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

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

Status
UNKNOWN
Phases
Unknown
Study type
Observational
Source
ClinicalTrials.gov
Registry ID
NCT05691322
Acronym
ValDEVA
Enrollment
276
Registered
2023-01-20
Start date
2022-10-19
Completion date
2024-01-19
Last updated
2023-01-20

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Endometriosis

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).

Interventions

DIAGNOSTIC_TESTtransvaginal ultrasound

Transvaginal ultrasound for endometriosis diagnosis (UBESS)

DIAGNOSTIC_TESTMRI

MRI for endometriosis diagnosis (Enzian classification)

Sponsors

One Clinic
Lead SponsorOTHER

Study design

Observational model
CASE_ONLY
Time perspective
PROSPECTIVE

Eligibility

Sex/Gender
FEMALE
Age
18 Years to 45 Years
Healthy volunteers
No

Inclusion criteria

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

Exclusion criteria

* 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.

Design outcomes

Primary

MeasureTime frame
Positive likelihood ratio of the algorithm classification based on the MRI or UBESS US results1 day

Countries

France

Contacts

Primary ContactAlba Nicolas-Boluda, PhD
alba.nicolas-boluda@one.fr0033782873392
Backup ContactArnaud Fauconnier, Prof
arnaud.fauconnier@ght-yvelinesnord.fr

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026