Objective: Cardiovascular disease (CVD) is a major cause of morbidity and mortality in systemic lupus erythematosus (SLE) patients. The aim of this study was to identify factors associated with patients' recognition of SLE as an independent risk factor for CVD and their perception of their personal CVD risk.
Methods: SLE patients were sent questionnaires that assessed demographic characteristics, any CVD risk factors, and information regarding the CVD counseling they had received from their physicians. Variables significantly associated with the outcomes were analyzed using logistic regression models.
Results: Information obtained from 226 questionnaires was analyzed. Fifty-eight percent of the respondents reported receiving no CVD counseling from a physician. Patients who recalled receiving counseling from a physician were 2.3 times more likely (P = 0.02) to recognize SLE as a CVD risk factor and 3.2 times more likely (P = 0.02) to perceive themselves as being at risk of CVD compared with those patients who did not receive physician counseling. Receiving physician CVD counseling was the strongest predictor of a patient's self-perception of CVD risk. Those patients at intermediate to high risk (n = 167) who reported having received counseling were 5.3 times as likely (P = 0.007) to perceive themselves to be at higher risk of CVD compared with similar patients who did not receive counseling. Younger patients were 4.2 times as likely (P = 0.002) as older patients to recognize SLE as a CVD risk factor. Other variables associated with patients' self-perceptions of CVD risk included family history of CVD and hypertension.
Conclusion: Physician counseling regarding CVD in SLE patients has an important impact on patients' awareness of SLE as a CVD risk factor and their self-perception of CVD risk.
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http://dx.doi.org/10.1002/art.23419 | DOI Listing |
BMC Med Imaging
December 2024
Department of Radiology, Cardiothoracic Imaging, University of Utah, 30 N 1900 E #1A71, Salt Lake City, Utah, 84132, USA.
Background: Lung cancer is a leading cause of cancer-related mortality worldwide, with non-small cell lung cancer (NSCLC) comprising 85% of cases. Due to the lack of early clinical signs, metastasis often occurs before diagnosis, impacting treatment and prognosis. Cardiovascular disease (CVD) is a common comorbidity in lung cancer patients, with shared risk factors exacerbating outcomes.
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Department of Public Health and Informatics, Jahangirnagar University, Savar, Dhaka, 1342, Bangladesh.
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Department of Oncology, Liyuan Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, P.R. China.
Background: Cardiometabolic index (CMI) is a comprehensive clinical parameter which integrates overweight and abnormal lipid metabolism. However, its relationship with all-cause, cardiovascular disease (CVD), and cancer mortality is still obscure. Thus, a large-scale cohort study was conducted to illustrate the causal relation between CMI and CVD, cancer, and all-cause mortality among the common American population.
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December 2024
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The triglyceride-glucose (TyG) index and the Atherogenic Index of Plasma (AIP) are both predictors of cardiovascular diseases (CVD). However, their combined and individual contributions to CVD risk are not well understood. This study evaluate the joint and individual associations of the TyG index and AIP with CVD events in middle-aged and older Chinese adults.
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December 2024
The Key Laboratory for Computer Systems of State Ethnic Affairs Commission, School of Computer and Artificial Intelligence, Southwest Minzu University, Chengdu, 610041, China.
Coronary artery disease represents a formidable health threat to middle-aged and elderly populations worldwide. This research introduces an advanced BP neural network algorithm, EPSOSA-BP, which integrates particle swarm optimization, simulated annealing, and a particle elimination mechanism to elevate the precision of heart disease prediction models. To address prior limitations in feature selection, the study employs single-hot encoding and Principal Component Analysis, thereby enhancing the model's feature learning capability.
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