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스타틴 처방 후 연령에 따른 저밀도 콜레스테롤의 목표 도달률 분석
최선(Seon Choe),신지원(Jiwon Shinn),김헌성(Hun-Sung Kim),김주한(Ju Han Kim) 대한임상노인의학회 2020 대한임상노인의학회지 Vol.21 No.2
Background: The purpose of this study was to determine the target achievement level of low-density lipoprotein cholesterol (LDL-C) according to age and glomerular filtration rate (GFR) in patients taking LipilouⓇ, a generic formulation of atorvastatin, in clinical practice. Methods: This study was an electronic medical record-based study of patients prescribed LipilouⓇ for more than 2 months at Seoul National University Hospital between 2012 and 2018. The target achievement level of LDL-C was less than 70 mg/dL or 100 mg/dL, depending on the patient’s age. The target LDL-C achievement level according to renal function was evaluated in patients older than 60 years of age. Results: A total of 1,209 patients were included in this study. Target LDL-C achievement levels less than 70 mg/dL or 100 mg/dL showed a significant increase with age (P<0.001). For patients in their 60s, a target achievement level of less than 100 mg/dL increased significantly as the GFR decreased (P<0.05). Conclusion: Allowing for an increased target LDL-C achievement level may be important to prevent cardiovascular disease. Considering that elderly patients respond differently to treatments and follow different prognoses, as well as exhibiting individual differences, physicians must carefully plan treatments involving statins.
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Park Jong Il,이화영,Kim Hyunah,이지산,Shinn Jiwon,김헌성 대한의학회 2021 Journal of Korean medical science Vol.36 No.37
Various digital healthcare devices and apps, such as blood glucose meters, blood pressure monitors, and step-trackers are commonly used by patients; however, digital healthcare devices have not been widely accepted in the medical market as of yet. Despite the various legal and privacy issues involved in their use, the main reason for its poor acceptance is that users do not use such devices voluntarily and continuously. Digital healthcare devices generally do not provide valuable information to users except for tracking self-checked glucose or walking. To increase the use of these devices, users must first understand the health data produced in the context of their personal health, and the devices must be easy to use and integrated into everyday life. Thus, users need to know how to manage their own data. Medical staff must teach and encourage users to analyze and manage their patient-generated healthcare data, and users should be able to find medical values from these digital devices. Eventually, a single customized service that can comprehensively analyze various medical data to provide valuable customized services to users, and which can be linked to various heterogeneous digital healthcare devices based on the integration of various health data should be developed. Digital healthcare professionals should have detailed knowledge about a variety of digital healthcare devices and fully understand the advantages and disadvantages of digital healthcare to help patients understand and embrace the use of such devices.
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Kang Dong Yoon,Kim Hyunah,Ko SooJeong,Kim HyungMin,Shinn Jiwon,Kang Min-Gyu,Byeon Sun-ju,Choi Jeong-Hee,Shin Soo-Yong,Kim Hun-Sung 대한의학회 2022 Journal of Korean medical science Vol.37 No.7
Background: The most important aspect of a retrospective cohort study is the operational definition (OP) of the disease. We developed a detailed OP for the detection of sodiumglucose cotransporter-2 inhibitors (SGLT2i) related to diabetic ketoacidosis (DKA). The OP was systemically verified and analyzed. Methods: All patients prescribed SGLT2i at four university hospitals were enrolled in this experiment. A DKA diagnostic algorithm was created and distributed to each hospital; subsequently, the number of SGLT2i-related DKAs was confirmed. Then, the algorithm functionality was verified through manual chart reviews by an endocrinologist using the same OP. Results: A total of 8,958 patients were initially prescribed SGLT2i. According to the algorithm, 0.18% (16/8,958) were confirmed to have SGLT2i-related DKA. However, based on manual chart reviews of these 16 cases, there was only one case of SGLT2i-related DKA (positive predictive value = 6.3%). Even after repeatedly narrowing the diagnosis range of the algorithm, the effect of a positive predictive value was insignificant (6.3–10.0%, P > 0.999). Conclusion: Owing to the nature of electronic medical record data, we could not create an algorithm that clearly differentiates SGLT2i-related DKA despite repeated attempts. In all retrospective studies, a portion of the samples should be randomly selected to confirm the accuracy of the OP through chart review. In retrospective cohort studies in which chart review is not possible, it will be difficult to guarantee the reliability of the results.
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