Evidence map›Paper›PMID 39990556›Full record

ArticlemedRxiv : the preprint server for health sciences2025

Comorbidity analysis and clustering of endometriosis patients using electronic health records.

Umair Khan, Tomiko T Oskotsky, Bahar D Yilmaz, Jacquelyn Roger, Ketrin Gjoni, Juan C Irwin, Jessica Opoku-Anane, Noémie Elhadad, Linda C Giudice, Marina Sirota

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2025. 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

5 · Who and what money

Authors and funding

10 authors.

Umair KhanBakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA.ORCID 0000-0002-6361-4996
Tomiko T OskotskyBakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA.ORCID 0000-0001-7393-5120
Bahar D YilmazDepartment of Obstetrics, Gynecology, and Reproductive Sciences, Center for Reproductive Sciences, University of California, San Francisco, San Francisco, CA.
Jacquelyn RogerBiological and Medical Informatics Graduate Program, University of California, San Francisco, San Francisco, CA.ORCID 0000-0003-1823-0421
Ketrin GjoniPharmaceutical Sciences and Pharmacogenomics Graduate Program, University of California, San Francisco, San Francisco, CA.ORCID 0000-0001-5833-1089
Juan C IrwinDepartment of Obstetrics, Gynecology, and Reproductive Sciences, Center for Reproductive Sciences, University of California, San Francisco, San Francisco, CA.
Jessica Opoku-AnaneRobert Wood Johnson Medical School, Rutgers University, New Brunswick, NJ.ORCID 0000-0003-2252-1571
Noémie ElhadadDepartment of Biomedical Informatics, Columbia University, New York, NY.ORCID 0000-0001-9721-5240
Linda C GiudiceDepartment of Obstetrics, Gynecology, and Reproductive Sciences, Center for Reproductive Sciences, University of California, San Francisco, San Francisco, CA.ORCID 0000-0002-1677-0822
Marina SirotaBakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA.ORCID 0000-0002-7246-6083

Funding

UCSF Stanford Endometriosis Center for Discovery, Innovation, Training and Community EngagementP01HD106414 · NICHD · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI GAUDILLIERE, BRICE, GIUDICE, LINDA C · 2021 to 2025
$7.1M
BMI Bioinformatics Training GrantsT32GM067547 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI HERNANDEZ, RYAN D. · 2003 to 2022
$6.6M
REPRODUCTIVE ENDOCRINOLOGYT32HD007263 · NICHD · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI MELLON, SYNTHIA H · 1985 to 2023
$4.1M
Pharmaceutical Sciences and PharmacogenomicsT32GM142516 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI Su Guo · 2022 to 2026
$2.4M
NICHD NIH HHS P01 HD106414NICHD NIH HHS T32 HD007263NIGMS NIH HHS T32 GM067547NIGMS NIH HHS T32 GM142516
6 · The paper itself

Abstract

Endometriosis is a prevalent, complex, inflammatory condition associated with a diverse range of symptoms and comorbidities. Despite its substantial burden on patients, population-level studies that explore its comorbid patterns and heterogeneity are limited. In this retrospective case-control study, we analyzed comorbidities from over forty thousand endometriosis patients across six University of California medical centers using de-identified electronic health record (EHR) data. We found hundreds of conditions significantly associated with endometriosis, including genitourinary disorders, neoplasms, and autoimmune diseases, with strong replication across datasets. Clustering analyses identified patient subpopulations with distinct comorbidity patterns, including psychiatric and autoimmune conditions. This study provides a comprehensive analysis of endometriosis comorbidities and highlights the heterogeneity within the patient population. Our findings demonstrate the utility of EHR data in uncovering clinically meaningful patterns and suggest pathways for personalized disease management and future research on biological mechanisms underlying endometriosis.

Indexed as

comorbiditieselectronic health recordsendometriosisunsupervised clustering

Identifiers

PMID39990556
PMCPMC11844609

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.