Randomized Study of a Dematerialized Management for Post-Emergency Gynecological Follow-Up

NCT ID: NCT06615843

Last Updated: 2025-02-11

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

NOT_YET_RECRUITING

Clinical Phase

NA

Total Enrollment

200 participants

Study Classification

INTERVENTIONAL

Study Start Date

2025-05-31

Study Completion Date

2027-05-31

Brief Summary

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The GYNAB study aims to compare patient satisfaction and clinical outcomes between dematerialized post-emergency gynecological follow-up using a connected health app and traditional in-person follow-up. Conducted by Drs. Laëtitia CAMPIN and Louis MARCELLIN at Hôpital Cochin, it is sponsored by Assistance Publique - Hôpitaux de Paris. The main objective is to assess patient satisfaction at day 7 on a scale from 0 to 10, considering symptom management, follow-up quality, and task performance. Secondary objectives include comparing satisfaction at day 28, PROM-ED scores for symptom relief, understanding, reassurance, and care planning at days 7 and 28, hospitalization rates, unscheduled consultations, surgical interventions, total hospital time by day 28, and physician satisfaction at 28 days. This randomized trial involves women aged 18-60 needing follow-up for conditions like genital infections, non-complicated ectopic pregnancies, unlocated pregnancy, first trimester metrorrhagia, miscarriages , and severe vomiting before 12 weeks. Inclusion criteria are women aged 18-60 needing specified follow-up. Exclusion criteria are severe clinical intolerance, severe criteria ultrasound findings, non-French speakers, and inability to use digital applications. The intervention group uses a connected health app for data collection and communication, logging symptoms and test results, with medical team notifications for urgent updates. The control group receives traditional in-person follow-up with scheduled consultations and emergency contact information. The study hypothesizes that digital follow-up will improve patient experience, reduce hospital visits, and provide comparable or superior satisfaction and clinical outcomes. The results could promote broader digital health adoption in various medical fields.

Detailed Description

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Scientific Justification:

Emergency gynecological consultations are frequent reasons for hospital visits. Some clinical situations require scheduled follow-up to assess the progression of the initial condition through targeted questioning and additional tests such as blood work or pelvic ultrasounds. Digital health platforms, utilizing AI algorithms, have proven valuable in telehealth, appointment scheduling, personalized care, and prevention. These platforms allow secure, asynchronous follow-up, potentially enhancing patient experience and reducing unnecessary hospital visits.

Platform Capabilities: The secure platforms offer features like messaging, video consultations, document scanning, smart medical note-taking, shared medical records, structured questionnaires, and automatic extraction of standardized medical information from consultations, lab results, and other medical documents. Using machine learning, these platforms assist in structuring medical decisions while enabling patients to be active participants in their care.

Evaluation Criteria:

Post-emergency gynecological follow-up is not standardized. Iterative, unscheduled emergency visits can indicate poor care quality, though repeated scheduled visits are sometimes necessary for follow-up and not indicative of poor quality. Patient satisfaction, measured using validated tools like PROMs (Patient-Reported Outcome Measures), is a useful criterion for evaluating care quality. PROMs are increasingly used to assess healthcare quality and patient management, and their digitized, secure, and easily accessible formats allow efficient and standardized data collection.

PROM-ED Questionnaire:

A validated Canadian PROM-ED questionnaire evaluates patient experiences and satisfaction with emergency care, focusing on symptom relief, understanding, reassurance, and care planning. This standardized tool is appropriate for evaluating satisfaction with emergency gynecological care.

Hypothesis:

We hypothesize that dematerialized follow-up using a connected health app will improve patient experience, reduce hospital visits, and lower hospitalization rates compared to traditional follow-up.

Primary Objective:

Compare overall patient satisfaction between dematerialized post-emergency follow-up at home using a health app and traditional in-person follow-up at the hospital seven days after the initial consultation. Satisfaction will be assessed on a scale from 0 to 10, considering symptom management, follow-up quality, and task performance.

Secondary Objectives:

1. Compare satisfaction at 28 days.
2. Evaluate PROM-ED scores for symptom relief, understanding, reassurance, and care planning at days 7 and 28.
3. Assess hospitalization rates by day 28.
4. Assess unscheduled emergency consultations during the study.
5. Assess surgical intervention rates by day 28.
6. Assess total hospital time by day 28.
7. Evaluate physician satisfaction at 28 days.

Design: Randomized superiority trial involving women aged 18-60 consulting for specific gynecological conditions requiring post-emergency follow-up. Inclusion criteria are women aged 18-60 needing specified follow-up. Exclusion criteria are severe clinical intolerance, significant ultrasound findings, non-French speakers, and inability to use digital applications.

Intervention Group: Uses a connected health app for data collection and communication, logging symptoms and test results, with medical team notifications for urgent updates. Patients will record vital signs at nearby pharmacies and log symptoms like pain, bleeding, and vomiting.

Control Group: Receives traditional in-person follow-up with scheduled consultations and emergency contact information.

Data Collection and Randomization: Patients will be randomized into either the intervention or control group. Data will be collected via secure digital platforms, with follow-up evaluations at days 7 and 28. Clinical data will be pseudonymized and securely stored.

Expected Benefits: Improved patient coordination and care efficiency, reduced hospital visits, enhanced access to care, and reduced healthcare costs. The study aims to validate the feasibility and effectiveness of digital health solutions in post-emergency gynecological care. If successful, this could lead to broader adoption of such technologies in various medical fields.

Risks: The study poses minimal risks as it includes patients with stable conditions and ensures continuous monitoring through digital platforms with emergency response capabilities.

Participants: 200 women (100 per group) will be included, based on previous satisfaction scores. Inclusion criteria ensure patient stability and ability to use the digital platform.

Duration: Inclusion will last 24 months, with a 28-day follow-up period for each patient.

Statistical Analysis: Conducted by the statistical team led by Prof. Pierre-Yves Ancel at Université de Paris.

Funding: Private funding by NABLA (40%) and public funding by BPI (60%).

Conditions

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Genital Diseases, Female Ectopic Pregnancy Uncertain Viability of Pregnancy Abortion, Missed Hyperemesis Gravidarum

Study Design

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Allocation Method

RANDOMIZED

Intervention Model

PARALLEL

Primary Study Purpose

SUPPORTIVE_CARE

Blinding Strategy

NONE

Study Groups

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Traditional In-Person Follow-Up

Participants in this arm will receive traditional in-person follow-up at the hospital. They will have scheduled consultations at the emergency department where they will undergo necessary evaluations and receive instructions for follow-up visits. Contact information for urgent needs will also be provided, allowing patients to reach medical services 24/7 if required

Group Type NO_INTERVENTION

No interventions assigned to this group

Dematerialized Follow-Up

Participants in this arm will receive post-emergency gynecological follow-up at home using a connected health app.

Group Type EXPERIMENTAL

Participants will receive post-emergency gynecological follow-up at home using a connected health app

Intervention Type DEVICE

The app will facilitate data collection and communication, including logging of vital signs, symptoms (such as pain, bleeding, and vomiting), and uploading of test results. Medical professionals will review the health data twice daily and provide care instructions through instant messaging. The app will also alert the medical team of any urgent updates or deviations from expected health patterns

Interventions

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Participants will receive post-emergency gynecological follow-up at home using a connected health app

The app will facilitate data collection and communication, including logging of vital signs, symptoms (such as pain, bleeding, and vomiting), and uploading of test results. Medical professionals will review the health data twice daily and provide care instructions through instant messaging. The app will also alert the medical team of any urgent updates or deviations from expected health patterns

Intervention Type DEVICE

Eligibility Criteria

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Inclusion Criteria

* Women aged ≥ 18 and ≤ 60 years
* First-time consultation at gynecological emergencies within the past three months
* Requires post-emergency follow-up for one of the following clinical conditions:

* Genital Infections (IGH): Acute pelvic pain with leucorrhea requiring antibiotic treatment
* Ectopic Pregnancy (GEU): Non-complicated, not ruptured, no significant effusion on ultrasound, GEU \< 4 cm, no cardiac activity if embryo present, stable hemodynamic state, HCG \< 5000, compliant patient, no contraindications for Methotrexate
* Early Pregnancy of Uncertain Viability (GLI): Early stage with a positive bHCG test, no visible gestational sac on ultrasound, no effusion, bHCG \< 1500 IU
* Missed Miscarriage (GAM): Confirmed by an intrauterine gestational sac ≥ 25mm without an embryo or an intrauterine gestational sac with an embryo ≥ 7mm without visible cardiac activity, treated with Misoprostol
* First Trimester Bleeding (MTRt1): Moderate vaginal bleeding with an ongoing intrauterine pregnancy without hemodynamic instability
* Hyperemesis Gravidarum (VG): Severe vomiting before 12 weeks of pregnancy without severe criteria necessitating emergency follow-up

Exclusion Criteria

* Severe clinical intolerance, defined by:

* Signs of severe sepsis with unstable hemodynamics (TA \< 100/85 mmHg and FC ≥ 100 bpm)
* Abdominal defense or contracture
* Pain not controlled by level 1 analgesics (EVA \> 7)
* Severe dehydration defined by \> 10% body weight loss
* Total food intolerance due to vomiting necessitating hospitalization
* Uncontrolled active hemorrhage
* Significant ultrasound findings:

* Tubo-ovarian abscess ≥ 3 cm
* Significant intra-abdominal effusion
* Non-French speaking patients
* Inability to download or use the digital application due to precarious or isolated digital circumstances
* Refusal to sign consent form
* Patients under legal guardianship or court protection (tutelage, curatorship, or guardianship)
* Patients not affiliated with a social security system
Minimum Eligible Age

18 Years

Maximum Eligible Age

60 Years

Eligible Sex

FEMALE

Accepts Healthy Volunteers

No

Sponsors

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Assistance Publique - Hôpitaux de Paris

OTHER

Sponsor Role lead

Responsible Party

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Responsibility Role SPONSOR

Principal Investigators

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Louis MARCELLIN, MD, PhD

Role: STUDY_DIRECTOR

Assistance Publique - Hôpitaux de Paris

Laetitia CAMPIN, MD

Role: PRINCIPAL_INVESTIGATOR

Assistance Publique - Hôpitaux de Paris

Locations

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Hôpital Cochin

Paris, , France

Site Status

Countries

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France

Central Contacts

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Laetitia CAMPIN, MD

Role: CONTACT

33 015413680

Valérie PLENCE

Role: CONTACT

33 0158411178

Facility Contacts

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Laetitia CAMPAIN, MD, PhD

Role: primary

33+0158413680

Other Identifiers

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2023-A02336-39

Identifier Type: REGISTRY

Identifier Source: secondary_id

APHP230586

Identifier Type: -

Identifier Source: org_study_id

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