Cardiovascular Prevention and Pharmacotherapy

Hun-Sung Kim

10 Articles

Correlation analysis of cancer incidence after pravastatin treatment: Jin Yu, Raeun Kim, Jiwon Shinn, Man Young Park, Hun-Sung Kim; Cardiovasc Prev Pharmacother. 2023;5(2):61-68. Published online April 28, 2023; DOI: https://doi.org/10.36011/cpp.2023.5.e6

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Abstract PDF: Background
Few studies have investigated the cancer-preventive effects of statins, which are known to protect against cardio-cerebrovascular diseases. In this study, we analyzed the degree to which pravastatin, a low-potency statin, could prevent cancer.
Methods
This retrospective cohort study used data from the Korean National Health Insurance Service database. Patients diagnosed with diabetes after the age of 50 years were divided into a pravastatin group and a control group that did not receive any statin prescriptions.
Results
This study included 557 patients in the pravastatin group and 2,221 patients in the control (no statin) group. During the 5-year follow-up, the incidence of cancer was 16.7% (93 of 557 patients) in the pravastatin group and 19.9% (442 of 2,221 patients) in the control group. The incidence of cancer was 22% higher in the control group than in the pravastatin group (hazard ratio, 1.22; 95% confidence interval, 0.97–1.52; P=0.09). Death from various causes occurred at a 45% higher frequency in the control group than in the pravastatin group (hazard ratio, 1.45; 95% confidence interval, 0.99–2.12; P=0.06). However, neither of those relationships reached statistical significance.
Conclusions
Although pravastatin use did not show a significant causal relationship with cancer incidence, fewer cases of cancer occurred in pravastatin users than in controls. However, further large-scale studies are required to confirm these findings.

Liraglutide, a glucagon-like peptide-1 analog, in individuals with obesity in clinical practice: Juyoung Shin, Raeun Kim, Hun-Sung Kim; Cardiovasc Prev Pharmacother. 2023;5(2):49-53. Published online April 28, 2023; DOI: https://doi.org/10.36011/cpp.2023.5.e7

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Abstract PDF: Obesity is a disease requiring treatment. The prevalence of obesity is steadily increasing both in Korea and worldwide. Individuals with obesity are at elevated risks of diabetes, cerebrovascular disease, and solid cancer; therefore, obesity is now considered to be a disease requiring treatment, rather than merely a cosmetic problem. Nutrition and exercise are the basic forms of obesity management, but it is not easy to lose weight through only one’s own willpower. Accordingly, policies for establishing a cultural environment that encourages desirable behaviors are proposed through multifaceted efforts involving the media and local organizations. However, the pharmacological and surgical treatments selected as medical interventions should be individualized based on an understanding of each individual’s cause of obesity and characteristics. It is important to understand how to enhance and maintain the effectiveness of treatment not only for the prescribing medical staff, but also for the individual with obesity who is being treated.

The effects and side effects of liraglutide as a treatment for obesity: Jeonghoon Ha, Jin Yu, Joonyub Lee, Hun-Sung Kim; Cardiovasc Prev Pharmacother. 2022;4(4):142-148. Published online October 20, 2022; DOI: https://doi.org/10.36011/cpp.2022.4.e18

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Abstract PDF: 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.

Development of a predictive model for the side effects of liraglutide: Jiyoung Min, Jiwon Shinn, Hun-Sung Kim; Cardiovasc Prev Pharmacother. 2022;4(2):87-93. Published online April 27, 2022; DOI: https://doi.org/10.36011/cpp.2022.4.e12

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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

Development of a Predictive Model for Glycated Hemoglobin Values and Analysis of the Factors Affecting It: HyeongKyu Park, Da Young Lee, So young Park, Jiyoung Min, Jiwon Shinn, Dae Ho Lee, Soon Hyo Kwon, Hun-Sung Kim, Nan Hee Kim; Cardiovasc Prev Pharmacother. 2021;3(4):106-114. Published online October 31, 2021; DOI: https://doi.org/10.36011/cpp.2021.3.e14

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Abstract PDF: 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.

Modeling of Changes in Creatine Kinase after HMG-CoA Reductase Inhibitor Prescription: Hun-Sung Kim, Jiyoung Min, Jiwon Shinn, Oak-Kee Hong, Jang-Won Son, Seong-Su Lee, Sung-Rae Kim, Soon Jib Yoo; Cardiovasc Prev Pharmacother. 2021;3(4):115-123. Published online October 31, 2021; DOI: https://doi.org/10.36011/cpp.2021.3.e15

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Abstract PDF: 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.

Perceptron: Basic Principles of Deep Neural Networks: Eung-Hee Kim, Hun-Sung Kim; Cardiovasc Prev Pharmacother. 2021;3(3):64-72. Published online July 31, 2021; DOI: https://doi.org/10.36011/cpp.2021.3.e9

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Abstract PDF: 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.

Logistic Regression and Least Absolute Shrinkage and Selection Operator: Hyunyong Lee, Hun-Sung Kim; Cardiovasc Prev Pharmacother. 2020;2(4):142-146. Published online October 31, 2020; DOI: https://doi.org/10.36011/cpp.2020.2.e15

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Abstract PDF

Logistic regression, a model that forms a binary dependent variable and one or more independent variable(s), is used especially in epidemiological studies. By understanding the logistic model and its applications, such as odds ratio (OR) and performance efficiency, the concept of logistic regression can be easily grasped. The purpose of this article is to 1) introduce logistic regression, including odds and OR, 2) present predictive efficiency, such as area under the curve, and 3) explain the caution of logistic regression analysis.

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Perceptron: Basic Principles of Deep Neural Networks
Eung-Hee Kim, Hun-Sung Kim
Cardiovascular Prevention and Pharmacotherapy.2021; 3(3): 64. CrossRef

Changes in Target Achievement Rates after Statin Prescription Changes at a Single University Hospital: Seon Choe, Jiwon Shinn, Hun-Sung Kim, Ju Han Kim; Cardiovasc Prev Pharmacother. 2020;2(3):103-111. Published online July 31, 2020; DOI: https://doi.org/10.36011/cpp.2020.2.e14

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Background
We investigated the changes in low-density lipoprotein cholesterol (LDL-C) target achievement rates (<70 and <100 mg/dL) when the prescription changed from various statins to Lipilou^®, a generic formulation of atorvastatin.
Methods
This was a retrospective cohort study of patients who had been prescribed Lipilou^® for more than 3 months at Seoul National University Hospital from 2012 to 2018. For patients who were treated with a previous statin before the prescription of Lipilou^®, changes in target achievement rates of LDL-C less than 70 and less than 100 mg/dL were confirmed 3–6 months after the prescription of Lipilou^®.
Results
Among the 683 enrolled patients, when their prescription was changed to Lipilou^®, the target achievement rate of LDL-C significantly increased for LDL-C less than 70 mg/dL (from 22.1% to 66.2%, p<0.001) and less than 100 mg/dL (from 26.8% to 75.3%, p<0.001). In particular, when a moderate-low potency statin was changed to Lipilou^® (10 mg), the target achievement rates for LDL-C less than 70 mg/dL (from 28.9% to 66.7%, p<0.001) and less than 100 mg/dL (from 42.2% to 86.7%, p<0.001) significantly increased. The change from a moderate-high potency statin to Lipilou^® (20 mg) showed an increased target achievement rates for LDL-C <70 mg/dL (from 33.3% to 80.0%, p=0.008) and 100 mg/dL (from 40.0% to 73.3%, p<0.025).
Conclusions
We cannot simply conclude that Lipilou^® is superior to other statins. However, when the target LDL-C was not reached with previous statin treatments, a high target achievement rate could be achieved by changing the prescription to Lipilou^®. Physicians should always consider aggressive statin prescription changes for high target achievement rates.

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Understanding and Utilizing Claim Data from the Korean National Health Insurance Service (NHIS) and Health Insurance Review & Assessment (HIRA) Database for Research
Dae-Sung Kyoung, Hun-Sung Kim
Journal of Lipid and Atherosclerosis.2022; 11(2): 103. CrossRef

Recent Technology-Driven Advancements in Cardiovascular Disease Prevention: Jisan Lee, Hun-Sung Kim, Dai-Jin Kim; Cardiovasc Prev Pharmacother. 2019;1(2):43-49. Published online October 31, 2019; DOI: https://doi.org/10.36011/cpp.2019.1.e7

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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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Sex- and Age-Specific Trends in Cardiovascular Health in Korea, 2007–2018
So Mi Jemma Cho, Hokyou Lee, Hyeon Chang Kim
Korean Circulation Journal.2021; 51(11): 922. CrossRef
Lack of Acceptance of Digital Healthcare in the Medical Market: Addressing Old Problems Raised by Various Clinical Professionals and Developing Possible Solutions
Jong Il Park, Hwa Young Lee, Hyunah Kim, Jisan Lee, Jiwon Shinn, Hun-Sung Kim
Journal of Korean Medical Science.2021;[Epub] CrossRef
Lessons from Use of Continuous Glucose Monitoring Systems in Digital Healthcare
Hun-Sung Kim, Kun-Ho Yoon
Endocrinology and Metabolism.2020; 35(3): 541. CrossRef

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