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

Risk factors and prediction models for cardiovascular complications of hypertension in older adults with machine learning: A cross-sectional study

Background

Hypertension has emerged as a chronic disease prevalent worldwide that may cause severe cardiovascular complications, particularly in older patients. However, there is a paucity of studies that use risk factors and prediction models for cardiovascular complications associated with hypertension in older adults.

Objectives

To identify the risk factors and develop prediction models for cardiovascular complications among older patients with hypertension.

Methods

A convenience sample of 476 older patients with hypertension was recruited from a university-affiliated hospital in China. Demographic data, clinical physiological indicators, regulatory emotional self-efficacy, medication adherence, and lifestyle information were collected from participants. Binary logistic regression analysis was performed to screen for preliminary risk factors associated with cardiovascular complications. Two machine learning methods, Back-Propagation neural network, and random forest were applied to develop prediction models for cardiovascular complications among the study cohort. The sensitivity, specificity, accuracy, receiver operating characteristic curve, and area under the curve (AUC) values were used to assess the performance of the prediction models.

Results

Binary logistic regression identified nine risk factors for cardiovascular complications among older patients with hypertension. The machine learning models displayed excellent performance in predicting cardiovascular complications, with the random forest model (AUC 0.954) outperforming the Back-Propagation neural network model (AUC 0.811), as confirmed by model comparison analysis. The sensitivity, specificity and accuracy of the Back-Propagation neural network model compared to the random forest model were 74.2% vs. 86.5%, 75.2% vs. 94.3%, and 74.7% vs. 90.4%, respectively.

Conclusion

The machine learning methods employed in this study demonstrated feasibility in predicting cardiovascular complications among older patients with hypertension, with the random forest model based on nine risk factors exhibiting excellent prediction performance. These models could be used to identify high-risk populations and suggest early interventions aimed at preventing cardiovascular complications in such cohorts.

Development and Validation of a Coronary Heart Disease Risk Prediction Model in Snorers with Hypertension: A Retrospective Observed Study

Purpose

To develop and validate a risk prediction model for coronary heart disease (CHD) in snorers with hypertension, including traditional and new risk factors.

Patients and Methods

Twenty factors were evaluated in the records of 2810 snorers with hypertension. Training (70%) and validation (30%) sets were created by random allocation of data, and a new nomogram model was developed. The model's discrimination and calibration were measured by calculating the area under the receiver operating curve (AUC) and creating calibration charts. The performance of the nomogram model was compared with that of the Prediction for ASCVD Risk in China (China-PAR) and Framingham models by decision curve analysis. An optimal cutoff point for the risk score in the training set was computed to stratify patients.

Results

In the nomogram model, the AUCs for predicting CHD at 5, 7 and 9 years in the training set were 0.706 (95% confidence interval [CI] 0.649-0.763), 0.703 (95% CI 0.655-0.751) and 0.669 (95% CI 0.593-0.744), respectively. The respective AUCs were 0.682 (95% CI 0.607-0.758), 0.689 (95% CI 0.618-0.760) and 0.664 (95% CI 0.539-0.789) in the validation set. The calibration chart showed that the predicted events from the nomogram score were close to the observed events. Decision curve analysis indicated that the nomogram score was slightly better than the Prediction for ASCVD Risk in China (China-PAR) and Framingham models for predicting the risk of CHD in snorers with hypertension. A cutoff point was identified for being CHD-free (a nomogram score of ≤121), which could be helpful for the early identification of individuals at high-risk of CHD.

Conclusion

The nomogram score predicts the risk probability of CHD in snorers with hypertension at 5, 7 and 9 years, and shows good capability in terms of discrimination and calibration. It may be a useful tool for identifying individuals at high risk of CHD.