Clinical Trial Details
— Status: Active, not recruiting
Administrative data
NCT number |
NCT05299801 |
Other study ID # |
2021-MMC-059 |
Secondary ID |
|
Status |
Active, not recruiting |
Phase |
|
First received |
|
Last updated |
|
Start date |
July 1, 2021 |
Est. completion date |
July 1, 2025 |
Study information
Verified date |
March 2022 |
Source |
Maxima Medical Center |
Contact |
n/a |
Is FDA regulated |
No |
Health authority |
|
Study type |
Observational
|
Clinical Trial 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.
Description:
Ten-year retrospective single-center cohort study in which we will:
1. retrospectively follow patients who underwent endometrial ablation using Novasure
between 2009-2019 at the Máxima Medical Center and observe the incidence of outcomes and
features.
2. assess which patient and procedure features are associated with failure of NovaSure
treatment, defined as occurrence of any subsequent invasive procedure related to AUB
within 3 years, of women who underwent this treatment in Maxima MC between 2008 and
2018.
3. assess which patient and procedure features are associated with a successful Novasure
treatment, defined as freedom of subsequent invasive procedure within 3 years related to
AUB, of women who underwent this treatment in Maxima MC between 2008 and 2018.
4. develop predictive and prognostic models that assess the probability of treatment
success or failure based on these patient and procedure variables.
Procedure
In this observational study, a clinical data collector will be used to review women (with
automatically pseudonymised data) who had endometrial ablation with the Novasure. An overview
will be given of patient and procedure features that may have played a role in the failure of
the treatment. To be able to investigate this, all women who underwent the intervention at
the Máxima Medical Center between 2008-2018 are included in the study. They received the
usual care and did not have to follow any additional procedures.
All patients will be included in the descriptive analysis. All descriptive analyses,
including total population size and follow-up time, will be given. Continuous variables are
presented as mean with standard deviation, or median with interquartile range, depending on
the distribution. Categorical variables are presented as number with percentage. Binary data
are presented as frequency and percentage. Time between Novasure and reintervention will be
given as mean (SD) and time-to-event (Kaplan-Meier). Moreover the relative risk for
reintervention is calculated. In addition to classical statistical analyses, data mining
(linear regression) and machine learning techniques are applied. Supervised classification
models are learned to compute the importance factors of independent variables with respect to
the dependent variable.
In this research only retrospective file research on pseudonymised data is carried out with
outcome measures that are part of the standard quality control. All data is obtained from the
Clinical data Collector. The collector extracts and pseudonymizes data from the electronic
patient record. Patients are not subjected to additional actions or are not imposed to any
rules of conduct for this research. There are also no questionnaires sent to patients that
affect the psychological integrity or that are perceived as stressful by the test subjects.