Evidence map›Paper›PMID 37560021›Full record

ArticleJACC. Advances2023

Assessment and Prediction of Cardiovascular Contributions to Severe Maternal Morbidity.

Aarti Thakkar, Afshan B Hameed, Minhal Makshood, Brent Gudenkauf, Andreea A Creanga, Isabelle Malhamé, Sonia M Grandi, Sara A Thorne, Rohan D'Souza, Garima Sharma

Open access · diamondAbstract read
In one paragraph

Article in JACC. Advances, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
4.2field-weighted citation impact, top 5% 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

9 citing papers in PubMed, 19 citations in OpenAlex.

  1. Article
  2. Article
  3. Acute cardiovascular complications of pregnancy.Case reports in women's health · 2025
    Article
  4. Article
  5. Review
  6. Review
  7. Article
  8. Article
  9. 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

10 authors at 6 institutions in 2 countries.

Aarti ThakkarDivision of Cardiology, Department of Medicine, Ciccarone Center for the Prevention of Cardiovascular Disease, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
Afshan B HameedDepartment of Obstetrics & Gynecology, Department of Medicine, University of California-Irvine, Irvine, California, USA.
Minhal MakshoodDivision of Cardiology, Department of Medicine, Ciccarone Center for the Prevention of Cardiovascular Disease, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
Brent GudenkaufDivision of Cardiology, Department of Medicine, Ciccarone Center for the Prevention of Cardiovascular Disease, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
Andreea A CreangaDepartment of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.
Isabelle MalhaméDivision of General Internal Medicine, Department of Medicine, McGill University Health Centre, Montreal, Quebec, Canada.
Sonia M GrandiChild Health Evaluative Sciences Program, The Hospital for Sick Children, Toronto, Ontario, Canada.
Sara A ThorneDivision of Cardiology, Pregnancy & Heart Disease Program, Mount Sinai Hospital & University Health Network, University of Toronto, Toronto, Ontario, Canada.
Rohan D'SouzaDepartments of Obstetrics & Gynaecology and Health Research Methods, Evidence and Impact, McMaster University, Hamilton, Ontario, Canada.
Garima SharmaDivision of Cardiology, Department of Medicine, Ciccarone Center for the Prevention of Cardiovascular Disease, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
Johns Hopkins University · USImpact · CAMcGill University Health Centre · CAMount Sinai Hospital · CAUniversity of California, Irvine · USUniversity of Toronto · CA

Funding

Developing a refined comorbidity index for use in obstetric patientsR01HD112442 · NICHD · UNIVERSITY OF MARYLAND BALTIMORE · PI Andreea Alina Creanga · 2023 to 2026
$2.2M
Cardiovascular Disease in Pregnancy and the Postpartum Period in MarylandR03HD104888 · NICHD · JOHNS HOPKINS UNIVERSITY · PI CREANGA, ANDREEA ALINA · 2021 to 2022
$164k
NICHD NIH HHS R01 HD112442NICHD NIH HHS R03 HD104888
6 · The paper itself

Abstract

Severe maternal morbidity (SMM) refers to any unexpected outcome directly related to pregnancy and childbirth that results in both short-term delivery complications and long-term consequences to a women's health. This affects about 60,000 women annually in the United States. Cardiovascular contributions to SMM including cardiac arrest, arrhythmia, and acute myocardial infarction are on the rise, probably driven by changing demographics of the pregnant population including more women of extreme maternal age and an increased prevalence of cardiometabolic and structural heart disease. The utilization of SMM prediction tools and risk scores specific to cardiovascular disease in pregnancy has helped with risk stratification. Furthermore, health system data monitoring and reporting to identify and assess etiologies of cardiovascular complications has led to improvement in outcomes and greater standardization of care for mothers with cardiovascular disease. Improving cardiovascular disease-related SMM relies on a multipronged approach comprised of patient-level identification of risk factors, individualized review of SMM cases, and validation of risk stratification tools and system-wide improvements in quality of care. In this article, we review the epidemiology and cardiac causes of SMM, we provide a framework of risk prediction clinical tools, and we highlight need for organization of care to improve outcomes.

Indexed as

cardio-obstetricscardiovascular diseasesevere maternal morbidity

Identifiers

PMID37560021
PMCPMC10410605
OpenAlexW4353048102

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.