The impact of comorbidity and guideline-concordant care on survival in ovarian cancer patients: a retrospective cohort study

The impact of comorbidity and guideline-concordant care on survival in ovarian cancer patients: a retrospective cohort study | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 5 March 2026 V1 Latest version Share on The impact of comorbidity and guideline-concordant care on survival in ovarian cancer patients: a retrospective cohort study Authors : Jennifer S. Ferris [email protected] , Joel Agarwal , Ling Chen , Yongmei Huang , Claudia L. Seguin , Andrew Eckel , Clare E. Wiberg , Alexi A. Wright , Mary Linton B. Peters , Amy B. Knudsen , Pari V. Pandharipande , and Jason Wright Authors Info & Affiliations https://doi.org/10.22541/au.177268161.19223350/v1 146 views 69 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Objective : To examine the impact of comorbidity on overall survival in ovarian cancer patients and determine whether guideline-concordant care (GCC) mitigates the adverse effects of comorbidity. Design : Retrospective cohort study. Setting : United States. Population : Ovarian cancer patients in the National Cancer Database (2004-2021). Methods : Patients with comorbidities were identified using the Charlson-Deyo Comorbidity index (0, 1, and ≥2) and a GCC metric was developed with patients classified as receiving concordant care if they received all eligible treatments, and discordant care otherwise. We generated Kaplan-Meier curves and Cox proportional hazards models to examine the association between comorbidity, GCC, and overall survival. Multiplicative and additive interactions were examined between comorbidity and GCC. Results : We identified 165,944 ovarian cancer patients. Overall survival decreased with increasing comorbidity score (p<0.001) and was worse for patients receiving discordant vs. concordant care. Patients who received discordant care had worse survival across comorbidity levels compared with those who received concordant care (p<0.001). We observed additive and multiplicative interactions between comorbidity and GCC. Patients with 1 and ≥2 comorbidities with discordant care had a 27% and 65% increased hazard of death (adjusted hazard ratio [aHR]=1.27, 95% CI: 1.23 – 1.32 and aHR=1.65, 95% CI: 1.59 – 1.72), respectively, while those with concordant care had an 11% and 27% increased hazard of death (aHR=1.11, 95% CI: 1.08 – 1.14 and aHR=1.27, 95% CI: 1.22 – 1.33), respectively. Conclusion : Ovarian cancer patients with pre-existing comorbidities have worse survival than patients without comorbidities. Increasing adherence to guideline-directed treatment for all patients may improve outcomes. Introduction In the U.S., ovarian cancer is the 6 th leading cause of cancer death in women, with an estimated 12,730 deaths in 2025. 1 The 5-year relative survival for women with ovarian cancer is 52%; however, in the absence of an effective population-based screening strategy, 2 the majority of patients (55%) are diagnosed with distant-stage disease when the 5-year relative survival is only 32%. 3 Therefore, it is critical to identify determinants of improved outcomes to guide advances in treatment and management. Ovarian cancer patients with medical comorbidities have worse survival, compared with those without. A recent meta-analysis found a 20% higher ovarian cancer-specific mortality rate among patients with any comorbidity compared to those with no comorbidities, and a 68% higher ovarian cancer-specific mortality among patients with the highest comorbidity levels compared with those with the lowest comorbidity level. 4 After accounting for patient age, medical comorbidity has also been associated with worse survival in ovarian cancer patients who received primary debulking surgery 5 and those treated with neoadjuvant chemotherapy. 6 The potential mechanisms underlying the association between pre-existing comorbidities and ovarian cancer survival are likely multifactorial, and may include delays in diagnosis or treatment initiation, differential allocation of treatment or response to treatments, and direct effects on cancer cellular processes. 7–10 Patients with ovarian cancer who have medical comorbidities are less likely to receive guideline-concordant care. 11 However, it remains unclear whether adherence to guideline-concordant care mitigates the adverse survival associated with medical comorbidities among patients with ovarian cancer. The objectives of our study were to examine the association between comorbidity and survival among patients with ovarian cancer and to determine whether receipt of guideline-concordant care attenuates the adverse effects of comorbidity on overall survival. Methods Data : We utilized the National Cancer Database (NCDB) for this analysis. 12 The NCDB is a hospital registry database which captures over 1,500 Commission on Cancer-accredited facilities in the U.S. each year. The NCDB is a joint venture between the American Cancer Society and the American College of Surgeons. As this study includes de-identified and publicly available secondary data, the Columbia University College of Physicians and Surgeons Institutional Review Board deemed this study to be non-human subjects research. The data are available after registration with the NCDB and can be obtained independently. Study Population : We identified patients with ovarian cancer diagnosed from 2004 to 2021. Patients were sequentially excluded if they had a history of another malignancy, non-invasive disease, non-epithelial histology, unknown staging, or received care at an unknown facility type or location. The final analytic cohort included patients with histologically confirmed invasive, epithelial ovarian cancer and complete clinical and facility information. Exposure : The main study exposure was the Charlson–Deyo Comorbidity Index which has been highly associated with cancer survival. 13 The Charlson-Deyo Comorbidity index was adapted from the original Charlson Comorbidity Index to better fit claims-based data. 14 This measure utilizes a weighted score derived from the sum of scores for each comorbid condition a patient has listed in the Charlson-Deyo comorbidity score mapping table. 12 Detailed information regarding the Charlson-Deyo comorbidity score mapping table and conditions are available on the NCDB participant user file for 2021. 12 The comorbidity score is summarized as 0, 1, and ≥2, where a score of 0 indicates no comorbid conditions. Outcome Measure : The primary outcome measure was overall survival, which was defined as time from the date of initial diagnosis of ovarian cancer to the date of death from any cause or date of last contact. Study Covariates : Patient and hospital characteristics included year of diagnosis (2004-2021), age at diagnosis (<40, 40-49, 50-59, 60-69, 70-79, ≥80 years), race/ethnicity (White, Black, Hispanic, Other/Unknown), insurance status (not insured, private insurance or managed care, Medicaid, Medicare, other/unknown), socioeconomic status (high, medium high, medium low, low, unknown), urban/rural status (metropolitan, urban, rural, unknown), facility type (community cancer program, comprehensive community cancer program, academic/research, integrated network cancer program), and facility location (Eastern, South, Midwest, and West). Tumor characteristic data included stage categorized using pathologic over clinical staging variables (I not otherwise specified, IA, IB, IC, IIA, IIB, II not otherwise specified, IIIA, IIIB, IIIC, IV), histology (serous, mucinous, endometrioid, clear cell, transitional cell, and epithelial not otherwise specified) and tumor grade (well-differentiated, moderately differentiated, poorly differentiated, unknown). 15,16 Primary treatment was categorized as none, primary surgery with adjuvant chemotherapy, primary surgery alone, neoadjuvant chemotherapy with interval surgery, neoadjuvant chemotherapy alone, and surgery and chemotherapy with sequence unknown. To measure guideline-concordant care, we relied on four primary treatment metrics based on National Comprehensive Cancer Network (NCCN) guidelines for treatment of ovarian cancer. 15,17 First, we examined performance of lymphadenectomy (metric 1) defined as examination of any nodal tissue in patients with stage IA–IIIB tumors (stage IA, IB, IC, I not otherwise specified, IIA, IIB/IIC, II not otherwise specified, IIIA, IIIB). Second, we examined performance of omentectomy or cytoreductive surgery (metric 2) in patients with stage IIA-IV ovarian cancer (IIA, IIB, IIC, IIIA, IIIB, IIIC, IV) who underwent primary cytoreductive surgery. Third, we examined administration of chemotherapy in patients with high-risk, early-stage ovarian cancer (metric 3) defined as patients with stage IA or IB grade 3 serous, mucinous, endometrioid or transitional cell histology tumors, any stage IC tumor or any stage I clear cell tumor. Finally, we examined receipt of chemotherapy for advanced-stage ovarian cancer (metric 4), including use of any chemotherapy in patients with stage IIB-IV ovarian cancer (IIB/IIC, IIIA, IIIB, IIIC, IV). A composite measure of guideline-concordant care was derived incorporating all four treatment metrics. Patients were classified as receiving guideline-concordant care (concordant care) if they received all eligible treatment metrics, and not receiving guideline-concordant care (discordant care) otherwise. For example, if a patient was eligible for three treatment metrics, but only received two, then they were classified as receiving discordant care. Patients with ambiguous data to determine eligibility (e.g., stage IA-IB with serous, mucinous, endometrioid or transitional cell histology but missing grade, stage I not otherwise specified and II not otherwise specified) or with missing data to determine treatment concordance (missing lymph node dissection status, missing surgical data on performance of omentectomy or cytoreduction, and missing chemotherapy status) were classified as unknown. Statistical Analysis : Baseline demographic characteristics including clinical characteristics and information about the treatment facilities where care was delivered were assessed across comorbidity levels (0, 1, ≥2). Categorical variables were compared using Chi-square tests. Survival was estimated using the Kaplan-Meier method across comorbidity and receipt of guideline-concordant care and compared using log-rank tests. Two- and 5-year survival estimates were reported. Cox proportional hazards models were fit, adjusting for age (Model 1) and sociodemographic (age, race, insurance, socioeconomic status, urban/rural) and clinical characteristics (stage, histology, grade, year of diagnosis, and treatment), as well as facility type and location (Model 2). We examined effect modification (interaction) between comorbidity and guideline-concordant care using an interaction term with the Model 2 covariates and guideline-concordant care (Model 3). We examined multiplicative interaction using the p-value of the interaction term and additive interaction using the relative excess risk due to interaction (RERI). The models censored patients at last known follow-up if vital status was not deceased, and the exposure compared comorbidity scores of 1 versus 0, and ≥2 versus 0. All statistical analyses were conducted using SAS version 9.4 (SAS Institute Inc). Statistical significance was determined using a 2-tailed hypothesis test with p<0.05. Results We identified a total of 165,944 patients with ovarian cancer who were diagnosed between 2004-2021 (Table 1). Most patients had a comorbidity score of 0 (80.3%), while fewer had a score of 1 or ≥2 (14.7% and 5.0%, respectively). Patients with ovarian cancer and a comorbidity score of ≥2 were more likely to be diagnosed in recent years, to be older, to be Black, to have Medicare insurance, to be diagnosed with Stage 4 disease, to have unknown grade, to have not received cancer treatment, and to have received discordant care (p<0.001 for all). For each primary treatment metric, the proportion of patients who received discordant care for the eligible metric was highest among patients with a comorbidity score of ≥2, compared with those with comorbidity scores of 1 and 0 (p<0.001 for all). Correspondingly, among patients with a comorbidity score of ≥2, 47.8% received discordant care, compared to 37.4% for those with a comorbidity score of 1 and 33.6% for those with a comorbidity score of 0 (<0.001). Overall survival decreased with increasing comorbidity (p<0.001) (Figure 1). The five-year overall survival was 53.2% (95% CI: 52.9-53.5%) for those with no comorbidities, 44.9% (95% CI: 44.2-45.5%) for those with a score of 1 and 33.6% (95% CI: 32.5-34.8%) for those with a score of ≥2. Patients who received discordant care had a 5-year survival of 41.7% (95% CI: 41.2-42.1%), while patients who received concordant care had a 5-year survival of 54.8% (95% CI: 54.4-55.1%) (Table 2). When we examined overall survival by comorbidity score and receipt of guideline-concordant care, ovarian cancer patients with comorbidities who received discordant care had worse survival than patients with comorbidities who received concordant care. (Supplemental Figure 1) Specifically, patients with comorbidity scores of 1 and ≥2 who received discordant care had 5-year survivals of 33.7% and 22.3%, respectively, compared with patients who received concordant care who had 5-year survivals of 50.3% and 41.3%, respectively. Even among patients with no comorbidities, patients who received discordant care had worse survival than patients who received concordant care. Specifically, patients with a comorbidity score of 0 who received discordant care had a 5-year survival of 44.9% while those who received concordant care had a 5-year survival of 56.1%. Further, patients with no comorbidities who received discordant care had worse short-term survival (approximately 4 years post-diagnosis) than patients with ≥2 comorbidities who received concordant care (p<0.001). We examined the association between comorbidity and survival, adjusting for potential confounders, and assessed whether this association was modified by receipt of guideline-concordant care. As in our unadjusted survival results, our multivariable models showed patients with comorbidities had worse survival than patients without comorbidities, and mortality increased with increasing comorbidity score. Compared to those with a comorbidity score of 0, patients with a comorbidity score of 1 had an 18% higher hazard of death (adjusted hazard ratio [aHR]=1.18; 95% CI, 1.16 - 1.20) while those with a comorbidity score of ≥2 had a 46% higher hazard of death (aHR=1.46; 95% CI, 1.42 - 1.50). We further observed statistically significant multiplicative (p<0.001 for comorbidity scores 1 and ≥2 versus 0) and additive (RERI=0.19, 95% CI: 0.14-0.24 and RERI= 0.45, 95% CI: 0.36-0.54 for comorbidity scores 1 and ≥2 versus 0, respectively) interactions between comorbidity and receipt of guideline-concordant care. Specifically, receipt of concordant care partially mitigated the adverse impact of comorbidity on survival. Among patients who received discordant care, comorbid illness was associated with a 27% (aHR=1.27, 95% CI: 1.23 - 1.31) and 65% (aHR=1.65, 95% CI: 1.59 - 1.72) higher hazard of death for comorbidity scores 1 and ≥2 versus 0. However, among patients who received concordant care, comorbid illness was associated with an 11% (aHR=1.11, 95% CI: 1.08 - 1.14) and 27% (aHR=1.27, 95% CI: 1.22 - 1.33) higher hazard of death for comorbidity scores 1 and ≥2 versus 0, respectively. Further, compared to patients who received concordant care, patients who received discordant care had an 11% (aHR=1.11, 95% CI: 1.09, 1.14), 27% (aHR=1.27, 95% CI: 1.23, 1.32), and 44% (aHR=1.44, 95% CI: 1.36, 1.53) increased hazard of death for patients with comorbidity scores of 0, 1, and ≥2, respectively. (Table 3) Discussion Main Findings : In this cohort of patients with ovarian cancer, pre-existing comorbid illness was associated with worse overall survival. This association was modified by guideline-concordant care, where patients with comorbid illness who received discordant care had worse survival than patients with comorbid illness who received concordant care. This highlights the importance of ensuring that all patients receive guideline-concordant care for treatment of ovarian cancer, and particularly those with pre-existing comorbidities. Interpretation : Similar work has been done assessing the role of various aspects of ovarian cancer treatment on the association between comorbidity and survival. One Danish study found that comorbidity was not associated with the type of treatment received, but was associated with delays in treatment initiation. 10 Another Danish study in older patients with ovarian cancer observed that comorbid illness was associated with decreased likelihood of receiving standard treatment (cytoreductive surgery and combination chemotherapy with carboplatin and a taxane), but even among those who received standard treatment, increased comorbid illness was associated with a worse prognosis. 9 Our findings expand upon this prior research by looking at care in a U.S. health system, including a treatment quality metric based on national treatment guidelines which incorporate specific tumor characteristics, and quantifying the interaction between comorbidity and receipt of guideline-concordant care. Adherence to evidence-based treatment guidelines has been shown to mitigate other adverse patient and hospital system factors that influence survival. One study demonstrated that patients with ovarian cancer had worse survival if they received care at a low- vs. high-volume hospital; however, adherence to guideline-concordant care at low-volume facilities improved patient survival. 15 Similarly, a study in patients undergoing coronary artery bypass grafting observed that patients treated at low-volume hospitals who received guideline-concordant care had similar survival to those treated at high-volume hospitals who received guideline-concordant care. 18 Expanding from these hospital-level studies, we developed an individual-level guideline-concordant care variable to examine its moderating role in the association between pre-existing comorbidities and survival. Strengths and Limitations : While this study benefited from the use of a large national cancer database, providing a large sample size of patients with ovarian cancer, we recognize several limitations of the data. While the Charlson-Deyo Comorbidity Index has been validated and shown to be highly associated with survival, it does not capture all comorbidities a patient may have. Some limitations of the guideline-concordant care variable reflect the structure and data availability within the NCDB. Specifically, the guideline-concordant care variable only captured first-line treatment and did not include adherence to evidence-based guidelines for subsequent treatments. Further, the variable did not capture potential delays in treatment onset, the number of treatment cycles received, or incomplete courses of therapy which may have impacted survival. Additionally, the guideline-concordant care variable was a composite measure of four treatment metrics. It is possible the mitigating effect of guideline-concordant care on the association between comorbidity and survival varies across the treatment metrics which we were unable to capture. Our analysis assumed all patients were candidates for treatment despite comorbid illness which may have affected treatment eligibility. For instance, prior work has shown patients with ovarian cancer with pre-existing cardiovascular conditions may be less likely to receive chemotherapy treatment. 19 Moreover, if specific comorbid illnesses were principal drivers of specific treatment discordances, then efforts to increase corresponding adherence may not be impactful. Lastly, we were only able to examine overall survival, as cause-specific survival is unavailable in the NCDB. P atients with ovarian cancer with pre-existing comorbidities had worse survival compared with those without comorbidities; however, receipt of guideline-concordant care partially attenuated this survival disadvantage. These findings suggest that improving concordance to guideline-directed treatment for patients with ovarian cancer may improve prognosis, even among those with pre-existing comorbid illness. Accordingly, future research should focus on identifying modifiable barriers to receipt of guideline-concordant care. In addition, further work is needed to determine which specific comorbid illnesses most substantially limit guideline-directed care for patients with ovarian cancer, and to establish evidence-based treatment strategies for these vulnerable patients. Conclusions : In closing, we found that patients with ovarian cancer who have pre-existing comorbidities had worse survival than those without comorbidities, and this association was modified by receipt of guideline-concordant care. Specifically, patients with ovarian cancer and comorbid illness who received discordant care had worse survival compared with those who received concordant care. CRediT authorship contribution statement : Jennifer Ferris: Formal analysis, methodology, writing-original draft, writing-review and editing; Joel Agrawal: Data curation, formal analysis, writing-original draft, writing-review and editing; Ling Chen: Data curation, formal analysis, methodology, writing-review and editing; Yongmei Huang: Methodology, writing-review and editing; Claudia Seguin: Conceptualization, writing-review and editing; Andrew Eckel: Writing-review and editing; Clare Wiberg: Writing-review and editing; Alexi Wright: Writing-review and editing, Mary Linton Peters: Writing-review and editing; Amy Knudsen: Writing-review and editing; Pari Pandharipande: Conceptualization, funding acquisition, writing-review and editing; Jason Wright: Conceptualization, methodology, funding acquisition, writing-review and editing. Declaration of competing interest : Dr. Jason Wright has received royalties from UpToDate, honoraria from the American College of Obstetricians and Gynecologists, and research support from Merck. Dr. Mary Linton Peters has received research support from Taiho Pharmaceuticals, AstraZeneca, Nucana, Lilly, and Genentech. Dr. Pari Pandharipande was a member of the Association of University Radiologists (AUR) – General Electric Radiology Research Academic Fellowship Board of Review; this activity is completed as of 2023. The other authors have no conflicts of interest. Funding : This work was supported by the National Cancer Institute [grant number R01CA266402] and was awarded following external peer review. The funder had no role in conducting the research and writing the paper. Acknowledgements : No acknowledgements References 1. American Cancer Society. Cancer Facts & Figures 2025. Atlanta: American Cancer Society; 2025. 2. US Preventive Services Task Force. 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History of Comorbidities and Survival of Ovarian Cancer Patients, Results from the Ovarian Cancer Association Consortium. Cancer Epidemiol Biomark Prev Publ Am Assoc Cancer Res Cosponsored Am Soc Prev Oncol 2017;26(9):1470–3. 8. Minlikeeva AN, Freudenheim JL, Cannioto RA, et al. History of hypertension, heart disease, and diabetes and ovarian cancer patient survival: evidence from the ovarian cancer association consortium. Cancer Causes Control CCC 2017;28(5):469–86. 9. Jørgensen TL, Teiblum S, Paludan M, et al. Significance of age and comorbidity on treatment modality, treatment adherence, and prognosis in elderly ovarian cancer patients. Gynecol Oncol 2012;127(2):367–74. 10. Noer MC, Sperling CD, Ottesen B, Antonsen SL, Christensen IJ, Høgdall C. Ovarian Cancer and Comorbidity: Is Poor Survival Explained by Choice of Primary Treatment or System Delay? Int J Gynecol Cancer Off J Int Gynecol Cancer Soc 2017;27(6):1123–33. 11. Bristow RE, Chang J, Ziogas A, Anton-Culver H. 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J Natl Compr Cancer Netw JNCCN 2024;22(8):512–9. 17. Liu J, Berchuck A, Backes FJ, et al. NCCN Guidelines® Insights: Ovarian Cancer/Fallopian Tube Cancer/Primary Peritoneal Cancer, Version 3.2024. J Natl Compr Cancer Netw JNCCN 2024;22(8):512–9. 18. Auerbach AD, Hilton JF, Maselli J, Pekow PS, Rothberg MB, Lindenauer PK. Shop for quality or volume? Volume, quality, and outcomes of coronary artery bypass surgery. Ann Intern Med 2009;150(10):696–704. 19. Chang WH, Lai AG. Pan-cancer analyses of the associations between 109 pre-existing conditions and cancer treatment patterns across 19 adult cancers. Sci Rep 2024;14:464. Supplementary Material File (ovca_comorbidity_survival_figures_bjog.docx) Download 34.86 KB File (ovca_comorbidity_survival_tables_bjog.docx) Download 41.66 KB Information & Authors Information Version history V1 Version 1 05 March 2026 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords carcinoma of the ovary: chemotherapy carcinoma of the ovary: diagnosis carcinoma of the ovary: surgery epidemiology: gynaecological cancer gynaecological oncology Authors Affiliations Jennifer S. Ferris [email protected] Columbia University Department of Obstetrics and Gynecology View all articles by this author Joel Agarwal Columbia University Department of Obstetrics and Gynecology View all articles by this author Ling Chen Columbia University Department of Obstetrics and Gynecology View all articles by this author Yongmei Huang Columbia University Department of Obstetrics and Gynecology View all articles by this author Claudia L. Seguin The Ohio State University Wexner Medical Center Department of Radiology View all articles by this author Andrew Eckel Massachusetts General Hospital Institute for Technology Assessment View all articles by this author Clare E. Wiberg Massachusetts General Hospital Institute for Technology Assessment View all articles by this author Alexi A. Wright Dana-Farber Cancer Institute Department of Medical Oncology View all articles by this author Mary Linton B. Peters Massachusetts General Hospital Institute for Technology Assessment View all articles by this author Amy B. Knudsen Massachusetts General Hospital Institute for Technology Assessment View all articles by this author Pari V. Pandharipande The Ohio State University Wexner Medical Center Department of Radiology View all articles by this author Jason Wright Columbia University Department of Obstetrics and Gynecology View all articles by this author Metrics & Citations Metrics Article Usage 146 views 69 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Jennifer S. Ferris, Joel Agarwal, Ling Chen, et al. The impact of comorbidity and guideline-concordant care on survival in ovarian cancer patients: a retrospective cohort study. Authorea . 05 March 2026. 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