Submission Type

Empirical Research


cancer, methods, comparative effectiveness research


Objective: Administrative claims data offer an alternative to chart abstraction to assess ovarian cancer recurrence, treatment and outcomes. Such analyses have been hindered by lack of valid recurrence and treatment algorithms. In this study, we sought to develop claims-based algorithms to identify ovarian cancer recurrence and secondary debulking surgery, and to validate them against the gold-standard of chart abstraction.

Methods: We conducted chart validation studies; 2 recurrence algorithms and 1 secondary surgery among 94 ovarian cancer patients treated at one hospital between 2003-2009. A new recurrence algorithm was based on treatment timing (≥6 months after primary treatment) and a previously validated algorithm was based on secondary malignancy codes. A secondary debulking surgery algorithm was based on surgical billing codes.

Results: The new recurrence algorithm had: sensitivity=100% (95% confidence interval [CI]=87%-100%), specificity=89% (95%CI=78%-95%), kappa=84% (SE=10%) while the secondary-malignancy-code recurrence algorithm had: sensitivity=84% (95%CI=66%-94%), specificity=44% (95%CI=31%-57%), kappa=23% (SE=8%). The secondary surgery algorithm had: sensitivity=77% (95%CI=50%-92%), specificity= 92% (95%CI=83%-97%), kappa=66% (SE=10%).

Conclusions: A recurrence algorithm based on treatment timing accurately identified ovarian cancer recurrence. If validated in other populations, such an algorithm can provide a tool to compare effectiveness of recurrent ovarian cancer treatments.

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License.