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CPP : Cardiovascular Prevention and Pharmacotherapy

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Volume 2(3); July 2020
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Review Article
Prevention of Type 2 Diabetes
Jong Dai Kim, Won-Young Lee
Cardiovasc Prev Pharmacother. 2020;2(3):63-76.   Published online July 31, 2020
DOI: https://doi.org/10.36011/cpp.2020.2.e10
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Abstract PDF
The number of patients with type 2 diabetes (T2D) is increasing worldwide and that in Korea, particularly, has shown an exponential increase with a rise in the older population. The diabetic population is predicted to soar up to 6 million by 2050. The prevalence of diabetes among Korean adults is approximately 15%, while that of prediabetes is 25%, with a total prevalence of 40%. As 40% of the prediabetes cases subsequently progress to T2D, prevention through proactive interventions at the prediabetes stage is essential to reduce the socioeconomic burden due to T2D and the complications of diabetes. With regard to the prevention of T2D, new findings have been published related to the implementation of lifestyle interventions such as exercise and diet as well as drug treatments and surgeries, which have deepened our understanding of the prevention of T2D. Based on published evidence, this review aimed to examine the methods used in the prevention of diabetes.
Special Articles
Competing Risk Model in Survival Analysis
Yena Jeon, Won Kee Lee
Cardiovasc Prev Pharmacother. 2020;2(3):77-84.   Published online July 31, 2020
DOI: https://doi.org/10.36011/cpp.2020.2.e11
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Abstract PDF
Survival analysis is primarily used to identify the time-to-event for events of interest. However, there subjects may undergo several outcomes; competing risks occur when other events may affect the incidence rate of the event of interest. In the presence of competing risks, traditional survival analysis such as the Kaplan-Meier method or the Cox proportional hazard regression introduces biases into the estimation of survival probability. In this review, we discuss several methods that can be used to consider competing risks in survival analysis: the cumulative incidence function, the cause-specific hazard function, and Fine and Gray's Subdistribution hazard function. We also provide a guide for conducting competing risk analysis using SAS with the bone marrow transplantation dataset presented by Klein and Moeschberger (1997).
Pragmatic Clinical Trials for Real-World Evidence: Concept and Implementation
Na-Young Jeong, Seon-Ha Kim, Eunsun Lim, Nam-Kyong Choi
Cardiovasc Prev Pharmacother. 2020;2(3):85-98.   Published online July 31, 2020
DOI: https://doi.org/10.36011/cpp.2020.2.e12
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  • 1 Citations
Abstract PDF
The importance of real-world evidence (RWE) has been highlighted in recent years, and the limitations of the classical randomized controlled trials, also known as explanatory clinical trials (ECTs), have been emphasized. Post-marketing observational studies have several problems, such as biases and incomparability between patient groups, and RWE can only be obtained after a certain period. Therefore, pragmatic clinical trials (PCTs) have garnered attention as an alternative to obtaining scientifically robust RWE in a relatively short time. PCTs are clinical trials that have a pragmatic concept, i.e., the opposite of ECTs and are intended to help decision makers by evaluating the effectiveness of interventions in routine clinical practice. The characteristics of PCTs are the inclusion of various patients in clinical practice, recruitment of patients in heterogeneous settings, and comparison with actual clinical treatments rather than a placebo. Thus, the results of PCTs are likely to be generalized and can have a direct impact on clinical and policy decision-making. This study aimed to describe the characteristics and definitions of PCTs compared with those of ECTs and to highlight the important considerations in the planning process of PCTs. To perform PCTs for the purpose of obtaining RWE, the contents covered in this study will be helpful.
Causal Claims in Health Sciences and Medicine: a Difference-in-Differences Method
Kyoung-Nam Kim
Cardiovasc Prev Pharmacother. 2020;2(3):99-102.   Published online July 31, 2020
DOI: https://doi.org/10.36011/cpp.2020.2.e13
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Abstract PDF
The difference-in-differences (DID) method is a useful tool to make causal claims using observational data. The key idea is to compare the difference between exposure and control groups before and after an event. The potential outcome of the exposure group during the post-exposure period is estimated by adding the observed outcome change of the control group between the pre- and post-exposure period to the observed outcome of the exposure group during the pre-exposure period. Because the effect of exposure is evaluated by comparing the observed outcome and potential outcome of the same exposure group, unmeasured potential confounders can be cancelled out by the design. To apply this method appropriately, the difference between the exposure and control groups needs to be relatively stable if no exposure occurred. Despite the strengths of the DID method, the assumptions, such as parallel trends and proper comparison groups, need to be carefully considered before application. If used properly, this method can be a useful tool for epidemiologists and clinicians to make causal claims with observational data.
Original Article
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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  • 1 Download
Abstract PDF
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.

CPP : Cardiovascular Prevention and Pharmacotherapy