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#1072 Assessment of the prognostic impact of the European society of gynecologic oncology quality indicators for advanced ovarian cancer surgery
  1. Nicole Concin1,
  2. Christoph Grimm1,
  3. Guillermo Villacampa2,
  4. Ane Gerda Z Eriksson3,
  5. ESGO Consortium4
  1. 1Medical University of Vienna, Vienna, Austria
  2. 2Vall d’Hebron Institute of Oncology, Barcelona, Spain
  3. 3Oslo University Hospital, The Norwegian Radiumhospital, Oslo, Norway
  4. 4N/A, N/a, Belgium


Introduction/Background Multiple studies have confirmed that centralization improves outcomes for women with advanced ovarian cancer (OC). Attempting to implement quantifiable measures on quality of care, ESGO has established ten quality indicators (QIs) for advanced OC surgery based on available literature and expert opinion. This multi-center real-world study assesses the prognostic impact of ESGOs QIs in ESGO-accredited centers.

Methodology Consecutive patients presenting with advanced stage OC from 2020-2022 at participating centers are included. Primary endpoint is progression-free survival (PFS). At least 2000 PFS events are needed for final analysis. Secondary endpoints include overall survival (OS) and percentage of centers adhering to each QI.

Time-to-event endpoints (PFS, OS, etc) will be analyzed according to the Kaplan-Meier method. Kaplan-Meier survival curves, medians and associated 95% CIs will be reported. PFS and OS rates at 1, 2 and 3 years will be estimated. Cox regression on univariate models will be used to calculate risk reduction. Hazard ratio (HRs) with 95% CIs will be reported to evaluate impact of prognostic factors, including center’s accreditation status and each QI (met/not met) assessed on a patient level. Other clinical factors will be considered, such as (not exclusively) FIGO stage, ECOG status, surgical complexity score, residual disease, BRCA mutation status, adjuvant- and maintenance therapy use and HIPEC. To select variables with the highest prognostic impact for PFS and OS, we will perform a least absolute shrinkage and selection operator regression using package glmnet in R software to build the most parsimonious multivariable model. The relevance of missing sample data will be assessed. If nothing unusual is observed, the missing at random approach will be use, and multiple imputation of random missing values will be carried out.

Results Data cutoff will be 31/01/2024. We expect to include more than 10,000 patients registered in more than 60 centers.

Conclusion Not available.

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