A consensus-based preoperative risk stratification algorithm has been shown to dramatically reduce unnecessary oophorectomies in children and adolescents with ovarian masses. The findings of this study were published in the Journal of American Medical Association.
This research conducted in 11 children's hospitals in the United States between August 2018 and January 2021 aimed to evaluate the effectiveness of this algorithm in distinguishing between benign and malignant ovarian pathology, ultimately leading to fewer unnecessary surgeries.
The study involved 519 patients aged 6 to 21 years. Three phases were examined: a preintervention phase with 96 patients, an adoption phase with 105 patients, and the main intervention phase with 318 patients.
In the preintervention phase, 16.1% of patients underwent unnecessary oophorectomies. However, during the intervention phase, this percentage dropped significantly to 8.4%, marking an absolute reduction of 7.7%. This reduction is a testament to the effectiveness of the preoperative risk stratification algorithm.
In the intervention phase, the algorithm demonstrated impressive test performance. It exhibited a sensitivity of 91.6%, meaning it accurately identified benign lesions in over 91% of cases. The specificity was equally impressive at 90%, indicating it correctly identified malignant cases 90% of the time. Moreover, the algorithm boasted a positive predictive value of 99.3% and a negative predictive value of 41.9%.
Overall, the algorithm was able to significantly reduce misclassification during the intervention phase, with only 0.7% of malignant cases being treated with ovary-sparing surgery. Algorithm adherence during this phase was also high, at 95%, with a fidelity rate of 81.8%.
These findings are highly promising as the authors suggest that the adoption of this algorithm could prevent unnecessary oophorectomies during adolescence, sparing young patients from potential lifelong consequences. While the results are encouraging, further research will be needed to explore any barriers to algorithm adherence and ensure its widespread implementation.
Reference:
Minneci, P. C., Bergus, K. C., Lutz, C., Aldrink, J., Bence, C., Breech, L., Dillon, P. A., Downard, C., Fraser, J. D., Grabowski, J., Hirschl, R. B., Kabre, R., Lal, D. R., … Landman, M. (2023). Reducing Unnecessary Oophorectomies for Benign Ovarian Neoplasms in Pediatric Patients. In JAMA (Vol. 330, Issue 13, p. 1247). American Medical Association (AMA). https://doi.org/10.1001/jama.2023.17183