, and mortality during delivery hospitalization. We calculated adjusted rates for cancer diagnosis at time of delivery and adjusted odds ratios of severe maternal morbidity and maternal death during hospitalization using survey-weighted multivariable logistic regression models.

resultsIn this sample of 9,418,761 delivery-associated hospitalizations, 63 per 100,000 deliveries had a concurrent cancer diagnosis (95% confidence interval, 60-66; national weighted estimate, 46,654,042). The most common cancer types were breast cancer (8.4 per 100,000 deliveries), leukemia (8.4 per 100,000 deliveries), Hodgkin lymphoma (7.4 per 100,000 deliveries), non-Hodgkin lymphoma (5.4 per 100,000 deliveries), and thyroid cancer (4.0 per 100,000 deliveries). Patients with cancer were at significantly higher risk for any severe maternal morbidity (adjusted odds ratio, 5.25; 95% confidence interval, 4.73-5.83) and maternal death (adjusted odds ratio, 67.5; 95% confidence interval, 45.1-101.4). Risks of hysterectomy (adjusted odds ratio, 16.92; 95% confidence interval, 13.96-20.52), acute respiratory distress (adjusted odds ratio, 12.76; 95% confidence interval, 9.92-16.42), sepsis (adjusted odds ratio, 11.91; 95% confidence interval, 8.68-16.32), and embolism (adjusted odds ratio, 11.12; 95% confidence interval, 6.94-17.82) were particularly heightened among patients with cancer. Patients with leukemia, specifically, had the highest risk of adverse maternal outcomes (adjusted rate, 113 per 1000 deliveries; 95% confidence interval, 91-135 per 1000) when evaluating risk by cancer type.

conclusionPatients with cancer are at markedly increased risk of maternal morbidity and all-cause mortality during delivery-associated hospitalization. Risk is distributed unevenly within this population, with certain cancer types carrying unique risks for specific morbidity events.

Indexed as

LeukemiaMaternal DeathNeoplasmsFemaleHospitalizationHumansMaternal MortalityMorbidityPregnancyUnited Statescancerepidemiologyleukemiamaternal morbiditymaternal mortalitypregnancy

Identifiers

PMID37295633
PMCPMC10593119
OpenAlexW4379746405

What OpenQuestion holds

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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.