Development and validation of a nomogram for predicting stroke risk in rheumatoid arthritis patients

Area of Science:

• Rheumatology
• Cardiovascular Epidemiology
• Medical Informatics

Background:

• Rheumatoid arthritis (RA) is associated with an increased risk of stroke.
• Accurate prediction of stroke risk is crucial for managing RA patients.
• Existing risk models may not fully capture stroke risk in specific populations like those in northern China.

Purpose of the Study:

• To develop and validate a predictive nomogram for stroke risk in rheumatoid arthritis patients.
• To compare the performance of the developed nomogram against the established Framingham risk model.
• To identify key clinical and laboratory factors contributing to stroke risk in RA.

Main Methods:

• Development and validation of a predictive nomogram using logistic regression.
• Inclusion of demographic, clinical, laboratory, and traditional cardiovascular risk factors.
• Performance evaluation using calibration, decision curve analysis, and discrimination metrics (AUC, NRI, IDI).

Main Results:

• Logistic regression demonstrated high performance for stroke risk prediction.
• The nomogram incorporated factors including sex, age, blood pressure, inflammatory markers (CRP, ESR), lipid profiles, and comorbidities.
• The developed nomogram showed superior accuracy and clinical utility compared to the Framingham risk model.

Conclusions:

• The validated nomogram provides an accurate and individualized method for predicting stroke risk in RA patients.
• This tool can aid clinicians in preoperative risk stratification and patient management.
• Further research may explore external validation and broader clinical implementation.