Department of Clinical Epidemiology and Evidence-based Medicine, The First Affiliated Hospital, China Medical University, Shenyang, China.
Department of Medical Record Management Center, The First Affiliated Hospital, China Medical University, Shenyang, China.
Aging (Albany NY). 2020 Feb 29;12(4):3190-3204. doi: 10.18632/aging.102823.
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.
我们开发并验证了一个列线图，用于预测中国北方类风湿关节炎（RA）患者的冠心病（CHD）。我们分析了 2011 年至 2017 年期间在中国医科大学第一附属医院风湿免疫科就诊的 RA 患者队列。为了选择用于临床数据预测的高性能模型，我们评估了六个机器学习模型的 F1 分数。基于这些结果，我们选择多变量逻辑回归分析来开发预测模型。然后，我们生成了一个个体化的预测列线图，其中包括年龄、性别、高血压、抗环瓜氨酸肽抗体阳性、红细胞沉降率以及血清 LDL-胆固醇、甘油三酯和 HDL-胆固醇水平。与 Framingham 风险评分相比，该预测模型在预测 RA 患者的 CHD 方面具有更好的区分能力。该预测模型的曲线下面积为 0.77，敏感性为 63.9%，特异性为 77.2%。该列线图具有良好的校准度和临床实用性。总之，我们的预测模型在预测 RA 患者的 CHD 方面比 Framingham 风险评分更准确。我们的列线图结合了各种危险因素，可用于 RA 患者 CHD 的个体化术前预测。
