Mette Calundann Noer1, Cecilie Dyg Sperling2, Sofie Leisby Antonsen3, Bent Ottesen3, Ib Jarle Christensen4, Claus Høgdall3. 1. Department of Gynecology, Juliane Marie Centre, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark. Electronic address: mette.calundann.noer@regionh.dk. 2. Documentation and Quality, The Danish Cancer Society, Strandboulevarden 149, Copenhagen, Denmark. 3. Department of Gynecology, Juliane Marie Centre, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark. 4. Department of Pathology, Herlev University Hospital, Herlev, Denmark.
Abstract
OBJECTIVE: To develop and validate a new feasible comorbidity index based on self-reported information suited for preoperative risk assessment of ovarian cancer patients. METHODS: The study was based on patient self-reported data from ovarian cancer patients registered in the Danish Gynecological Cancer Database between January 1, 2005 and December 31, 2012. The study population was divided into a development cohort (n=2020) and a validation cohort (n=1975). Age-stratified multivariate Cox regression analyses were conducted to identify comorbidities significantly impacting five-year overall survival in the development cohort, and regression coefficients were used to construct a new weighted comorbidity index. The index was applied to the validation cohort, and its predictive ability in regard to overall and cancer-specific five-year-survival was investigated. Finally, the performance of the new index was compared to that of the Charlson Comorbidity Index. RESULTS: Regression coefficients of age and five comorbidities (atherosclerotic cardiac disease, chronic obstructive pulmonary disease, diabetes, dementia and hypertension) were included in the new comorbidity index. The validation study found the new index to be significantly associated to both overall survival (HR 1.44, p=0.013) and cancer-specific survival (HR 1.51, p=0.017) in multivariate analyses adjusted for other prognostic factors. The index was a significantly better predictor than the Charlson Comorbidity Index. CONCLUSION: This new age-specific comorbidity index based on self-reported information is a significant predictor of overall and cancer-specific survival in ovarian cancer. It can be used to quickly identify those ovarian cancer patients requiring special attention in terms of preoperative optimization and postoperative care.
OBJECTIVE: To develop and validate a new feasible comorbidity index based on self-reported information suited for preoperative risk assessment of ovarian cancerpatients. METHODS: The study was based on patient self-reported data from ovarian cancerpatients registered in the Danish Gynecological Cancer Database between January 1, 2005 and December 31, 2012. The study population was divided into a development cohort (n=2020) and a validation cohort (n=1975). Age-stratified multivariate Cox regression analyses were conducted to identify comorbidities significantly impacting five-year overall survival in the development cohort, and regression coefficients were used to construct a new weighted comorbidity index. The index was applied to the validation cohort, and its predictive ability in regard to overall and cancer-specific five-year-survival was investigated. Finally, the performance of the new index was compared to that of the Charlson Comorbidity Index. RESULTS: Regression coefficients of age and five comorbidities (atherosclerotic cardiac disease, chronic obstructive pulmonary disease, diabetes, dementia and hypertension) were included in the new comorbidity index. The validation study found the new index to be significantly associated to both overall survival (HR 1.44, p=0.013) and cancer-specific survival (HR 1.51, p=0.017) in multivariate analyses adjusted for other prognostic factors. The index was a significantly better predictor than the Charlson Comorbidity Index. CONCLUSION: This new age-specific comorbidity index based on self-reported information is a significant predictor of overall and cancer-specific survival in ovarian cancer. It can be used to quickly identify those ovarian cancerpatients requiring special attention in terms of preoperative optimization and postoperative care.