Hypertension Center of People's Hospital of Xinjiang Uygur Autonomous Region.
Int Heart J. 2023 Nov 30;64(6):970-978. doi: 10.1536/ihj.23-384. Epub 2023 Nov 14.
Hypertensive patients with snoring and elevated plasma homocysteine levels are common. When these factors are combined, the risk of coronary heart disease (CHD) is high. Herein, we developed and validated an easy-to-use nomogram to predict high-risk CHD in snoring hypertensive patients with elevated plasma homocysteine.Snoring patients (n = 1,962) with hyperhomocysteinemia and hypertension were divided into training (n = 1,373, 70%) and validation (n = 589, 30%) sets. We extracted CHD predictors using multivariate Cox regression analysis, then constructed a nomogram model. Internal validation using 1,000 bootstrap resampling was performed to assess the consistency and discrimination of the predictive model using the area under the receiver operating characteristic curve (AUC) and calibration plots.We constructed a nomogram model with the extracted predictors, including age, waist-height ratio, smoking, and low-density lipoprotein cholesterol levels. The AUCs of the training and validation cohorts at 80 months were 0.735 (95% CI: 0.678-0.792) and 0.646 (95% CI: 0.547-0.746), respectively. The consistency between the observed CHD survival and the probability of CHD survival in the training and validation sets was acceptable based on the calibration plots. A total of more than 151 points in the nomogram can be used in the identification of high-risk patients for CHD among snoring hypertensive patients with elevated plasma homocysteine.We developed a CHD risk prediction model for snoring hypertension patients with hyperhomocysteinemia. Our findings provide a useful clinical tool for the rapid identification of high-risk CHD at an early stage.
打鼾且血浆同型半胱氨酸水平升高的高血压患者很常见。当这些因素结合在一起时,患冠心病(CHD)的风险很高。在此,我们开发并验证了一种易于使用的列线图,以预测打鼾伴高血浆同型半胱氨酸的高血压患者发生高危 CHD 的风险。
将患有高同型半胱氨酸血症和高血压的打鼾患者(n = 1962)分为训练集(n = 1373,70%)和验证集(n = 589,30%)。我们使用多变量 Cox 回归分析提取 CHD 预测因素,然后构建列线图模型。使用 1000 次 bootstrap 重采样进行内部验证,通过接收者操作特征曲线(AUC)下的面积和校准图评估预测模型的一致性和区分度。
我们使用提取的预测因素构建了一个列线图模型,包括年龄、腰高比、吸烟和低密度脂蛋白胆固醇水平。训练队列和验证队列在 80 个月时的 AUC 分别为 0.735(95%CI:0.678-0.792)和 0.646(95%CI:0.547-0.746)。基于校准图,训练和验证组中观察到的 CHD 生存率与 CHD 生存率概率之间的一致性是可以接受的。列线图中总得分超过 151 分可用于识别打鼾伴高血浆同型半胱氨酸的高血压患者中 CHD 的高危患者。
我们开发了一种用于打鼾伴高同型半胱氨酸血症的高血压患者的 CHD 风险预测模型。我们的研究结果为早期快速识别高危 CHD 提供了一种有用的临床工具。