An observational study in Endometriosis, Pelvic Pain and Infertility, Female, sponsored by Semmelweis University. Status unknown at 2 sites in Hungary. Open to female participants aged 14 Years to 45 Years, including healthy volunteers. Per ClinicalTrials.gov, last updated 2023-11-28.
Sponsored by Semmelweis University · Observational
The project aims to create a large prospective data bank using the Lucy medical mobile application and collect and analyze patient profiles and structured clinical data with artificial intelligence. In addition, authors will investigate the association of removed or restricted dietary components with quality of life, pain, and central sensitization.
Introduction: Endometriosis is a complex and chronic disease that affects ∼176 million women of reproductive age and remains largely unresolved. It is defined by the presence of endometrium-like tissue outside the uterus and is commonly associated with chronic pelvic pain, infertility, and decreased quality of life. Despite numerous proposed screening and triage methods such as biomarkers, genomic analysis, imaging techniques, and questionnaires to replace invasive diagnostic laparoscopy, none have been widely adopted in clinical practice.
. Despite the availability of various screening methods (e.g., biomarkers, genomic analysis, imaging techniques) that are intended to replace the need for invasive diagnostic laparoscopy, the time to diagnosis remains in the range of 4 to 11 years. Aims: The project aims to create a large prospective data bank using the Lucy medical mobile application and collect and analyze patient profiles and structured clinical data with artificial intelligence. In addition, authors will investigate the association of removed or restricted dietary components with quality of life, pain, and central sensitization. Methods: A Baseline and Longitudinal Questionnaire in the Lucy app collects self-reported information on symptoms related to endometriosis, socio-demographics, mental and physical health, nutritional, and other lifestyle factors. 5,000 women with endometriosis and 5,000 women in a control group will be enrolled and followed up for one year. With this information, any connections between symptoms and endometriosis will be analyzed with machine learning. Conclusions: Authors can develop a phenotypic description of women with endometriosis by linking the collected data with existing registry-based information on endometriosis diagnosis, healthcare utilization, and big data approach. This may help to achieve earlier detection of endometriosis with pelvic pain and significantly reduce the current diagnostic delay. Additionally, authors can identify nutritional components that may worsen the quality of life and pain in women with endometriosis; thus, authors can create evidence-based dietary recommendations.
Keywords: Endometriosis, Machine learning, Non-invasive diagnosis, Diet
925 studies on the registry are indexed under Endometriosis; 264 are open to participants now.
This study's planned enrollment of 10,000 is above the median of 125 across 354 observational studies indexed under Endometriosis.
Browse Endometriosis studies →Semmelweis University is the lead sponsor of 139 studies on the registry; 55 are open to participants now.
Counted across the registry records on this site, refreshed daily.
5000/5000 patienets with/without endometriosis This study is conducted as a multicenter, parallel-group trial. Study participants are being recruited through the Lucy app. Data are collected in the following countries: Hungary, Denmark, Sweden, Germany, and Austria. Participant recruitment began in December 2021 and is expected to continue until December 2024.
Go through the criteria one by one — yes, no or not sure — and take the list to your doctor or the study team. Kept in this browser only.
Exclusion Criteria:
5 000 people with endometriosis will be enrolled and followed up for 1one year. To participate in the study, the women must meet the inclusion criteria.
Diagnostic Test: Self reported data collection
5 000 people in a control group will be enrolled and followed up for 1one year. To participate in the study, the women must meet the inclusion criteria.
Diagnostic Test: Self reported data collection
ML assessement of colleceted data
Also known as: ML assessement
Patient- profiling using the Lucy app
Establish a comprehensive and extensive prospective big data repository using the Lucy app. This initiative aims to identify unique clinical cohorts by leveraging various factors such as digital footprints, symptoms, patient experiences, comorbidities, clinical severity, and lifestyle patterns. By employing Using ML for big data analysis, authors can build patient profiles and structured clinical data that facilitate the early detection of endometriosis with pelvic pain. Self-reported data of the participants will be measured as follows: * Evaluating the quality of life using the 5-level EQ-5D (EQ-5D-5L) * Endometriosis Health Profile 5 (EHP-5) . * Pain scores using the Visual Analogue Scale (VAS) . * Central pain sensitization using the short version of Central Sensitization Inventory (CSI-9)
Time frame: 24 month
Impact of diet and lifestyle on the development of endometriosis
Additionally, authors can identify nutritional components that may worsen the quality of life and pain in women with endometriosis; thus, they can create evidence-based dietary recommendations. The changes in quality of life will be assessed by using Self-reported data of the participants will be measured as follows: Change From Baseline in Pain Scores on the Visual Analog Scale at 12 months. Changes from baseline values on EHP5 at 12 months
Time frame: 24 month
Economical burden of endometriosis
Economical burden taking into account the cost of diet and healthcare use. The exact cost of endometriosis related diet will be reported per month in EUR.
Time frame: 24 month
Plan to share: Yes — In case of any interest we are happy to share IPD
Supporting information: Study protocol
Time frame: Data will become available from 01.01.2025 for 5 years
Access criteria: Data will be available for researchers in the field
No publications or documents are linked to this record.
This study is status unknown, as verified in Sep 2023. You cannot join it, but the record below documents what was studied.
Get an email when the registry record changes — status, dates, results — or when someone posts here.
Sign in to followQuestions and observations about this study, from anyone following it. Not medical advice, and not a channel to the study team — their contact details are on the registry record.
Sign in to join the discussion. Reading takes no account; posting does. You choose a display name, and a pseudonym is the default.
Nothing here yet. If you are running this trial, taking part in it, or weighing whether to, this is the place to say so.