Background: Professional drivers' work under conditions predisposes them for development of sleep-disordered breathing (SDB) and cardiovascular disease (CVD). However, the effect of SDB on CVD risk among professional drivers has never been investigated. A cohort study was used to evaluate the effectiveness of overnight pulse oximeter as a sleep apnea screening tool to assess the 8-year risk of CVD events.
Methods: The Taiwan Bus Driver Cohort Study (TBDCS) recruited 1014 professional drivers in Taiwan since 2005. The subjects completed questionnaire interview and overnight pulse oximeter survey. This cohort was linked to the National Health Insurance Research Dataset (NHIRD). Researchers found 192 CVD cases from 2005 to 2012. Cox proportional hazards model was performed to estimate the hazard ratio for CVD. The statistical analysis was performed using SAS software in 2015.
Results: ODI4 and ODI3 levels increased the 8-year CVD risk, even adjusting for CVD risk factors (HR: 1.36, 95% CI: 1.05 to 1.78; p=0.022, and HR: 1.40, 95% CI: 1.03 to 1.90; p=0.033). ODI4 and ODI3 thresholds of 6.5 and 10events/h revealed differences of CVD risks (HR: 1.72, 95% CI: 1.00 to 2.95; p=0.048, and HR: 1.76, 95% CI: 1.03 to 3.03; p=0.041). Moreover, the ODI levels had an increased risk for hypertensive disease (not including essential hypertension).
Conclusions: This study concludes that ODI for a sign of SDB is an independent predictor of elevated risk of CVD. Further research should be conducted regarding measures to prevent against SDB in order to reduce CVD risk in professional drivers.
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http://dx.doi.org/10.1016/j.ijcard.2016.09.110 | DOI Listing |
BMC Res Notes
January 2025
UQ Centre for Clinical Research, Faculty of Health Medicine and Behavioural Sciences, The University of Queensland, Brisbane, Australia.
Objectives: This data note presents a comprehensive geodatabase of cardiovascular disease (CVD) hospitalizations in Mashhad, Iran, alongside key environmental factors such as air pollutants, built environment indicators, green spaces, and urban density. Using a spatiotemporal dataset of over 52,000 hospitalized CVD patients collected over five years, the study supports approaches like advanced spatiotemporal modeling, artificial intelligence, and machine learning to predict high-risk CVD areas and guide public health interventions.
Data Description: This dataset includes detailed epidemiologic and geospatial information on CVD hospitalizations in Mashhad, Iran, from January 1, 2016, to December 31, 2020.
Cardiovasc Diabetol
January 2025
Medical Big Data Center, Department of General Medicine, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, No. 26 Daoqian Street, Suzhou, 215001, Jiangsu, China.
Background: Triglyceride-glucose (TyG) related indices, which serve as simple markers for insulin resistance, have been closely linked to metabolic dysfunction-associated steatotic liver disease (MASLD), cardiovascular disease (CVD), and mortality. However, the prognostic utility of TyG-related indices in predicting the risk of CVD and mortality among patients with MASLD remains unclear.
Methods: Data of 97,331 MASLD patients, with a median age of 58.
NPJ Digit Med
January 2025
Department of Medical Informatics, University Medical Center Göttingen, Göttingen, Germany.
Aging affects the 12-lead electrocardiogram (ECG) and correlates with cardiovascular disease (CVD). AI-ECG models estimate aging effects as a novel biomarker but have only been evaluated on single ECGs-without utilizing longitudinal data. We validated an AI-ECG model, originally trained on Brazilian data, using a German cohort with over 20 years of follow-up, demonstrating similar performance (r = 0.
View Article and Find Full Text PDFCMAJ
January 2025
Schools of Health and Wellbeing (Nakada, Pell, Ho), and Cardiovascular and Metabolic Health (Welsh, Celis-Morales), University of Glasgow, Glasgow, UK; Human Performance Laboratory, Education, Physical Activity and Health Research Unit (Celis-Morales), Universidad Católica del Maule, Talca, Chile; Centro de Investigación en Medicina de Altura (CEIMA) (Celis-Morales), Universidad Arturo Prat, Iquique, Chile.
Background: Anxiety and depression are associated with cardiovascular disease (CVD). We aimed to investigate whether adding measures of anxiety and depression to the American Heart Association Predicting Risk of Cardiovascular Disease Events (PREVENT) predictors improves the prediction of CVD risk.
Methods: We developed and internally validated risk prediction models using 60% and 40% of the cohort data from the UK Biobank, respectively.
Prev Med
January 2025
Department of Environmental Health Sciences, Fielding School of Public Health, University of California Los Angeles, Los Angeles, CA, USA; Department of Epidemiology, Fielding School of Public Health, University of California Los Angeles, Los Angeles, USA; School of Nursing, University of California Los Angeles, Los Angeles, CA, USA. Electronic address:
Aims: Cardiovascular disease (CVD) is the leading cause of death in the United States (U.S.).
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