We aimed to test the association between low-density lipoprotein cholesterol (LDL-C) and cardiovascular disease (CVD), cancer, and all-cause mortality in non-statin users. A total of 347,971 subjects in Kangbuk Samsung Health Study (KSHS.57.4% men, mean follow up: 5.64 ± 3.27 years) were tested. To validate these associations, we analyzed data from another cohort (Korean genome and epidemiology study, KoGES, 182,943 subjects). All subjects treated with any lipid-lowering therapy and who died during the first 3 years of follow up were excluded. Five groups were defined according to baseline LDL-C concentration (<70, 70–99, 100–129, 130–159, ≥160 mg/dL). A total of 2028 deaths occurred during follow-up in KSHS. The lowest LDL-C group (LDL < 70 mg/dL) had a higher risk of all-cause mortality (HR 1.95, 1.55–2.47), CVD mortality (HR 2.02, 1.11–3.64), and cancer mortality (HR 2.06, 1.46–2.90) compared to the reference group (LDL 120–139 mg/dL). In the validation cohort, 2338 deaths occurred during follow-up. The lowest LDL-C group (LDL < 70 mg/dL) had a higher risk of all-cause mortality (HR 1.81, 1.44–2.28) compared to the reference group. Low levels of LDL-C concentration are strongly and independently associated with increased risk of cancer, CVD, and all-cause mortality. These findings suggest that more attention is needed for subjects with no statin-induced decrease in LDL-C concentrations.

Kim C, et al. Korean Circ J. 2018 Aug;49:e129. https://doi.org/10.4070/kcj.2019.0194

Though clinical practice guidelines (CPGs) for cardiac rehabilitation (CR) are an effective and widely used treatment method worldwide, they are as yet not widely accepted in Korea. Given that cardiovascular disease is the second leading cause of death in Korea, it is urgent that CR programs be developed. In 2008, the Government of Korea implemented CR programs at 11 university hospitals as part of its Regional Cardio-Cerebrovascular Center Project, and three additional medical facilities will be added in 2019. In addition, owing to the promotion of CR nationwide and the introduction of CR insurance benefits, 40 medical institutions nationwide have begun CR programs even as a growing number of medical institutions are preparing to offer CR. The purpose of this research was to develop evidence-based CPGs to support CR implementation in Korea. This study is based on an analysis of CPGs elsewhere in the world, an extensive literature search, a systematic analysis of multiple randomized control trials, and a CPG management, development, and assessment committee comprised of 33 authors-primarily rehabilitation specialists, cardiologists, and thoracic surgeons in 21 university hospitals and two general hospitals. Twelve consultants, primarily rehabilitation, sports medicine, and preventive medicine specialists, CPG experts, nurses, physical therapists, clinical nutritionists, and library and information experts participated in the research and development of these CPGs. After the draft guidelines were developed, three rounds of public hearings were held with staff members from relevant academic societies and stakeholders, after which the guidelines were further reviewed and modified. CR involves a more cost-effective use of healthcare resources relative to that of general treatments, and the exercise component of CR lowers cardiovascular mortality and readmission rates, regardless of the type of coronary heart disease and type and setting of CR. Individualized CR programs should be considered together with various factors, including differences in heart function and lifestyle, and doing so will boost participation and adherence with the CR program, ultimately meeting the final goals of the program, namely reducing the recurrence of myocardial infarction and mortality rates.

The purpose of this study was to examine the effects of aqua walking (AW) on coronary artery disease (CAD) and cardiorespiratory fitness in older adults with osteoarthritis in the lower extremity and compare it with that of traditional over-ground walking. Sixty consecutive eligible patients who had undergone percutaneous coronary intervention for CAD with limited ambulation due to lower extremity osteoarthritis were recruited. They were randomly assigned to the AW program group, treadmill/track walking (TW) program group, or non-exercise control group (CON). Assessments were performed before and after 24 weeks of medically supervised exercise training. Significant differences were observed in the change in %body fat (TW: −2.7%, AW: −2.8%, CON: −0.4%), total cholesterol level (TW: −23.6 mg/dL, AW: −27.2 mg/dL, CON: 15.8 mg/dL), resting heart rate (TW: −6.3 bpm, AW: −6.9 bpm, CON: 1.3 bpm), and cardiorespiratory fitness expressed as VO2 peak (TW: 2.3 mL/kg·min−1, AW: 2.0 mL/kg·min−1, CON: −2.5 mL/kg·min−1) over 24 weeks among the groups. However, no significant differences in the change in these measures were found between the TW and AW groups. AW appears to be a feasible alternative exercise modality to over-ground walking for cardiac rehabilitation and can be recommended for older adults with CAD and osteoarthritis.

Objective: The aim of this study was to develop, compare, and validate models for predicting cardiovascular disease (CVD) mortality and hospitalization with hypertension using a conventional statistical model and a deep learning model. Methods: Using the database of Korean National Health Insurance Service, 2,037,027 participants with hypertension were identified. Among them, CVD (myocardial infarction or stroke) death and/or hospitalization that occurred within one year after the last visit were analyzed. Oversampling was performed using the synthetic minority oversampling algorithm to resolve imbalances in the number of samples between case and control groups. The logistic regression method and deep neural network (DNN) method were used to train models for assessing the risk of mortality and hospitalization. Findings: Deep learning-based prediction model showed a higher performance in all datasets than the logistic regression model in predicting CVD hospitalization (accuracy, 0.863 vs. 0.655; F₁-score, 0.854 vs. 0.656; AUC, 0.932 vs. 0.655) and CVD death (accuracy, 0.925 vs. 0.780; F₁-score, 0.924 vs. 0.783; AUC, 0.979 vs. 0.780). Interpretation: The deep learning model could accurately predict CVD hospitalization and death within a year in patients with hypertension. The findings of this study could allow for prevention and monitoring by allocating resources to high-risk patients.