Objective:

To evaluate the association of clinical factors, primarily ECOG classification, with patient performance status and their use in clinical decision-making for surgical treatment of epithelial ovarian cancer (EOC).

Approach:

• Study Design: Retrospective analysis of 228 patients with EOC who underwent debulking surgery at Kuopio University Hospital from 2019 to 2022.
• Data Collection: ECOG classification and other clinical parameters were collected from hospital records.
• Statistical Analysis: Kruskal–Wallis test for association analysis; Kaplan–Meier for univariate survival analysis; Cox regression for multivariate survival analysis.

Key Findings:

• 56.6% of patients were ECOG 0, 33% were ECOG 1, and 10.1% were ECOG 2–4.
• Age, self-reported walking distance, stair climbing capacity, ASA classification, albumin, prealbumin, and number of comorbidities were significantly associated with ECOG groups.
• ECOG classification was not associated with complications or length of hospital stay.
• Lower ECOG scores predicted higher survival rates.

Interpretation:

ECOG classification is a useful tool in evaluating patients' physical status for surgical treatment planning in EOC.

Limitations:

• Retrospective design may introduce selection bias.
• Single-center study limits generalizability.

Conclusion:

ECOG classification can aid in precise patient selection for operative treatment in EOC.