DAta MIning to Evaluate Novasure Treatment
NCT05299801 · Status: UNKNOWN · Type: OBSERVATIONAL · Enrollment: 1038
Last updated 2022-03-29
Summary
Abnormal Uterine Bleeding (AUB) is a common health problem that affects approximately 30% of women of reproductive age and can have several underlying causes. It significantly affects quality of life, use of medical resources and health costs. Endometrial ablation is a commonly used minimally invasive surgical procedure for the treatment of AUB that destroys endometrial tissue. This procedure is an alternative treatment to hysterectomy because it is less invasive and has a shorter recovery period. Several ablation techniques are available to remove endometrial tissue, including bipolar radiofrequency (NovaSure treatment). While patient satisfaction with this form of endometrial ablation for the treatment of AUB is high, approximately 10-20% of women undergoing endometrial ablation require additional invasive surgery, primarily because of persistent blood loss or pain. There is therefore a need to identify and evaluate factors that can improve women's outcomes, or that can be building blocks for prognostic models that can be used to influence clinical practice. In this 10-year single-center retrospective cohort study, we aim to apply data mining and machine learning techniques to uncover hidden relationships/patterns between variables, and identify factors and patients at increased risk for Novasure treatment failure. With multiple time variables, this is not possible with a simple statistical analysis. Discovering these patterns and risk factors could help improve medical care, patient counseling and patient satisfaction.
Conditions
- Abnormal Uterine Bleeding
Sponsors & Collaborators
-
Utrecht University
collaborator OTHER -
Maxima Medical Center
lead OTHER
Eligibility
- Sex
- FEMALE
- Healthy Volunteers
- No
Timeline & Regulatory
- Start
- 2021-07-01
- Primary Completion
- 2024-07-01
- Completion
- 2025-07-01
Countries
- Netherlands
Study Locations
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