Risk factors and predictive models for frozen shoulder

Area of Science:

• Orthopedics
• Epidemiology
• Medical Diagnostics

Background:

• Frozen shoulder (FS), also known as adhesive capsulitis, is a debilitating condition characterized by shoulder stiffness and pain.
• Early detection and risk factor identification are crucial for effective management and prevention of FS.

Purpose of the Study:

• To identify significant risk factors associated with the development of frozen shoulder (FS).
• To develop and validate a predictive model for the early diagnosis of FS.

Main Methods:

• A case-control study involving 103 FS patients and 309 controls.
• Statistical analyses included Mann-Whitney U test, t-test, chi-square test, and multivariate binary logistic regression.
• Receiver operating characteristic (ROC) curve analysis was used to evaluate diagnostic efficacy.

Main Results:

• Low body mass index (BMI), diabetes, cervical spondylosis, and hyperlipidemia were identified as significant risk factors for FS.
• The developed predictive model demonstrated a superior area under the curve (0.787) compared to individual indicators.
• The model achieved a sensitivity of 62.1% and specificity of 82.2%.

Conclusions:

• Low BMI, diabetes, cervical spondylosis, and hyperlipidemia are significant risk factors for frozen shoulder.
• The developed predictive model offers enhanced diagnostic capability for early FS detection.
• This model can provide valuable insights for clinical practice in identifying individuals at high risk for FS.