Purpose: Early detection and management of coronary heart disease (CHD) are embedded into many community health service and primary care practices in western countries. The Framingham CHD risk score has been used to predict CHD and mortality for nearly 20 years, and it has predicted CHD event risk accurately in multiethnic populations. The aim of this study was to access the effect of a 6-month community-based intervention on CHD risk in individuals at high risk.
Methods: A randomized controlled trial of individuals with a high 10-year CHD risk were recruited from two communities in China. Individuals in the intervention group (n = 53) received a 3-month group education and a 3-month coaching session. Physical examination and self-report questionnaires were used to collect both pre- and postintervention data on blood pressure, glucose, cholesterol, body mass index, smoking, depression, and health-related quality of life (HRQoL).
Results: A total of 102 participants (85.0%) completed the 6-month study. Compared with the usual care group, the intervention group had a 5 mmHg greater reduction in systolic blood pressure (t = 2.01, p = .047), larger declines in glucose (t = -2.49, p = .015), cholesterol (t = -2.44, p = .017), body mass index (t = -2.58, p = .011), and depression (t = -2.05, p = .043), and better reports of HRQoL (t = 3.36, p = .001). No significant group differences in smoking behaviors were reported.
Conclusion: A 6-month community-based intervention in a CHD high-risk population improved disease-related risk factors, depression, and HRQoL. Results provide preliminary evidence for primary prevention of cardiovascular disease risk in a community high-risk population.
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http://dx.doi.org/10.1016/j.anr.2017.07.004 | DOI Listing |
Background: Coronary heart disease (CHD) and depression frequently co-occur, significantly impacting patient outcomes. However, comprehensive health status assessment tools for this complex population are lacking. This study aimed to develop and validate an explainable machine learning model to evaluate overall health status in patients with comorbid CHD and depression.
View Article and Find Full Text PDFFront Cardiovasc Med
January 2025
Department of Nursing, School of Medical and Health Engineering, Changzhou University, Changzhou, Jiangsu, China.
Background: Coronary atherosclerotic heart disease (coronary heart disease; CHD) is the leading cause of death in women worldwide, and the number of patients and deaths is increasing each year. Approximately 3.8 million women die from CHD every year globally.
View Article and Find Full Text PDFFront Med (Lausanne)
January 2025
Department of Emergency, Wuhan Fourth Hospital, Wuhan, Hubei, China.
Background: At present, the relationship among inflammatory markers [monocytes/HDL-c (MHR), neutrophils/HDL-c (NHR) and lymphocytes/HDL-c (LHR)] and long-term prognosis of coronary heart disease (CHD) is still unclear. Therefore, this study explores the relationship between inflammatory indicators and the risk of long-term major adverse cardiovascular events (MACE) in elderly patients with CHD.
Methods: A retrospective analysis was conducted on 208 elderly patients who underwent coronary angiography at Wuhan Fourth Hospital from August 2022 to August 2023.
BMC Med Genomics
January 2025
Department of Cardiovascular Surgery, Gansu Provincial Hospital, No. 204, Donggang West Road, Lanzhou City, Gansu Province, 730000, China.
Background: We did this study to better clarify the correlations of methylenetetrahydrofolate dehydrogenase 1 (MTHFD1)-G1958A (rs2236225) gene polymorphism with the risk of congenital heart diseases (CHD) and its subgroups.
Methods: Relevant articles were searched in PubMed, Web of Science, Cochrane Library, Embase, CNKI, VIP database and Wanfang DATA until October 2023. We will use odds ratios (ORs) and 95% confidence intervals (CIs) to examine the potential associations of MTHFD1- G1958A gene polymorphism with CHD and its subgroups.
Med Phys
January 2025
Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, China.
Background: Online adaptive radiotherapy (OART) and rapid quality assurance (QA) are essential for effective heavy ion therapy (HIT). However, there is a shortage of deep learning (DL) models and workflows for predicting Monte Carlo (MC) doses in such treatments.
Purpose: This study seeks to address this gap by developing a DL model for independent MC dose (MCDose) prediction, aiming to facilitate OART and rapid QA implementation for HIT.
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