Objective Frailty is more reliable than chronological age in predicting the effectiveness and tolerability of treatments in cancer patients. An increasing number of screening tools have been proposed, however none have received unanimous consent or been specifically designed for women with gynecological malignancies.
This study’s aim was to develop a clinical application of a screening tool to identify frail patients >70 years old diagnosed with either ovarian or endometrial cancers.
Methods A 20 item questionnaire was developed and administered to the cohort before surgery or neoadjuvant chemotherapy. A cut-off for frailty definition was determined by analyzing the correlation of questionnaire scores with the completion of treatments. The association between frailty and treatment related complications was assessed using a Chi-squared test for categorical variables and a t-test for continuous variables.
Results Our study included 100 patients, 50% diagnosed with endometrial cancer and 50% with ovarian cancer. A questionnaire score of 4 was the best cut-off for frailty definition (sensitivity 77%, specificity 100%). Surgical grade III and grade IV complications were observed only in frail patients (p=0.01) and hospitalization was significantly longer in frail women affected by ovarian cancer (p=0.01). Frail patients were more exposed to chemotherapy administration delay (p=0.0005), treatment discontinuation (p=0.001) and hematological toxicities, especially anemia ≥grade 2 (p=0.009) and thrombocytopenia any grade (p=0.0001).
Conclusion With a cut-off score of 4, our tool can identify frail patients with significantly higher incidence of grade III-IV postoperative complications, length of stay, medical treatment discontinuation rates and hematological toxicities.
- Ovarian Cancer
- Endometrial Neoplasms
Data availability statement
All data relevant to the study are included in the article or uploaded as supplementary information.
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Contributors AF: conceptualization, methodology, project administration, writing - review and editing, guarantor. RM: data curation, investigation, writing- original draft preparation. MV: data curation, writing- original draft preparation, software, formal analysis. EB: data curation, statistical analysis. JOSPS: data curation, writing-original draft preparation. MG: data curation, writing-original draft preparation. AT: data curation, writing-original draft preparation. FG: data curation, writing-original draft preparation. DA: investigation, data curation, writing-original draft preparation. NB: validation, supervision.
Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.
Competing interests None declared.
Provenance and peer review Not commissioned; externally peer reviewed.