Evidence map›Paper›PMID 40749680›Full record

ArticleCell reports. Medicine2025

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 readMulticenter Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Cell reports. Medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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, USA; Biological and Medical Informatics Graduate Program, University of California, San Francisco, San Francisco, CA, USA.
Tomiko T OskotskyBakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA; Division of Clinical Informatics and Digital Transformation, Department of Medicine, University of California, San Francisco, San Francisco, CA, USA.
Bahar D YilmazDepartment of Obstetrics, Gynecology, and Reproductive Sciences, Center for Reproductive Sciences, University of California, San Francisco, San Francisco, CA, USA.
Jacquelyn RogerBiological and Medical Informatics Graduate Program, University of California, San Francisco, San Francisco, CA, USA.
Ketrin GjoniPharmaceutical Sciences and Pharmacogenomics Graduate Program, University of California, San Francisco, San Francisco, CA, USA.
Juan C IrwinDepartment of Obstetrics, Gynecology, and Reproductive Sciences, Center for Reproductive Sciences, University of California, San Francisco, San Francisco, CA, USA.
Jessica Opoku-AnaneRobert Wood Johnson Medical School, Rutgers University, New Brunswick, NJ, USA.
Noémie ElhadadDepartment of Biomedical Informatics, Columbia University, New York, NY, USA.
Linda C GiudiceDepartment of Obstetrics, Gynecology, and Reproductive Sciences, Center for Reproductive Sciences, University of California, San Francisco, San Francisco, CA, USA.
Marina SirotaBakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA. Electronic address: marina.sirota@ucsf.edu.

Funding

Clinical and Translational Science InstituteUL1TR001872 · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2025 to 2025
$8.5M
UCSF Stanford Endometriosis Center for Discovery, Innovation, Training and Community EngagementP01HD106414 · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2025 to 2025
$1.4M
Pharmaceutical Sciences and PharmacogenomicsT32GM142516 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2022 to 2025
$966k
BMI Bioinformatics Training GrantT32GM067547 · UNIVERSITY OF CALIFORNIA SAN FRANCISCO · 2003 to 2005
$522k
NCATS NIH HHS UL1 TR001872NICHD NIH HHS P01 HD106414NIGMS 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 analyze comorbidities from over forty thousand endometriosis patients across six University of California medical centers using de-identified electronic health record (EHR) data. We find hundreds of conditions significantly associated with endometriosis, including genitourinary disorders, neoplasms, and autoimmune diseases, with strong replication across datasets. Clustering analyses identify 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

EndometriosisAcademic Medical CentersAdultCaliforniaCase-Control StudiesCluster AnalysisComorbidityElectronic Health RecordsFemaleHumansMaleMiddle Agedcase-controlcomorbiditieselectronic health recordsendometriosispropensity score matchingunsupervised clustering

Identifiers

PMID40749680
PMCPMC12432376

What Socratic holds

Textmetadata
LicenceCC BY
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.