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

UMMS Affiliation

Meyers Primary Care Institute; Department of Medicine, Division of Geriatric Medicine

Publication Date


Document Type



Clinical Epidemiology | Diagnosis | Epidemiology | Female Urogenital Diseases and Pregnancy Complications | Neoplasms | Oncology | Therapeutics | Translational Medical Research | Women's Health


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.


UMCCTS funding, ovarian cancer, prognosis, survival

DOI of Published Version



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

Journal/Book/Conference Title

Cancer epidemiology, biomarkers and prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology

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Link to Article in PubMed

PubMed ID