Clinical, Functional, and Therapeutic Characterization of Women With Urinary Incontinence and Analysis of Factors Associated With Treatment Response

NCT07887464 | Not Yet Recruiting FDA Device

• 1 other identifier: 2026/271
• Study Type: observational
• Target: 80
• Locations: 1 country
• Sites: 1

Brief Summary

The goal of this observational retrospective study is to characterize women with urinary incontinence and to identify factors associated with their response to treatment using artificial intelligence models. The main questions it aims to answer are: What are the clinical, functional, and therapeutic characteristics of women with urinary incontinence? Which clinical, functional, and lifestyle factors are associated with response to treatment? Can artificial intelligence models identify patterns and factors associated with treatment response? Researchers will analyze data from women with urinary incontinence collected during routine clinical care at a specialized pelvic floor clinic. The study will combine clinical information from medical records, measurements obtained from clinical assessment instruments, and daily-life data collected using wearable devices such as activity bracelets. Artificial intelligence and machine learning models will be applied to these data to characterize participants and identify factors associated with treatment response.

Conditions

Urinary Incontinence (UI)

Keywords

urinary incontinence; Artificial inteligence

Outcome Measures

Primary Outcomes (1)

• Severity of urinary incontinence (ICIQ-UI SF score)

  Total score on the ICIQ-UI SF (International Consultation on Incontinence Questionnaire - Urinary Incontinence Short Form), range 0-21, with higher scores indicating greater severity. Treatment response will be defined as an improvement ≥2.52 points (MCID).

  Baseline (V0) through 12 months (V0, V1 at 3 months, V2 at 6 months, V3 at 12 months)

Secondary Outcomes (15)

• Overactive bladder symptoms (ICIQ-OAB)

  V0(Day 1), V1 (3 months), V2 (6 months), V3 (12 months)

• Incontinence severity - Sandvik Index (ISI)

  V0(Day 1) and V2 (6 months)

• Lower urinary tract symptom-specific quality of life (ICIQ-LUTSqol)

  V0(Day 1), V1 (3 months), V2 (6 months), V3 (12 months)

• Patient Global Impression of Severity (PGI-S)

  V0 (baseline)

• Patient Global Impression of Improvement (PGI-I)

  V1 (3 months), V2 (6 months), V3 (12 months)

Study Arms (1)

Women with urinary incontinence

Women with urinary incontinence whose clinical, functional, therapeutic, and daily-life data are analyzed retrospectively. Data include information from medical records, clinical assessment instruments, and wearable devices such as activity bracelets. Artificial intelligence and machine learning models will be applied to these data to identify factors associated with treatment response.

Other: Artificial intelligence clinical decision support model

Interventions

Artificial intelligence clinical decision support model OTHER

Development and evaluation of an artificial intelligence-based clinical decision support model for women with urinary incontinence. The model will integrate clinical data obtained during routine care, measurements from clinical assessment instruments, patient-reported information from questionnaires, and daily-life data collected through wearable devices. The model is intended to identify patterns and factors associated with treatment response and to provide information that may support clinicians in the assessment and management of women with urinary incontinence. The study does not assign or modify the treatment received by participants, which is provided as part of routine clinical care.

Women with urinary incontinence

Eligibility Criteria

• Age: 18 Years+
• Gender Eligibility Details: Female
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Women aged 18 years or older with a diagnosis of urinary incontinence who receive routine care at the Clínica Cobián. Participants will provide clinical, functional, therapeutic, questionnaire, and daily-life data, including information collected through wearable devices, for the development and evaluation of an artificial intelligence-based clinical decision support model.

You may qualify if:

• Women aged 18 years or older.
• Diagnosis of urinary incontinence (stress, urgency, or mixed urinary incontinence).
• Patients attending the Clínica Cobián.
• Willingness to participate in follow-up and complete the required patient-reported outcome measures (PROMs).

You may not qualify if:

• Inability to provide informed consent.
• Severe diseases or clinical conditions that, in the investigator's judgment, may interfere with participation in the study or interpretation of the results.
• Neurological Disorders (Causes of Neurogenic Bladder): Conditions affecting the central or peripheral nervous system can substantially alter bladder control; therefore, urinary incontinence may not be attributable to common mechanical or functional causes, such as stress or standard urgency incontinence.
• Multiple sclerosis (MS). Advanced Parkinson's disease or parkinsonian syndromes. Traumatic or medical spinal cord injuries (paraplegia or tetraplegia). Recent stroke or stroke with severe cognitive or motor sequelae. Severe diabetic autonomic neuropathy associated with detrusor hypotonia or urinary retention.
• Oncological Conditions and Previous Treatments: Pelvic cancer or its treatments may permanently alter pelvic anatomy and bladder tissue function.
• Active urinary tract or gynecological cancer (bladder, prostate, urethra, uterus, or cervix).
• History of pelvic radiotherapy. Current chemotherapy, due to systemic effects and potential bladder toxicity (such as cyclophosphamide).
• Urological and Urogynecological Conditions: Local conditions that may confound the diagnosis of common urinary incontinence or require immediate surgical treatment.
• Severe pelvic organ prolapse (POP), stages III or IV according to the POP-Q classification, as it may cause obstruction or overflow incontinence.
• Urogenital fistulas (vesicovaginal or urethrovaginal), in which urine leakage is anatomical and continuous.
• Urethral diverticula or severe urethral stenosis. Active bladder stones or untreated low-compliance bladder.
• Severe or Unstable Systemic Diseases: Conditions that may pose a risk to the participant's health or adherence to the study, or that may produce symptoms that could be confused with urinary incontinence.
• Decompensated congestive heart failure (CHF), which may cause marked nocturnal polyuria and edema, interfering with voiding diaries and assessment of nocturia.
• Advanced chronic kidney disease (estimated glomerular filtration rate \[eGFR\] \<30 mL/min/1.73 m² or requiring dialysis).
• Uncontrolled diabetes mellitus (HbA1c \>8.5% or 9%), due to osmotic polyuria that may mask actual urinary frequency and urgency.

Sponsors & Collaborators

• Javier Pereira: lead
• Universidade da Coruña: collaborator
• Ministry of Work and Welfare - Xunta de Galicia: collaborator

Study Sites (1)

Clínica Cobian

A Coruña, La Coruña, 15009, Spain

Location

MeSH Terms

Conditions

Urinary Incontinence

Condition Hierarchy (Ancestors)

Urination Disorders; Urologic Diseases; Female Urogenital Diseases; Female Urogenital Diseases and Pregnancy Complications; Urogenital Diseases; Male Urogenital Diseases; Lower Urinary Tract Symptoms; Urological Manifestations; Signs and Symptoms; Pathological Conditions, Signs and Symptoms

Central Study Contacts

Javier P Loureiro, computer science

CONTACT

881 01 55 21; javier.pereira@udc.es

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Target Duration: 12 Months
• Sponsor Type: OTHER
• Responsible Party: SPONSOR INVESTIGATOR
• PI Title: Principal Investigator

Study Record Dates

• First Submitted: September 17, 2026
• First Posted: October 21, 2026
• Study Start (Estimated): December 1, 2026
• Primary Completion (Estimated): December 1, 2027
• Study Completion (Estimated): September 30, 2028
• Last Updated: October 21, 2026

Record last verified: 2026-10

Data Sharing

• IPD Sharing: Will not share

Locations

ES (1)