Develop and validate a nomogram for predicting stroke inrheumatoidarthritis patients by electronic medical record data in northern China

Abstract Background: to develop and validate a serum lipid and inflammatory marker model based on the nomogram for the prediction of stroke risk in rheumatoid arthritis patients.Methods: This study was conducted among 313 rheumatoid arthritis with stroke patients and 1827 rheumatoid arthritis patients divided into develop and validation cohorts from the First Affiliated Hospital of China Medical University during January 2011 to December 2018. Logistic regression analysis was used to create a nomogram of predictive model of stroke risk in rheumatoid arthritis patients, after comparing with other machine algorithms. The performance of the nomogram was evaluated by discrimination, calibration and decision curve analysis, also compared with the Framingham Risk Score in predicting stroke in rheumatoid arthritis patients.Results: the nomogram was performed by logistic regression algorithm, and predictors of which included the stratifications of sex, age, systolic blood pressure, C-reactive protein, erythrocyte sedimentation rate, total cholesterol, low density lipoprotein cholesterol and the distribution of being accompanied with hy-med, diabetes, atrial fibrillation and coronary heart disease history, which exhibited a well goodness fit and a good agreement. The analysis with area under the curve, the net reclassification index, the integrated discrimination improvement and clinical use, suggested that this is an easy-to-use nomogram compared with the Framingham Risk Score.Conclusion: This study presents a risk nomogram that incorporates the traditional risk factors, serum lipids and inflammatory markers which can be used to predict stroke in rheumatoid arthritis patients.