Evidence map›Paper›PMID 38405849›Full record

ArticlemedRxiv : the preprint server for health sciences2024

Discover overlooked complications after preeclampsia from three real-world medical record datasets of over 100,000 pregnancies.

Haoming Zhu, Xiaotong Yang, Leyang Tao, Wanling Xie, Jui-Hsuan Chang, Zhiping Paul Wang, Elizabeth Langen, Ruowang Li, Lana X Gamire

Abstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2024. 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

9 authors.

Haoming ZhuDepartment of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, 48105, United States of America.
Xiaotong YangDepartment of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, 48105, United States of America.
Leyang TaoDepartment of Biomedical Engineering, University of Michigan, Ann Arbor, MI, 48105, United States of America.
Wanling XieDepartment of Computational Biomedicine, Cedars-Sinai Medical Center, West Hollywood, CA, 90069, United States of America.
Jui-Hsuan ChangDepartment of Computational Biomedicine, Cedars-Sinai Medical Center, West Hollywood, CA, 90069, United States of America.
Zhiping Paul WangDepartment of Computational Biomedicine, Cedars-Sinai Medical Center, West Hollywood, CA, 90069, United States of America.
Elizabeth LangenDepartment of Obstetrics and Gynecology, University of Michigan, Ann Arbor, MI, 48105, United States of America.
Ruowang LiDepartment of Computational Biomedicine, Cedars-Sinai Medical Center, West Hollywood, CA, 90069, United States of America.
Lana X GamireDepartment of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, 48105, United States of America.

Funding

An Integrative Omics Approach to Identify Biomarkers Related to Preeclampsia and Breast Cancer RisksR01HD084633 · NICHD · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI GARMIRE, LANA X · 2016 to 2020
$3.0M
Biomedical Informatics and Data Science Training Program (BIDS-TP)T32GM141746 · NIGMS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Ivo D Dinov, RYAN E MILLS · 2021 to 2026
$2.6M
Cancer precision medicine through spatially informative single cell image and transcriptomics data analysisR01LM012373 · NLM · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI GARMIRE, LANA X · 2016 to 2024
$2.4M
DR. EPS: Drug Repurposing for Extended Patient SurvivalR01LM012907 · NLM · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI GARMIRE, LANA X · 2019 to 2022
$1.0M
NICHD NIH HHS R01 HD084633NIGMS NIH HHS T32 GM141746NLM NIH HHS R01 LM012373NLM NIH HHS R01 LM012907
6 · The paper itself

Abstract

Importance: Preeclampsia poses a significant threat to women's long-term health. However, what diseases are affected and at what level they are affected by PE needs a thorough investigation. Objective: To conduct the first large-scale, non-hypothesis-driven study using EHR data from multiple medical centers to comprehensively explore adverse health outcomes after preeclampsia. Design: Retrospective multi-cohort case-control study. Participants: We analyzed 3,592 preeclampsia patients and 23,040 non-preeclampsia controls from the University of Michigan Healthcare System. We externally validated the findings using UK Biobank data (443 cases, 14,870 controls) and Cedar Sinai data(2755 cases, 60,305 controls). Main outcomes: We showed that six complications are significantly affected by PE. We demonstrate the effect of race as well as preeclampsia severity on these complications. Results: PE significantly increases the risk of later hypertension, uncomplicated and complicated diabetes, renal failure and obesity, after careful confounder adjustment. We also identified that hypothyroidism risks are significantly reduced in PE patients, particularly among African Americans. Severe PE affects hypertension, renal failure, uncomplicated diabetes and obesity more than mild PE, as expected. Caucasians are affected more negatively than African Americans by PE on future hypertension, uncomplicated and complicated diabetes and obesity. Conclusion: This study fills a gap in the comprehensive assessment of preeclampsia's long-term effects using large-scale EHR data and rigorous statistical methods. Our findings emphasize the need for extended monitoring and tailored interventions for women with a history of preeclampsia, by considering pre-existing conditions, preeclampsia severity, and racial differences.

Identifiers

PMID38405849
PMCPMC10888996

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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.