Why the study?
The study was conducted to develop and validate a nomogram to predict coronary heart disease in patients with rheumatoid arthritis in northern China.
Does a novel nomogram improve the prediction of coronary heart disease compared to the Framingham Risk Score in patients with rheumatoid arthritis?
Does a novel nomogram improve the prediction of coronary heart disease compared to the Framingham Risk Score in patients with rheumatoid arthritis?
A newly developed nomogram incorporating clinical and laboratory variables provides better individualized prediction of coronary heart disease than the Framingham Risk Score in patients with rheumatoid arthritis.
May aid CHD risk stratification in RA; leaves open external validation and prospective testing.
We developed and validated a nomogram to predict coronary heart disease (CHD) in patients with rheumatoid arthritis (RA) in northern China. We analyzed a cohort of RA patients admitted to the Department of Rheumatology and Immunology of the First Affiliated Hospital of China Medical University from 2011 to 2017. To select a high-performance model for clinical data prediction, we evaluated the F1-scores of six machine learning models. Based on the results, we selected multivariable logistic regression analysis for the development of a prediction model. We then generated an individualized prediction nomogram that included age, sex, hypertension, anti-cyclic citrullinated peptide antibody positivity, the erythrocyte sedimentation rate, and serum LDL-cholesterol, triglyceride and HDL-cholesterol levels. The prediction model exhibited better discrimination than the Framingham Risk Score in predicting CHD in RA patients. The area under the curve of the prediction model was 0.77, with a sensitivity of 63.9% and a specificity of 77.2%. The nomogram exhibited good calibration and clinical usefulness. In conclusion, our prediction model was more accurate than the Framingham Risk Score in predicting CHD in RA patients. Our nomogram combining various risk factors can be used for the individualized preoperative prediction of CHD in patients with RA.
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Wei et al. (2020) studied this question.
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