Reducing unnecessary oophorectomies for benign neoplasms in girls: A multi-institutional study
BACKGROUND: Although the majority of ovarian neoplasms in children and adolescents are benign, many patients with a benign ovarian mass undergo an unnecessary oophorectomy, which can have permanent negative impact on multiple health domains.
GAP: There are no evidence-based interventional studies aimed at reducing unnecessary oophorectomies in girls.
HYPOTHESIS: An evidence-based, pre-operative risk stratification algorithm will accurately identify girls with benign ovarian neoplasms that are candidates for OSS with a positive predictive value (PPV) of 98% and a false positive rate of <5%. Implementation of a pre-operative risk stratification algorithm will reduce the rate of unnecessary oophorectomies in girls with benign ovarian neoplasms from 27% to < 10%.
METHODS: We will perform a multi-institutional, interventional study of an evidence-based pre-operative risk stratification algorithm. Using a pre-post design, we will implement this algorithm across 11 children’s hospitals and evaluate its ability to discriminate between benign and malignant ovarian pathology and reduce the rate of unnecessary oophorectomies for benign lesions.
RESULTS: We will calculate the accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the algorithm for identifying benign lesions, and we will compare the rate of unnecessary oophorectomies, defined as an oophorectomy performed for pathologically benign disease, before and after implementation.
IMPACT: Positive results from this study will provide robust evidence for a national initiative to promote wide spread adoption of a pre-operative risk stratification algorithm to identify benign lesions appropriate for ovarian sparing surgery as the standard of care for girls with ovarian masses.
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