Development and Validation of a Nomogram Model to Predict Coronary Heart Disease in Snoring Patients With Hypertension and Non-glucose Metabolism Disorders:

Zhen  Wei; Menghui  Wang; Xiaoguang  Yao; Liang  Ming; Xintian  Cai; Nanfang Li

doi:10.5937/jomb0-57804

Zhen Wei Hypertension Center of People’s Hospital of Xinjiang Uygur Autonomous Region
Menghui Wang Hypertension Center of People’s Hospital of Xinjiang Uygur Autonomous Region
Xiaoguang Yao Hypertension Center of People’s Hospital of Xinjiang Uygur Autonomous Region
Liang Ming Department of rehabilitation medicine of People’s Hospital of Xinjiang Uygur Autonomous Region
Xintian Cai Hypertension Center of People’s Hospital of Xinjiang Uygur Autonomous Region
Nanfang Li Hypertension Center of People’s Hospital of Xinjiang Uygur Autonomous Region

DOI: https://doi.org/10.5937/jomb0-57804

Keywords: snoring, hypertension, non-glucose metabolism, coronary heart disease, apnea index, nomogram

Abstract

Background: We developed and validated a nomogram to predict the risk of coronary heart disease in hypertensive patients who snore, excluding those with glucose metabolism disorders.

Methods: Records from 2105 snoring patients with hypertension and non-glucose metabolism disorders. A random grouping technique was utilized to split the data into validation and derivation datasets (split ratio = 0.7: 0.3). Least absolute shrinkage and selection operator regression was applied to select predictors and constructed a nomogram model based on multivariate Cox regression analysis. The discrimination and consistency of the nomogram were evaluated using the area under the receiver operating characteristic curve (AUC), calibration plots, and decision curve analysis (DCA) to assess its performance. We found age, male, waist-to-height ratio (WHtR), low and high-density lipoprotein cholesterol (LDL-C and HDL-C), and apnea index (AI) identified as predictors to generate this nomogram model.

Results: The C-index at 84 months was 0.703 (95% confidence interval 0.653–0.754) in the derivation set and 0.645 (95% confidence interval 0.562–0.728) in the validation set. The nomogram demonstrated good performance in the calibration curve and DCA.

Conclusions: So, our study proposed an effective nomogram model with potential application value for individualized prediction of coronary heart disease outcomes in snoring individuals with hypertension, excluding glucose metabolism disorders. And “AI” was proposed as a novel predictor.

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Development and Validation of a Nomogram Model to Predict Coronary Heart Disease in Snoring Patients With Hypertension and Non-glucose Metabolism Disorders

Predicting Coronary Heart Disease in Hypertensive Snorers with Non-Glucose Disorders: Nomogram Development/Validation

Abstract

References