Precision medicine and personalized care in patients with rotator cuff disease: future perspectives and new frontiers using machine learning models
Not Applicable
- Conditions
- Rotator cuff tearsMusculoskeletal Diseases
Recruitment & Eligibility
- Status
- Ongoing
- Sex
- All
- Target Recruitment
- 100
Inclusion Criteria
1. Age 40-75 years
2. Rotator cuff tears documented with MRI
3. No surgical treatment to the affected shoulder before
4. No episodes of shoulder instability
5. No radiographic signs of fracture of the glenoid fossa or the greater or lesser tuberosity
Exclusion Criteria
1. Frozen shoulder
2. Radiological osteoarthritis of the glenohumeral joint
3. Neurological disease or language barriers
4. Impossibility to undergo an MRI scan for any reason
Study & Design
- Study Type
- Interventional
- Study Design
- Not specified
- Primary Outcome Measures
Name Time Method Structural tendon integrity, as evidenced by MRI evaluations at 12 months after surgery
- Secondary Outcome Measures
Name Time Method Measured before and after surgery:<br>1. Kinematic variables (such as range of motion, angular velocity) measured by kinematic analysis<br>2. Physical and subjective measures of the affected shoulder in terms of pain, activities of daily living (ADL), range of motion (ROM), and strength measured by the Constant-Murley score (CMS)<br>3. Patient self-reported and clinician scores about pain, ADL, ROM, signs, strength, and instability measured by the American Shoulder and Elbow Surgeons (ASES) score<br>4. Quality of life and mental health (such as physical and social functioning, general health perception limitations due to emotional aspects, vitality) measured by SF-36<br>5. The level of pain perceived by patients measured by visual analogue score (VAS)