Introduction/Background*Chemotherapy negatively affects ovarian function and fertility. At present, clinical assessment does not allow an accurate prediction of ongoing ovarian function after cancer or future ability to conceive. This study aims to develop a predictive model for premature ovarian failure (POF) after chemotherapy analyzing the outcomes of a cohort of young women with different types of cancer in terms of menstrual function recovery and fertility.
Methodology Retrospective, monocentric cohort study including 348 patients referring to Oncofertility Unit of San Raffaele Hospital (Milan, Italy) from August 2011 to January 2020. POF was defined as absence of menstrual cycles for at least 12 months before the time of study. Infertility was defined as failure to achieve a spontaneous pregnancy after regular unprotected intercourse for at least 12 months. Prognostic factors associated with POF were identified using ANOVA, χ-square test and univariate binary logistic regression. A multivariate logistic regression analysis using forward conditional mode was performed to create a predictive model by selecting the best combination of prognostic factors. A ROC curve was constructed, with measurement of area under the curve (AUC) and corresponding 95% confidence intervals.
Result(s)*At follow-up, a total of 227 patients were alive without disease. Data about menstrual function resumption was available for 184 patients. Forty-five patients (25%) experienced POF after cancer treatment. A total of 60 patients (33%) were infertile. Factors and chemotherapy schemes associated with a higher prevalence of POF are reported in table 1 and table 2, respectively. The best predictive model for POF could be identified by the combination of the following factors: age; number of chemotherapy lines; vincristine, adriamycin and ifosphamide/adriamycin and ifosphamide (VAI/AI), capecitabine and adriamycin, bleomycine, vinblastine and doxorubicin (ABVD) (AUC=0.906, CI 95% 0.858 – 0.954, p=0.0001).
Conclusion*The model we constructed predicts with good accuracy the individual probability of loss of ovarian function at cancer diagnosis and with every change of treatment. This has a very important implication: the necessity to implement fertility preservation strategies when the risk increases.
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