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

ABSTRACT: We developed and validated a nomogram to predict the risk of stroke in patients with rheumatoid arthritis (RA) in northern China. Out of six machine learning algorithms studied to improve diagnostic and prognostic accuracy of the prediction model, the logistic regression algorithm showed high performance in terms of calibration and decision curve analysis. The nomogram included stratifications of sex, age, systolic blood pressure, C-reactive protein, erythrocyte sedimentation rate, total cholesterol, and low-density lipoprotein cholesterol along with the history of traditional risk factors such as hypertensive, diabetes, atrial fibrillation, and coronary heart disease. The nomogram exhibited a high Hosmer-Lemeshow goodness-for-fit and good calibration (P > 0.05). The analysis, inclu

SUBMITTER: Xin F 

PROVIDER: S-EPMC8221354 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature