Evidence map›Paper›PMID 42569246›Full record

ArticlePregnancy (Hoboken, N.J.)2026

Congenital heart disease in pregnancy and severe maternal morbidity: a distributed data network study.

Elizabeth B Sherwin, Benjamin Martin, Xiao Xu, Anna Booman, Alyssa Howren, Kimberlee McKay, Sadaf Kazi, Sean O'Reilly, Katie S Kolla, Khyzer Aziz and 2 more

Abstract read
In one paragraph

Article in Pregnancy (Hoboken, N.J.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

12 authors.

Elizabeth B SherwinDivision of Maternal-Fetal Medicine and Obstetrics, Department of Obstetrics and Gynecology, Stanford University School of Medicine, Stanford, CA, USA.ORCID 0000-0002-7513-3823
Benjamin MartinJohns Hopkins University School of Medicine, Baltimore, MD, USA.ORCID 0000-0001-6510-6824
Xiao XuDepartment of Obstetrics and Gynecology, Columbia University Irving Medical Center, New York, NY, USA.ORCID 0000-0001-6519-1731
Anna BoomanDepartment of Anesthesiology, Perioperative, and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA.ORCID 0000-0002-8112-6929
Alyssa HowrenDepartment of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, CA, USA.ORCID 0000-0002-9901-6947
Kimberlee McKayAvera Research Institute, Sioux Falls, SD, USA.
Sadaf KaziNational Center for Human Factors in Healthcare, MedStar Health Research Institute, Washington, DC, USA.ORCID 0000-0002-7189-6989
Sean O'ReillyJohns Hopkins University School of Medicine, Baltimore, MD, USA.
Katie S KollaDepartment of Cardiothoracic Surgery, Stanford Children's Health, Stanford, CA, USA.
Khyzer AzizJohns Hopkins University School of Medicine, Baltimore, MD, USA.
Katherine BiancoDivision of Maternal-Fetal Medicine and Obstetrics, Department of Obstetrics and Gynecology, Stanford University School of Medicine, Stanford, CA, USA.
Stephanie A LeonardDivision of Maternal-Fetal Medicine and Obstetrics, Department of Obstetrics and Gynecology, Stanford University School of Medicine, Stanford, CA, USA.ORCID 0000-0001-8213-1319

Funding

Policy solutions for addressing structural racism in maternal health disparitiesU54HD113172 · NICHD · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI GUOHUA LI · 2023 to 2026
$11.6M
Stanford PRIHSM: Promoting Improvement in Hemorrhage-related Severe Maternal MorbidityU54HD113142 · NICHD · STANFORD UNIVERSITY · PI Elliott K Main · 2023 to 2026
$11.0M
Maternal Health Data Innovation and Coordination HubU24HD113136 · NICHD · UNIVERSITY OF MARYLAND BALTIMORE · PI Andreea Alina Creanga · 2023 to 2026
$9.3M
Training ComponentU54HD113179 · NICHD · AVERA MCKENNAN · PI Amy J Elliott, Lacey A McCormack · 2023 to 2026
$8.6M
Optimizing treatment of chronic hypertension in pregnancy using a national distributed data networkK01HL171699 · NHLBI · STANFORD UNIVERSITY · PI Stephanie A. Leonard · 2024 to 2026
$514k
NHLBI NIH HHS K01 HL171699NICHD NIH HHS U24 HD113136NICHD NIH HHS U54 HD113142NICHD NIH HHS U54 HD113172NICHD NIH HHS U54 HD113179
6 · The paper itself

Abstract

Introduction: Pregnant people with congenital heart disease (CHD) are a growing patient population in obstetrics, yet evidence on the risk for severe maternal morbidity (SMM) has largely been limited to studies that lack specificity for CHD. We conducted this study to demonstrate the utility of distributed data networks for obstetric research and to characterize pregnant patients with CHD and their risk of SMM. Methods: The study included female patients aged 12-55 years who delivered between 2015 and 2026 at three U.S. academic healthcare organizations. We used a distributed data network study design by developing analytical code, executing the code locally at each organization using its electronic health record (EHR) data in a common data model (CDM), then aggregating the results. This analytical approach is rigorous, reproducible, and protects patient confidentiality by not sharing patient-level data. The Observational Medical Outcomes Partnership (OMOP) CDM was used by each organization, and we used OMOP standard concepts to identify CHD and SMM. We categorized outcomes as SMM, non-transfusion SMM, and cardiac SMM based on the CDC SMM index. We analyzed the incidence of the outcomes and estimated risk ratios (RRs) and Wald 95% confidence intervals (CIs) using contingency tables. Results: The prevalence of CHD in pregnancy was 2.20% among 166,033 deliveries across three healthcare organizations. 19.30% of people with CHD had an acquired heart condition, compared to 2.14% of people without CHD. SMM occurred in 9.43% of deliveries to people with CHD compared to 4.53% among people without CHD (RR: 2.08; 95% CI: 1.88, 2.31), and non-transfusion SMM occurred in 6.72% vs 2.55% of deliveries (RR: 2.63; 95% CI: 2.33, 2.98), respectively. Cardiac SMM represented 51.84% of non-transfusion SMM among people with CHD compared to 16.18% among people without CHD. Conclusions: Our distributed data network study using EHR data from three geographically diverse U.S. healthcare organizations found that 1 in 15 people with CHD had non-transfusion SMM compared with 1 in 39 people without CHD. This study demonstrates the feasibility and utility of distributed data networks for obstetric research and provides novel evidence on maternal outcomes for pregnant patients with CHD.

Indexed as

common data elementscongenitaldata analyticselectronic health recordsheart defectsheart diseasespregnancysevere maternal morbidity

Identifiers

PMID42569246
PMCPMC13449514

What Socratic 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 Socratic graph.