Development and internal validation of a prediction model for rheumatoid arthritis: a case-control study

Area of Science:

• Rheumatology
• Epidemiology
• Genetics

Background:

• Rheumatoid Arthritis (RA) poses a significant health burden.
• Identifying early risk factors is crucial for prevention and management.
• Existing predictive models may not fully capture the multifactorial nature of RA.

Purpose of the Study:

• To identify independent risk factors associated with Rheumatoid Arthritis (RA) pathogenesis.
• To construct and validate a predictive model for RA risk.
• To evaluate the diagnostic accuracy and calibration of the developed model.

Main Methods:

• Logistic regression analysis was employed to identify significant risk factors.
• A predictive model was constructed using identified sociodemographic, lifestyle, dietary, and genetic factors.
• Model performance was assessed using the Area Under the Receiver Operating Characteristic Curve (AUC) and the Hosmer-Lemeshow test.

Main Results:

• Occupation type, residence, mumps history, diet (sweet, fish), dwelling conditions, vaccination history, and the genetic marker rs805297 were significantly associated with RA.
• The predictive model demonstrated high accuracy with an AUC of 0.912 (internal validation AUC 0.942).
• The model showed good sensitivity (0.847) and specificity (0.852), with good calibration (P=0.392).

Conclusions:

• A novel and promising model for identifying individuals at risk of developing Rheumatoid Arthritis has been developed.
• The identified factors provide insights into the pathogenesis of RA.
• This model holds potential for early risk assessment and targeted interventions.