A new clinically applicable age-specific comorbidity index for preoperative risk assessment of ovarian cancer patients

Abstract

Highlights

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Self-reported comorbidity is useful for risk-assessment in ovarian cancer.

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This new comorbidity index risk-scores patients according to overall survival.

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The index may help to ensure individualized treatment of ovarian cancer patients.

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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.