An observational study in Endometriosis, sponsored by Biofourmis Singapore Pte Ltd.. Completed at 7 sites in 3 countries. Open to female participants aged 21 Years to 50 Years. Per ClinicalTrials.gov, last updated 2022-05-19.
Sponsored by Biofourmis Singapore Pte Ltd. · Observational
This study aims to explore a novel objective measurement for endometriosis-related pain. A variety of pain symptoms are associated with endometriosis, including dysmenorrhea, dyspareunia, dysuria, dyschezia and chronic pelvic pain. However, a clear characterization of pain typology and topology in populations with endometriosis, other gynecologic pathology, or a normal pelvis is lacking. Understanding the precise nature of the relationship between pain and endometriosis is important for the clinical management of affected women, given the body of evidence indicating that medical and surgical management for pain associated with endometriosis has been shown to be effective. Evaluating the relationship between pain and endometriosis, however, is challenging given that pain is difficult to measure and the mechanism by which endometriosis causes pain is not well understood. While previous studies have provided important data on the incidence of pelvic pain and endometriosis, little research has been done to assess both the typology and topology of pelvic pain, pain beyond the pelvis, endometriosis diagnosis, or severity of pain using operative findings and a standardized classification system.
BACKGROUND ON ENDOMETRIOSIS
A variety of pain symptoms are associated with endometriosis, including dysmenorrhea, dyspareunia, dysuria, dyschezia and chronic pelvic pain. However, a clear characterization of pain typology and topology in populations with endometriosis, other gynecologic pathology, or a normal pelvis is lacking. Understanding the precise nature of the relationship between pain and endometriosis is important for the clinical management of affected women, given the body of evidence indicating that medical and surgical management for pain associated with endometriosis has been shown to be effective. Evaluating the relationship between pain and endometriosis, however, is challenging given that pain is difficult to measure and the mechanism by which endometriosis causes pain is not well understood. While previous studies have provided important data on the incidence of pelvic pain and endometriosis, little research has been done to assess both the typology and topology of pelvic pain, and pain beyond the pelvis, and endometriosis diagnosis and severity using operative findings and a standardized classification system.
Historically, pain has been measured using subjective scales to determine the presence of pain and its severity. Common scales include the numeric rating scale (NRS), visual analog scale (VAS), and visual response scale (VRS). While this is important information, self-reporting is a problematic metric for both diagnostic and research purposes as it depends on pain history, cognitive and behavioral factors, and can vary over time. Other measures used in clinical practice, such as the Biberoglu and Behrman (B\&B) score, incorporate both patient and clinician assessments of pain. However, patients describe symptomatology and gynecologists evaluate tenderness and induration during physical examination with an exceedingly high risk of bias and inconsistent reproducibility. Over the past few years, significant advances have been made in the development of valid biomarkers or surrogate markers for the presence and severity of pain. Measurement of various physiology parameters like heart rate, heart rate variability and electrodermal activity have shown to be associated with the presence of pain and can aid clinical interpretation.
STUDY RATIONALE
Several ratings, such as the numeric rating scale (NRS) are mainly used in clinical trials to determine the presence and severity of pain associated with endometriosis. Patient Reported Outcomes (PRO) such as NRS can be problematic as they are subjective, containing recall bias, and can vary over time. Thus, a more accurate and objective measurement of pain is needed to evaluate the efficacy of treatment with pain associated with endometriosis.
901 studies on the registry are indexed under Endometriosis; 259 are open to participants now.
This study's enrollment of 90 is below the median of 128 across 344 observational studies indexed under Endometriosis.
Browse Endometriosis studies →Biofourmis Singapore Pte Ltd. is the lead sponsor of 4 studies on the registry; none are open to participants now.
Counted across the registry records on this site, refreshed daily.
124 study participants, aged between 21 to 50 years old, female, who is confirmed diagnosis of endometriosis.
Patient who meets either A or B or both in the following criteria: A. Confirmed diagnosis of endometriosis (laparoscopy/laparotomy) performed WITHIN 10 YEARS prior to the study participation.
B. Current clinical diagnosis (endometriotic cysts or deep infiltrating endometriosis detected by TVUS, TRUS or MRI) WITHIN 6 MONTHS prior to the study participation.
Patient who meets either A or B in the following criteria:
A. Patient is NOT treated with hormonal agents for endometriosis WITHIN 4 WEEKS prior to study participation, and have regular menses (i.e. 21-38 days) within 38 days prior to the study participation.
B. Patient started hormonal agents for endometriosis, including combined oral contraceptives MORE THAN 8 WEEKS prior to the study participation, or progestins, danazol, GnRH agonists, GnRH antagonists or Progesterone and Levonorgestrel Releasing IUDs MORE THAN 12 WEEKS prior to the study participation, AND stable use of the medication is expected during the study period
Exclusion criteria:
The concordance between Pain Index and NRS scores during the study period. (Categorised into none, mild, moderate and severe pain)
Pain Index will be generated via vital sign collected from subjects and processed by Biofourmis's propriety algorithm. Both pain index and NRS will be categorised into None (0), Mild (1-3), Moderate (4-6), and Severe (7-10) pain. Concordance will be measured using unweighted Kappa Statistic for multiple categories with 95% CI. Percentage agreement between the categories will be also calculated by taking the number of concordant pairs divided by the total number of pain episodes.
Time frame: 12 weeks
The correlation between 11-point Pain Index (0-10) and 11-point NRS score (0-10).
The generated Pain Index will be classified into 11 points (0-10) in accordance with the raw NRS score. Correlation between the 11-point Pain Index and raw NRS score will be measured using Spearman correlation.
Time frame: 12 weeks
Exploratory Endpoint 1: Correlation between Quality of Life (EQ-5D-5L and EHP-30), Productivity (HRPQ), PROMIS-Fatigue with Sleep Quality and Stress Values calculated using Biofourmis's propriety algorithm.
The Pearson correlation and its statistical significance between the various Quality of Life measures and the Sleep Quality and Stress Values (calculated using Biofourmis's propriety algorithm) will be presented in a matrix table.
Time frame: 12 weeks
Exploratory Endpoint 2: Trend of Quality of Life over the study period
The trend of Quality of life measures will be presented using line charts.
Time frame: 12 weeks
Exploratory Endpoint 3: Trend of Pain Index and NRS categories over the study period
The trend of Pain Index and NRS categories (None, Mild, Moderate, Severe) will be presented using bar graphs.
Time frame: 12 weeks
Exploratory Endpoint 4: Effect of concomitant medication usage on the NRS categories
Effects of concomitant medication usage will be measured as an increment or decrement in NRS pain categories (None, Mild, Moderate, Severe), based on the highest pain reported by patient before taking the medication and the pain report after medication usage.
Time frame: 12 weeks
Exploratory Endpoint 5: Effect of concomitant medication usage on the Pain Index categories
Effects of concomitant medication usage will be measured as an increment or decrement in Pain Index categories (None, Mild, Moderate, Severe), based on the highest pain reported by patient before taking the medication and the Pain Index generated based on the pain report after medication usage.
Time frame: 12 weeks
Exploratory Endpoint 6: Correlation between EQ-5D-5L and physiological parameters.
The correlation between EQ-5D-5L and physiological parameters will be presented as scatterplots with the Pearson correlation and statistical significance.
Time frame: 12 weeks
Exploratory Endpoint 7: Correlation between EHP-30 and physiological parameters.
The correlation between EHP-30 and physiological parameters will be presented as scatterplots with the Pearson correlation and statistical significance.
Time frame: 12 weeks
Exploratory Endpoint 8: Correlation between Productivity (HRPQ) and physiological parameters.
The correlation between Productivity (HRPQ) and physiological parameters will be presented as scatterplots with the Pearson correlation and statistical significance.
Time frame: 12 weeks
Exploratory Endpoint 9: Correlation between PROMIS-Fatigue and physiological parameters.
The correlation between PROMIS-Fatigue and physiological parameters will be presented as scatterplots with the Pearson correlation and statistical significance.
Time frame: 12 weeks
Exploratory Endpoint 10: Change in Pain Index, NRS categories over the menstrual cycle.
Changes in Pain Index, NRS categories (None, Mild, Moderate, Severe) over the menstrual cycle will be summarized by plotting bar graphs across menstrual cycle.
Time frame: 12 weeks
Exploratory Endpoint 11: Change in physiological parameters over the menstrual cycle.
Changes in physiological parameters over the menstrual cycle will be summarized using boxplots across menstrual cycle.
Time frame: 12 weeks
Exploratory Endpoint 12: Change in Pain Index, NRS categories by the type of lesions
Changes in Pain Index, NRS categories (None, Mild, Moderate, Severe) over the lesion types will be summarized using bar graphs across the menstrual cycle.
Time frame: 12 weeks
Exploratory Endpoint 13: Change in physiological parameters by the type of lesions
Changes in physiological parameters over the lesion types will be summarized using boxplots across the menstrual cycle.
Time frame: 12 weeks
Plan to share: No
This study is completed, as verified in May 2022. You cannot join it, but the record below documents what was studied.
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Biofourmis Singapore Pte Ltd.