Evidence map›Paper›PMID 26855105›Full record

ArticleObstetrics and gynecology2016

Severe Maternal Morbidity and the Use of Assisted Reproductive Technology in Massachusetts.

Candice Belanoff, Eugene R Declercq, Hafsatou Diop, Daksha Gopal, Milton Kotelchuck, Barbara Luke, Thien Nguyen, Judy E Stern

Abstract read
In one paragraph

Article in Obstetrics and gynecology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.

0numbers the graph read from it
0cells of the map it votes in
29citing papers in PubMed
15.7field-weighted citation impact, top 1% 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

29 citing papers in PubMed, 58 citations in OpenAlex.

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  9. Severe Maternal Morbidity in Twins.American journal of perinatology · 2023
    Article
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  19. Infertility treatment and risk of severe maternal morbidity: a propensity score-matched cohort study.CMAJ : Canadian Medical Association journal = journal de l'Association medicale canadienne · 2019
    Article
  20. Observational
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

8 authors at 1 institution in 1 country.

Candice BelanoffDepartment of Community Health Sciences, Boston University School of Public Health, the Massachusetts Department of Public Health, and Massachusetts General Hospital, Center for Child & Adolescent Health Research and Policy, Harvard Medical School, Boston, Massachusetts; the Department of Obstetrics, Gynecology, and Reproductive Biology, Michigan State University, East Lansing, Michigan; and the Department of Obstetrics & Gynecology, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire.
Eugene R Declercq
Hafsatou Diop
Daksha Gopal
Milton Kotelchuck
Barbara Luke
Thien Nguyen
Judy E Stern
Massachusetts Department of Public Health · US

Funding

Women's Health After Assisted Reproductive Technology: A Population-Based StudyR01HD067270 · NICHD · DARTMOUTH-HITCHCOCK CLINIC · PI DIOP, HAFSATOU, STERN, JUDY E · 2011 to 2020
$7.4M
Child Health After Assisted Reproductive Technology: A Population-Based StudyR01HD064595 · NICHD · BOSTON UNIVERSITY MEDICAL CAMPUS · PI DECLERCQ, EUGENE R, DIOP, HAFSATOU · 2010 to 2014
$3.1M
NICHD NIH HHS R01 HD064595NICHD NIH HHS R01HD064595NICHD NIH HHS R01 HD067270NICHD NIH HHS R01HD067270
6 · The paper itself

Abstract

objectiveTo assess whether risk of severe maternal morbidity at delivery differed for women who conceived using assisted reproductive technology (ART), those with indicators of subfertility but no ART ("subfertile"), and those who had neither ART nor subfertility ("fertile").

methodsThis retrospective cohort study was part of the larger Massachusetts Outcomes Study of Assisted Reproductive Technology. To construct the Massachusetts Outcomes Study of Assisted Reproductive Technology database and identify ART deliveries, we linked ART treatment records to birth certificates and maternal and infant hospitalization records occurring in Massachusetts between 2004 and 2010. An algorithm of International Classification of Diseases, 9th Revision, Clinical Modification diagnosis and procedure codes identified severe maternal morbidity. We used logistic generalized estimating equations to estimate odds of severe maternal morbidity associated with fertility status, adjusting for maternal demographic and health factors and gestational age, stratifying on plurality and method of delivery.

resultsThe prevalence of severe maternal morbidity among this population (n=458,918) was 1.16%. The overall, crude prevalences of severe maternal morbidity among fertile, subfertile, and ART deliveries were 1.09%, 1.44%, and 3.14%, respectively. The most common indicator of severe maternal morbidity was blood transfusion. In multivariable analyses, among singletons, ART was associated with increased odds of severe maternal morbidity compared with both fertile (vaginal: adjusted odds ratio [OR] 2.27, 95% confidence interval [CI] 1.78-2.88; cesarean: adjusted OR 1.67, 95% CI 1.40-1.98, respectively) and subfertile (vaginal: adjusted OR 1.97, 95% CI 1.30-3.00; cesarean: adjusted OR 1.75, 95% CI 1.30-2.35, respectively) deliveries. Among twins, only cesarean ART deliveries had significantly greater severe maternal morbidity compared with cesarean fertile deliveries (adjusted OR 1.48, 95% CI 1.14-1.93).

conclusionWomen who conceive through ART may have elevated risk of severe maternal morbidity at delivery, largely indicated by blood transfusion, even when compared with a subfertile population. Further research should elucidate mechanisms underlying this risk.

Indexed as

AdultBlood TransfusionFemaleHumansMassachusettsObstetric Labor ComplicationsPregnancyReproductive Techniques, AssistedRetrospective Studies

Identifiers

PMID26855105
PMCPMC4764424
OpenAlexW2268275914

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

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.