Construction of a risk prediction model for cardiovascular events in community hypertensive patients based on remote ambulatory blood pressure parameters
10.3969/j.issn.1006-2483.2026.03.018
- VernacularTitle:基于远程动态血压参数的社区高血压患者心血管事件风险预测模型构建
- Author:
Guiqiu ZHU
1
;
Yihong WU
2
;
Hao ZHANG
3
;
Jun SUN
3
;
Yajuan ZHANG
3
;
Xiaohong WANG
3
;
Zongquan ZHAO
2
Author Information
1. Cardiovascular Center - Electrocardiography Center, The Affiliated Suzhou Hospital of Nanjing Medical University(Suzhou Municipal Hospital), Suzhou , Jiangsu 215000, China;
2. Department of General Practice, Runda Community Health Service Center Wumenqiao Subdistrict, Gusu District, Suzhou City, Suzhou , Jiangsu 215000, China
3. Department of General Practice, Pingjiang New Town Community Health Service Center Sujin Subdistrict, Gusu District, Suzhou City, Suzhou , Jiangsu 215000, China
- Publication Type:Journal Article
- Keywords:
Community hypertension;
Major adverse cardiovascular events;
Ambulatory blood pressure;
Remote;
Prediction model
- From:
Journal of Public Health and Preventive Medicine
2026;37(3):85-89
- CountryChina
- Language:Chinese
-
Abstract:
Objective To explore the risk prediction model of major adverse cardiovascular events (MACE) in community patients with hypertension based on remote ambulatory blood pressure parameters. Methods From November 2023 to October 2024, 486 community patients with hypertension who received standardized management in Nanjing Medical University Affiliated to Suzhou Hospital were retrospectively selected. All patients wore remote ambulatory blood pressure monitor to obtain 24-hour ambulatory blood pressure data. Clinical data were collected and remote ambulatory blood pressure parameters [24-hour systolic blood pressure variability (SBPV), 24-hour diastolic blood pressure variability (DBPV), nighttime SBPV, nighttime DBPV, daytime SBPV, daytime DBPV] were extracted. The patients were followed up for 12 months, and were classified into MACE group (n=42) and non-MACE group (n=444) according to whether MACE occurred during follow-up. Multivariate Logistic regression analysis was adopted to screen the influencing factors for MACE. Based on the above factors, a risk prediction model was constructed and verified by receiver operating characteristic (ROC) curve. Results MACE occurred in 42 cases among 486 patients, with an incidence rate of 8.64%. Multivariate Logistic regression analysis suggested that nighttime DBPV (OR=1.119, 95%CI: 1.030-1.214), 24h-SBPV (OR=1.115, 95%CI: 1.007-1.235), nighttime SBPV (OR=1.116, 95%CI: 1.016-1.226) and diabetes mellitus (OR=2.762, 95%CI: 1.059-7.203) were independent factors for MACE (P<0.05). The model validation results revealed that the area under the ROC curve was 0.905 (95%CI: 0.854-0.956 ), and the model had a good discrimination degree. Conclusion Nighttime DBPV, 24h-SBPV, nighttime SBPV and diabetes mellitus are independent risk factors for MACE in community patients with hypertension. The clinical prediction model based on these variables exhibits certain predictive value on MACE risk.
