Related Experiment Video
Updated: Sep 17, 2025
Murine Model for Non-invasive Imaging to Detect and Monitor Ovarian Cancer Recurrence
Published on: November 2, 2014
Register-based algorithm to detect post-operative complications in patients with ovarian cancer
Jakob Ohm Oreskov1,2, Cecilie Nørregaard Albertsen1, Claus Høgdall1
1Department of Gynaecology, Copenhagen University Hospital - Rigshospitalet.
Introduction:
Epithelial ovarian cancer (OC) is the most fatal gynaecological cancer. The use of extensive surgical procedures implies the potential severity of post-operative complications. In Denmark, registration of complications has changed from manual database registration to data transfer from medical records to the Danish National Patient Registry (NPR). This study examines whether a new complication algorithm based on NPR data may be used to identify 30-day post-operative complications among patients with advanced stage IIIC-IV OC.
Methods:
Complications were graded according to Clavien-Dindo (CD). The algorithm was validated in a cohort undergoing surgery at the OUH, between 1 January 2007 and 31 December 2012. The CD grades were sub-grouped into mild (CD 0-2) and severe (CD 3-5) complications for sub-analyses.
Results:
A total of 330 patients were included. The overall sensitivity (SN) and specificity (SP) of the algorithm (CD 0-5) were 56.4% (95% confidence interval (CI): 48.0-65.0%) and 92.4% (95% CI: 86.5-93.0%), respectively, with an overall kappa coefficient (κ) of 0.43. For severe complications (CD 3-5), the algorithm had an SN of 74.2% (95% CI: 67.4-83.6%) and an SP of 97.4% (95% CI: 95.5-99.4%), with a κ of 0.65.
Conclusions:
The algorithm had a moderate SN and a high SP with substantial agreement regarding severe complications. A standardised registration of complications in the NPR will likely improve the algorithm's performance.
Funding:
The Danish Clinical Quality Program DKK 200,000.
Trial Registration:
Not relevant.
