AUTHOR=Wang Meng-hui , Heizhati Mulalibieke , Li Nan-fang , Yao Xiao-guang , Luo Qin , Lin Meng-yue , Hong Jing , Ma Yue , Wang Run , Sun Le , Ren Ying-li , Yue Na 

TITLE=Development and Validation of a Prognostic Model to Predict High-Risk Patients for Coronary Heart Disease in Snorers With Uncontrolled Hypertension

JOURNAL=Frontiers in Cardiovascular Medicine

VOLUME=Volume 9 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2022.777946

DOI=10.3389/fcvm.2022.777946

ISSN=2297-055X

ABSTRACT=Purpose: Snoring or obstructive sleep apnea, with or without uncontrolled hypertension, are common and significantly increase the risk of coronary heart disease (CHD). The aim of this study was to develop and validate a prognostic model to predict and identify high-risk patients for CHD among snorers with uncontrolled hypertension. 
Methods: Records from 1822 snorers with uncontrolled hypertension were randomly divided into a training set (n=1275, 70%) and validation set (n=547, 30%). Predictors for CHD were extracted to construct a nomogram model based on Cox regression analysis in the training set. We performed internal validation to assess discrimination and consistency of the prediction model using area under the receiver operating characteristic curve and calibration plots. Based on linear predictors, a risk classifier for CHD could be set. 
Results: Age, waist circumference, and high- and low-density lipoprotein cholesterol were extracted as predictors to generate this nomogram model. The area under the receiver operating characteristic curve was 0.757 (95% confidence interval 0.626–0.887), 0.739 (0.647–0.831), and 0.732 (0.665–0.799) in the training set and 0.689 (0.541–0.837), 0.700 (0.606–0.796), and 0.711 (0.615–0.808) in the validation set at 3, 5, and 8 years, respectively. The calibration plots showed acceptable consistency between the probability of CHD-free survival and the observed CHD-free survival in the training and validation sets. A total of more than 134 points in the nomogram can be used in the identification of high-risk patients for CHD among snorers with uncontrolled hypertension.
Conclusions: We developed a CHD risk prediction model in snorers with uncontrolled hypertension, which can help clinicians with early and quick identification of patients with a high risk for CHD.