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9 "Jiwon Shinn"
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Review Article
Diverse perspectives on remote collaborative care for chronic disease management
Seo Yeon Baik, Hakyoung Park, Jiwon Shinn, Hun-Sung Kim
Cardiovasc Prev Pharmacother. 2024;6(1):26-32.   Published online January 25, 2024
DOI: https://doi.org/10.36011/cpp.2024.6.e5
  • 176 View
  • 8 Download
Abstract PDF
Remote collaborative care is a program that improves medical services by linking local and remote physicians with residents in areas where access to medical facilities is limited, utilizing information and communication technology. As a result, patients can obtain medical advice and counseling at local hospitals without needing to travel to distant facilities. This care model involves communication between doctors, facilitating the accurate transfer of medical information and reducing the risk of misunderstandings. For instance, managing conditions such as blood pressure or blood glucose is more straightforward because a local hospital can assess the patient's status while a remote hospital simultaneously provides high-quality, specialized medical services. With the rise in poorly controlled hypertension or diabetes, the need for remote collaborative care has also increased. This care model enables local hospitals to maintain continuous patient care with the support of remote facilities. This is particularly true following acute cardiovascular treatment, where local hospitals, assisted by remote institutions, can safely offer high-quality services such as rehabilitation and follow-up care. Although remote hospitals have many advantages with the increasing number of patients, many difficulties remain in commercializing unsystematized remote collaborative care. Specifically, low reimbursements for medical services must be addressed, proper equipment is needed, more time and effort must be invested, and the liability issue must also be dealt with. Nevertheless, remote collaborative care using information and communication technology will be necessary in the future. Medical staff need to objectively examine the advantages and disadvantages of remote collaborative care from various perspectives and find ways to revitalize it.
Original Article
Current status of remote collaborative care for hypertension in medically underserved areas
Seo Yeon Baik, Kyoung Min Kim, Hakyoung Park, Jiwon Shinn, Hun-Sung Kim
Cardiovasc Prev Pharmacother. 2024;6(1):33-39.   Published online January 22, 2024
DOI: https://doi.org/10.36011/cpp.2024.6.e2
  • 215 View
  • 10 Download
Abstract PDF
Background
Remote collaborative care (ReCC) is a legally recognized form of telehealth that facilitates communication between physicians. This study aimed to analyze the effectiveness of ReCC services and establish a foundation for the usefulness and effectiveness of ReCC.
Methods
This retrospective cohort study utilized data from the Digital Healthcare Information System (DHIS) managed by the Korea Social Security Information Service. We extracted data on patients who were registered from January 2017 through September 2023 to investigate the effects of various factors.
Results
A total of 10,407 individuals participated in the remote collaborative consultation service provided by the DHIS. Of these participants, those aged ≥80 years represented 39.2% (4,085 patients), while those aged 70 to 79 years comprised 36.9% (3,838 patients). The conditions treated included hypertension, affecting 69.2% (7,203 patients), and diabetes, affecting 21.1% (2,201 patients). Although various measurement items were recorded, most data beyond blood pressure readings were missing, posing a challenge for analysis. Notably, there was a significant reduction in blood pressure that was sustained at follow-up intervals of 1, 3, 6, and 12 months post-baseline (all P<0.05).
Conclusions
Owing to the lack of data, follow-up assessments for conditions other than hypertension proved to be challenging. Medical staff should increase their focus on and engagement with the system. Remote consultations have demonstrated efficacy in managing hypertension in medically underserved areas, where access to healthcare services is often limited. This suggests the potential for expanded use of remote chronic care in the future.
Review Article
Using medical big data for clinical research and legal considerations for the protection of personal information: the double-edged sword
Raeun Kim, Jiwon Shinn, Hun-Sung Kim
Cardiovasc Prev Pharmacother. 2024;6(1):8-16.   Published online January 22, 2024
DOI: https://doi.org/10.36011/cpp.2024.6.e1
  • 298 View
  • 5 Download
Abstract PDF
The advent of medical big data has increased the scope of the clinical use of such data; however, these data have raised serious concerns regarding personal privacy protection, which hinders their usage. For instance, as the pseudonymization or anonymization of data increases, the quality of its clinical use decreases. Thus, a balanced approach is required to maximize clinical data use while protecting personal information as much as possible. However, Korea’s existing laws mandate several kinds of consent; soliciting some of these types of consent can be cumbersome. Moreover, while the collection of medical data by hospitals requires considerable time and money, its ownership is difficult to ascertain. To bridge the enormous gap between the protection of personal information and the use of clinical data, the European Union and countries such as Finland have already proposed various modes of guaranteeing the free movement of personal information that simultaneously strengthen people’s personal rights. Similarly, Korea has initiated the MyData Service, although it faces several limitations. Therefore, this study reviews Korea’s current healthcare big data system, the laws governing data sharing and usage, and compares them with similar laws enacted by the European Union and Finland. It then provides future direction for Korea’s personal information protection legislation. Ultimately, governments must expand and elaborate upon the scope and content of personal information protection laws to enable the development of healthcare and other industries without sacrificing either personal information protection or clinical use of medical data.
Original Articles
Correlation between metformin intake and prostate cancer
Raeun Kim, Minsun Song, Jiwon Shinn, Hun-Sung Kim
Cardiovasc Prev Pharmacother. 2023;5(3):91-97.   Published online July 31, 2023
DOI: https://doi.org/10.36011/cpp.2023.5.e12
  • 1,066 View
  • 27 Download
Abstract PDF
Background
The relationship between metformin intake and prostate cancer incidence remains unclear. Therefore, we examined the correlation between prostate cancer and metformin use.
Methods
The subjects were diabetes patients aged ≥50 years who had been diagnosed with prostate cancer and had undergone surgery at Seoul St. Mary's Hospital. Groups taking metformin (MET(+) group) and not taking metformin (MET(–) group) were divided and compared.
Results
The mean preoperative prostate-specific antigen (PSA) levels in the MET(–) and MET(+) groups were 10.7±11.9 and 8.0±5.6 ng/mL, respectively, with no statistically significant difference between the two groups (P=0.387). The average prostate volume of the MET(–) group was 82.4±98.0 mL, and the average prostate volume of the MET(+) group was 55.4±20.1 mL, but there was no statistically significant difference between the two groups (P=0.226). The mean PSA velocity also did not show a significant difference between the two groups (0.025±0.102 ng/mL vs. 0.005±0.012 ng/mL, P=0.221).
Conclusions
We did not identify a significant positive correlation between metformin and prostate cancer. However, preoperational PSA and PSA velocity tended to be lower in the MET(+) group. A sophisticated prospective study with a large sample size should be planned.
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
  • 1,044 View
  • 20 Download
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.
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
  • 3,440 View
  • 38 Download
  • 1 Citations
Abstract PDFSupplementary Material
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.

Citations

Citations to this article as recorded by  
  • 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
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
  • 2,433 View
  • 23 Download
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.
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
  • 2,766 View
  • 42 Download
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.
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
  • 2,566 View
  • 9 Download
  • 1 Citations
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.

Citations

Citations to this article as recorded by  
  • 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

CPP : Cardiovascular Prevention and Pharmacotherapy