• Login
    View Item 
    •   Home
    • UMass Chan Departments, Programs and Centers
    • UMass Center for Clinical and Translational Science
    • UMass Center for Clinical and Translational Science Supported Publications
    • View Item
    •   Home
    • UMass Chan Departments, Programs and Centers
    • UMass Center for Clinical and Translational Science
    • UMass Center for Clinical and Translational Science Supported Publications
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of eScholarship@UMassChanCommunitiesPublication DateAuthorsUMass Chan AffiliationsTitlesDocument TypesKeywordsThis CollectionPublication DateAuthorsUMass Chan AffiliationsTitlesDocument TypesKeywords

    My Account

    LoginRegister

    Help

    AboutSubmission GuidelinesData Deposit PolicySearchingAccessibilityTerms of UseWebsite Migration FAQ

    Statistics

    Most Popular ItemsStatistics by CountryMost Popular Authors

    Predictors of Long-Term Survival among High-Grade Serous Ovarian Cancer Patients

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Authors
    Clarke, Christina L.
    Kushi, Lawrence H.
    Chubak, Jessica
    Pawloski, Pamala A.
    Bulkley, Joanna E.
    Epstein, Mara M.
    Burnett-Hartman, Andrea N.
    Powell, Bethan
    Pearce, Celeste L.
    Spencer Feigelson, Heather
    UMass Chan Affiliations
    Department of Medicine, Division of Geriatric Medicine
    Meyers Primary Care Institute
    Document Type
    Journal Article
    Publication Date
    2019-05-01
    Keywords
    UMCCTS funding
    ovarian cancer
    prognosis
    survival
    Clinical Epidemiology
    Diagnosis
    Epidemiology
    Female Urogenital Diseases and Pregnancy Complications
    Neoplasms
    Oncology
    Therapeutics
    Translational Medical Research
    Women's Health
    Show allShow less
    
    Metadata
    Show full item record
    Link to Full Text
    https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6500478/
    Abstract
    BACKGROUND: Relatively little is known about factors associated with long-term survival (LTS) following a diagnosis of ovarian cancer. METHODS: We conducted a retrospective study of high-grade serous ovarian cancer (HGSOC) to explore predictors of LTS (defined as > /=7 years of survival) using electronic medical record data from a network of integrated health care systems. Multivariable logistic regression with forward selection was used to compare characteristics of women who survived > /=7 years after diagnosis (n = 148) to those who died within 7 years of diagnosis (n = 494). RESULTS: Our final model included study site, age, stage at diagnosis, CA-125, comorbidity score, receipt of chemotherapy, BMI, and four separate comorbid conditions: weight loss, depression, hypothyroidism, and liver disease. Of these, only younger age, lower stage, and depression were statistically significantly associated with LTS. CONCLUSIONS: We did not identify any new characteristics associated with HGSOC survival. IMPACT: Prognosis of ovarian cancer generally remains poor. Large, pooled studies of ovarian cancer are needed to identify characteristics that may improve survival.
    Source

    Cancer Epidemiol Biomarkers Prev. 2019 May;28(5):996-999. doi: 10.1158/1055-9965.EPI-18-1324. Epub 2019 Apr 9. Link to article on publisher's site

    DOI
    10.1158/1055-9965.EPI-18-1324
    Permanent Link to this Item
    http://hdl.handle.net/20.500.14038/50373
    PubMed ID
    30967418
    Related Resources

    Link to Article in PubMed

    ae974a485f413a2113503eed53cd6c53
    10.1158/1055-9965.EPI-18-1324
    Scopus Count
    Collections
    UMass Center for Clinical and Translational Science Supported Publications

    entitlement

    DSpace software (copyright © 2002 - 2023)  DuraSpace
    Lamar Soutter Library, UMass Chan Medical School | 55 Lake Avenue North | Worcester, MA 01655 USA
    Quick Guide | escholarship@umassmed.edu
    Open Repository is a service operated by 
    Atmire NV
     

    Export search results

    The export option will allow you to export the current search results of the entered query to a file. Different formats are available for download. To export the items, click on the button corresponding with the preferred download format.

    By default, clicking on the export buttons will result in a download of the allowed maximum amount of items.

    To select a subset of the search results, click "Selective Export" button and make a selection of the items you want to export. The amount of items that can be exported at once is similarly restricted as the full export.

    After making a selection, click one of the export format buttons. The amount of items that will be exported is indicated in the bubble next to export format.