Prospective Validation of the DEVA Algorithm for the Prediction of Severe Endometriosis

NCT05691322 | Unknown

• 1 other identifier: ValDEVA-01
• Study Type: observational
• Target: 276
• Locations: 1 country
• Sites: 1

Brief Summary

Endometriosis is a chronic disease affecting 1 in 10 women. Its diagnosis is difficult and the time between the first symptoms and diagnosis is of about 7 years. diagnosis requires specialized imaging performed by referral practitioners for this pathology. According to current recommendations, non-specialized pelvic ultrasound is the recommended first-line examination. However, only reference ultrasound, performed by a doctor specialized in the disease and carried out according to a specific protocol (such as UBESS), is sufficiently reliable for diagnosis. The number of practitioners performing this type of examination is very low in France, which in practice results in a very low reliability of this strategy in real life. Pelvic MRI is also a much more reliable examination, available for review. However its access is limited. In addition, the false positive rate can be as high as 20%, particularly for minor forms of the disease. The DEVA algorithm has been developed for the identification of women with endometriosis based on a self-administered pelvic pain symptom questionnaire (ENDOPAIN). In a preliminary study, this algorithm seems reliable for identify patients at high risk of this disease and would thus allow to triage patients requiring patients requiring an immediate MRI in order to shorten diagnostic delays. External validation of the algorithm is therefore necessary before clinical use. The main objective od this study is the prospective external validation of the diagnostic reliability of the DEVA algorithm used for the detection of women with endometriosis (visible on MRI or transvaginal ultrasound).

Conditions

Endometriosis

Outcome Measures

Primary Outcomes (1)

• Positive likelihood ratio of the algorithm classification based on the MRI or UBESS US results

  1 day

Interventions

transvaginal ultrasound DIAGNOSTIC_TEST

Transvaginal ultrasound for endometriosis diagnosis (UBESS)

MRI DIAGNOSTIC_TEST

MRI for endometriosis diagnosis (Enzian classification)

Eligibility Criteria

• Age: 18 Years - 45 Years
• Healthy Volunteers: No
• Age Groups: Adult (18-64)
• Sampling Method: Probability Sample

Study Population

Female volunteers aged 18-45 years, referred for pelvic MRI or transvaginal ultrasound referral regardless of symptomatology. The practitioners performing the examinations are recognised for their experience in the diagnosis of diagnosis of endometriosis.

You may qualify if:

• Female patients
• patients aged 18-45
• Patients addressed for an pelvic IRM or transvaginal echography

You may not qualify if:

• Minor patients
• Patients Over 45 years of age
• Patients without social security
• Patients that do not know how to read in French
• Patients with a chronic pathology responsible of pain or handicap
• Patients with a major pelvic pathology
• Patients with more than 3 months of amenorrhea
• Patients diagnosed or suspicious of having an invasive cancer.

Sponsors & Collaborators

• One Clinic: lead

Study Sites (1)

Centre Hospitalier Intercommunal Poissy Saint Germain

Poissy, Île-de-France Region, 78303, France

RECRUITING

Related Publications (1)

• Fauconnier A, Drioueche H, Huchon C, Du Cheyron J, Indersie E, Candau Y, Panel P, Fritel X. Early identification of women with endometriosis by means of a simple patient-completed questionnaire screening tool: a diagnostic study. Fertil Steril. 2021 Dec;116(6):1580-1589. doi: 10.1016/j.fertnstert.2021.07.1205. Epub 2021 Sep 17.

  PMID: 34538656 RESULT

MeSH Terms

Conditions

Endometriosis

Condition Hierarchy (Ancestors)

Genital Diseases, Female; Female Urogenital Diseases; Female Urogenital Diseases and Pregnancy Complications; Urogenital Diseases; Genital Diseases

Central Study Contacts

Alba Nicolas-Boluda, PhD

CONTACT

0033782873392; alba.nicolas-boluda@one.fr

Arnaud Fauconnier, Prof

CONTACT

arnaud.fauconnier@ght-yvelinesnord.fr

Study Design

• Study Type: observational
• Observational Model: CASE ONLY
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: January 6, 2023
• First Posted: January 20, 2023
• Study Start: October 19, 2022
• Primary Completion: October 19, 2023
• Study Completion: January 19, 2024
• Last Updated: January 20, 2023

Record last verified: 2023-01

Data Sharing

• IPD Sharing: Will not share

Locations

FR (1)