Background There is a lack of data on modifiable coronary artery disease (CAD) risk factors in the Indonesian population, hindering the implementation of assessments and prevention programs in this population. This study investigated modifiable risk factors for CAD among Indonesians by comparing them between CAD-proven patients and healthy subjects from a similar population.
Methods In this nested, matched case-control study, the cases were patients from a referral hospital in Yogyakarta, Indonesia and the controls were respondents in a population surveillance system in Yogyakarta, Indonesia. The cases were 421 patients who had undergone coronary angiography, showing significant CAD. The sex- and age-matched controls were 842 respondents from the Universitas Gadjah Mada Health and Health and Demographic Surveillance System Sleman who indicated no CAD presence on a questionnaire. The modifiable CAD risk factors compared between cases and controls were diabetes mellitus, hypertension, central obesity, smoking history, physical inactivity, and less fruit and vegetable intake. A multivariate regression model was applied to determine independent modifiable risk factors for CAD, expressed as adjusted odds ratios (AORs).
Results A multivariate analysis model of 1,263 subjects including all modifiable risk factors indicated that diabetes mellitus (AOR, 3.32; 95% confidence interval [CI], 2.09–5.28), hypertension (AOR, 2.52; 95% CI, 1.76–3.60), former smoking (AOR, 4.18; 95% CI, 2.73–6.39), physical inactivity (AOR, 15.91; 95% CI, 10.13–24.99), and less fruit and vegetable intake (AOR, 5.42; 95% CI, 2.84–10.34) independently and significantly emerged as risk factors for CAD.
Conclusions Hypertension, diabetes mellitus, former smoking, physical inactivity, and less fruit and vegetable intake were independent and significant modifiable risk factors for CAD in the Indonesian population.
The incidence of obesity is increasing throughout the world, including Korea. Liraglutide, the main purpose of which is glucose control, has recently gained significant attention due to its additional effect on weight loss. Liraglutide injections have been widely used as an important treatment for obese patients in Korea. In addition to weight loss, liraglutide has various other effects, such as prevention of cardiovascular disease. Despite its excellent effect on weight loss, notable side effects, such as nausea and vomiting, have also been associated with liraglutide. Despite these side effects, liraglutide has not been discontinued due to its beneficial effects on weight loss. Nonetheless, there are reports wherein patients did not experience weight loss upon taking the drug. As such, there is a possibility of liraglutide misuse and abuse. Therefore, physicians need to have a broad understanding of liraglutide and understand the advantages and disadvantages of liraglutide prescription.
Background Liraglutide, a drug used for the management of obesity, has many known side effects. In this study, we developed a predictive model for the occurrence of liraglutide-related side effects using data from electronic medical records (EMRs).
Methods This study included 237 patients from Seoul St. Mary's Hospital and Eunpyeong St. Mary's Hospital who were prescribed liraglutide. An endocrinologist obtained medical data through an EMR chart review. Model performance was evaluated using the mean of the area under the receiver operating characteristic curve (AUROC) with a 95% confidence interval (CI).
Results A predictive model was developed for patients who were prescribed liraglutide. However, 37.1% to 75.5% of many variables were missing, and the AUROC of the developed predictive model was 0.630 (95% CI, 0.551–0.708). Patients who had previously taken antiobesity medication had significantly fewer side effects than those without previous antiobesity medication use (20.7% vs. 41.4%, P<0.003). The risk of side effect occurrence was significantly higher in patients with diabetes than in patients without diabetes by 2.389 times (odds ratio, 2.389; 95% CI, 1.115–5.174).
Conclusions This study did not successfully develop a predictive model for liraglutide-related side effects, primarily due to issues related to missing data. When prescribing antiobesity drugs, detailed records and basic blood tests are expected to be essential. Further large-scale studies on liraglutide-related side effects are needed after obtaining high-quality data.
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The effects and side effects of liraglutide as a treatment for obesity Jeonghoon Ha, Jin Yu, Joonyub Lee, Hun-Sung Kim Cardiovascular Prevention and Pharmacotherapy.2022; 4(4): 142. CrossRef
Background Anticoagulation is important in atrial fibrillation (AF) patients to reduce the occurrence of thrombotic events. We evaluated the efficacy and safety of percutaneous left atrial appendage occlusion (LAAO) as an alternative to systemic anticoagulation through an indirect comparative analysis.
Methods An indirect comparative analysis of nonvitamin K oral anticoagulants (NOACs) and LAAO was conducted. Comparisons were made using data from four landmark randomized clinical trials (RE-LY, ROCKET-AF, ARISTOTLE, and PROTECT AF). Using warfarin as the common comparator, an indirect comparison was performed using data from each trial, and the relative risk was calculated between NOACs and LAAO.
Results NOACs and LAAO showed similar results for the reduction of stroke and systemic embolism, with a non-statistically significant trend favoring NOACs (hazard ratio [HR], 0.74; 95% confidence interval [CI], 0.37–1.46 for dabigatran; HR, 0.99; 95% CI, 0.50–1.92 for rivaroxaban; HR, 0.89; 95% CI, 0.45–1.74 for apixaban). Significantly fewer major bleeding and procedure-related complications were found in patients treated with apixaban compared with LAAO (HR, 0.45; 95% CI, 0.26–0.75). Cardiovascular death occurred more frequently in patients administered NOACs than in patients with LAAO (HR, 2.28; 95% CI, 1.03–5.10 for dabigatran; HR, 2.41; 95% CI, 1.09–5.42 for rivaroxaban; HR, 2.40; 95% CI, 1.10–5.36 for apixaban).
Conclusions The rate of all-cause death was similar between NOACs and LAAO. Compared with LAAO, NOACs led to a nonsignificant numerical decrease in stroke and embolism in AF patients. Significantly fewer safety events occurred in patients treated with apixaban. LAAO significantly reduced cardiovascular death.
Inflammation plays a crucial role in the pathophysiology of coronary artery disease (CAD). Several types of sterile inflammation are mediated through the nucleotide-binding oligomerization domain-like receptor pyrin domain containing 3 (NLRP3) inflammasome. Colchicine has recently been shown to effectively block NLRP3 inflammasome assembly in addition to several other actions on inflammatory cells. Recent evidence also points to favorable effects of colchicine in patients with CAD, including lower levels of inflammatory markers, coronary plaque stabilization, and more favorable cardiac recovery after injury. This review focuses on the role of colchicine in the process of atherosclerosis and discusses its potential as a therapeutic option for the prevention and treatment of CAD.
Background Noninvasive fundus imaging may provide useful information on blood vessels. This study investigated the relationship between localized retinal nerve fiber layer defects (RNFLDs) and vascular biomarkers.
Methods This study included 1,316 participants without cardiovascular disease who were registered in a cardiovascular high-risk cohort. Examined vascular biomarkers included central hemodynamics, carotid-femoral pulse wave velocity (cfPWV), left ventricular hypertrophy (LVH) on electrocardiogram, and coronary artery calcium score (CACS). Fundus photography and optical coherence tomography were used to evaluate RNFLDs. The associations between RNFLDs and established high-risk cutoff points for each biomarker (central blood pressure [BP] ≥125/80 mmHg, central pulse pressure [PP] ≥50 mmHg, cfPWV ≥10 m/s, presence of LVH, and CACS ≥300) were assessed.
Results RNFLD was identified in 394 participants (29.9%) who had higher fasting glucose level, lower renal function, and higher BP than those without RNFLDs. Additionally, central BP, central PP, cfPWV, CACS, and the percentage of subjects with LVH were higher in the RNFLD group. After adjusting for confounders, RNFLDs were not associated with LVH or an elevated central BP, central PP, or cfPWV. However, they were associated with an elevated CACS (odds ratio, 1.44; 95% confidence interval, 1.04–2.00; p=0.029).
Conclusions Non-glaucomatous localized RNFLDs were associated with elevated CACS. Therefore, evaluating RNFLDs using fundus imaging may aid in the assessment of cardiovascular disease risk.
Background Glycated hemoglobin (HbA1c), which reflects the patient's blood sugar level, can only be measured in a hospital setting. Therefore, we developed a model predicting HbA1c using personal information and self-monitoring of blood glucose (SMBG) data solely obtained by a patient.
Methods Leave-one-out cross-validation (LOOCV) was performed at two university hospitals. After measuring the baseline HbA1c level before SMBG (Pre_HbA1c), the SMBG was recorded over a 3-month period. Based on these data, an HbA1c prediction model was developed, and the actual HbA1c value was measured after 3 months. The HbA1c values of the prediction model and actual HbA1c values were compared. Personal information was used in addition to SMBG data to develop the HbA1c predictive model.
Results Thirty model training sessions and evaluations were conducted using LOOCV. The average mean absolute error of the 30 models was 0.659 (range, 0.005–2.654). Pre_HbA1c had the greatest influence on HbA1c prediction after 3 months, followed by post-breakfast blood glucose level, oral hypoglycemic agent use, fasting glucose level, height, and weight, while insulin use had a limited effect on HbA1c values.
Conclusions The patient's SMBG data and personal information strongly influenced the HbA1c predictive model. In the future, it will be necessary to develop sophisticated predictive models using large samples for stable SMBG in patients.
Background Statin-associated muscle symptoms are one of the side effects that physicians should consider when prescribing statins. In this study, creatine kinase (CK) levels were measured following statin prescription, and various factors affecting the CK levels were determined using machine learning.
Methods Changes in the CK were observed every 3 months for a 12-month period in patients who received statins for the first time at Seoul St. Mary's Hospital. For each visit, we developed four basic models based on changes in the CK levels. Extreme gradient boosting, a scalable end-to-end tree boosting algorithm, which employs a decision-tree-based ensemble machine learning algorithm, was used for the prediction of changes in the CK.
Results A total of 23,860 patients were included. Among them, 19 patients (0.08%) had increased CK levels of 2,000 IU·L−1 or more 3 months after statin prescription, and 65 patients (0.27%) exhibited CK levels of over 2,000 IU·L−1 at least once during the 12-month study period. The area under the receiver operator characteristic of each model for each visit was 0.709–0.769, and the accuracy was 0.700–0.803. In each of the models, the variables that had the strongest influence on changes in the CK were sex and previous CK value.
Conclusions Through machine learning, factors influencing changes in the CK were identified. These results will provide the basis for future research, through which the optimal parameters of the CK prediction model can be found and the model can be used in clinical applications.
Big data, artificial intelligence, machine learning, and deep learning have received considerable attention in the medical field. Attempts to use such machine learning in areas where medical decisions are difficult or necessary are continuously being made. To date, there have been many attempts to solve problems associated with the use of machine learning by using deep learning; hence, physicians should also have basic knowledge in this regard. Deep neural networks are one of the most actively studied methods in the field of machine learning. The perceptron is one of these artificial neural network models, and it can be considered as the starting point of artificial neural network models. Perceptrons receive various inputs and produce one output. In a perceptron, various weights (ω) are given to various inputs, and as ω becomes larger, it becomes an important factor. In other words, a perceptron is an algorithm with both input and output. When an input is provided, the output is produced according to a set rule. In this paper, the decision rules of the perceptron and its basic principles are examined. The intent is to provide a wide range of physicians with an understanding of the latest machine-learning methodologies based on deep neural networks.
Heart failure (HF) is an important cardiovascular disease because of the increasing prevalence, high morbidity and mortality, and rapid expansion of health care costs. Over the past decades, efforts have been made to modify the prognosis of patients with HF. Regarding HF with reduced ejection fraction (HFrEF), several drugs have shown to improve mortality and morbidity, based on large-scale randomized controlled trials, leading to a critical paradigm shift in its pharmacological treatment. The paradigm of HFrEF pathophysiology has shifted from cardiorenal disease to hemodynamic changes, and neurohormonal activation is currently considered the prime pathophysiological mechanism of HFrEF. This review summarizes evidence on the pharmacological management of HFrEF derived from major randomized controlled trials, which have accomplished improvements in survival benefits.
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Recent dramatic developments in information and communication technologies have been widely applied to medicine and healthcare. In particular, biometric sensors in wearable devices linked to smartphones are collecting vast amounts of personal health data. To best use these accumulated data, personalized healthcare services are emerging, and digital platforms are being developed and studied to enable data integration and analysis. The implementation of biometric sensors and smartphones for cardiovascular and cerebrovascular healthcare emerged from the research on the feasibility and efficacy of the devices in the clinical environment. It is important to understand the recent research trends in data generation, integration, and application to prevent and treat cardiovascular and cerebrovascular diseases. This paper describes these recent developments in treating cardiovascular diseases.
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