ABSTRACT
Background@#The severity of gestational diabetes mellitus (GDM) is associated with adverse pregnancy outcomes. We aimed to generate a risk model for predicting insulin-requiring GDM before pregnancy in Korean women. @*Methods@#A total of 417,210 women who received a health examination within 52 weeks before pregnancy and delivered between 2011 and 2015 were recruited from the Korean National Health Insurance database. The risk prediction model was created using a sample of 70% of the participants, while the remaining 30% were used for internal validation. Risk scores were assigned based on the hazard ratios for each risk factor in the multivariable Cox proportional hazards regression model. Six risk variables were selected, and a risk nomogram was created to estimate the risk of insulin-requiring GDM. @*Results@#A total of 2,891 (0.69%) women developed insulin-requiring GDM. Age, body mass index (BMI), current smoking, fasting blood glucose (FBG), total cholesterol, and γ-glutamyl transferase were significant risk factors for insulin-requiring GDM and were incorporated into the risk model. Among the variables, old age, high BMI, and high FBG level were the main contributors to an increased risk of insulin-requiring GDM. The concordance index of the risk model for predicting insulin-requiring GDM was 0.783 (95% confidence interval, 0.766 to 0.799). The validation cohort’s incidence rates for insulin-requiring GDM were consistent with the risk model’s predictions. @*Conclusion@#A novel risk engine was generated to predict insulin-requiring GDM among Korean women. This model may provide helpful information for identifying high-risk women and enhancing prepregnancy care.
ABSTRACT
Artificial intelligence (AI)-based diagnostic technology using medical images can be used to increase examination accessibility and support clinical decision-making for screening and diagnosis. To determine a machine learning algorithm for diabetes complications, a literature review of studies using medical image-based AI technology was conducted using the National Library of Medicine PubMed, and the Excerpta Medica databases. Lists of studies using diabetes diagnostic images and AI as keywords were combined. In total, 227 appropriate studies were selected. Diabetic retinopathy studies using the AI model were the most frequent (85.0%, 193/227 cases), followed by diabetic foot (7.9%, 18/227 cases) and diabetic neuropathy (2.7%, 6/227 cases). The studies used open datasets (42.3%, 96/227 cases) or directly constructed data from fundoscopy or optical coherence tomography (57.7%, 131/227 cases). Major limitations in AI-based detection of diabetes complications using medical images were the lack of datasets (36.1%, 82/227 cases) and severity misclassification (26.4%, 60/227 cases). Although it remains difficult to use and fully trust AI-based imaging analysis technology clinically, it reduces clinicians’ time and labor, and the expectations from its decision-support roles are high. Various data collection and synthesis data technology developments according to the disease severity are required to solve data imbalance.
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Background@#This study investigated the risk of cause-specific mortality according to glucose tolerance status in elderly South Koreans. @*Methods@#A total of 1,292,264 individuals aged ≥65 years who received health examinations in 2009 were identified from the National Health Information Database. Participants were classified as normal glucose tolerance, impaired fasting glucose, newly-diagnosed diabetes, early diabetes (oral hypoglycemic agents ≤2), or advanced diabetes (oral hypoglycemic agents ≥3 or insulin). The risk of system-specific and disease-specific deaths was estimated using multivariate Cox proportional hazards analysis. @*Results@#During a median follow-up of 8.41 years, 257,356 deaths were recorded. Diabetes was associated with significantly higher risk of all-cause mortality (hazard ratio [HR], 1.58; 95% confidence interval [CI], 1.57 to 1.60); death due to circulatory (HR, 1.49; 95% CI, 1.46 to 1.52), respiratory (HR, 1.51; 95% CI, 1.47 to 1.55), and genitourinary systems (HR, 2.22; 95% CI, 2.10 to 2.35); and neoplasms (HR, 1.30; 95% CI, 1.28 to 1.32). Diabetes was also associated with a significantly higher risk of death due to ischemic heart disease (HR, 1.70; 95% CI, 1.63 to 1.76), cerebrovascular disease (HR, 1.46; 95% CI, 1.41 to 1.50), pneumonia (HR, 1.69; 95% CI, 1.63 to 1.76), and acute or chronic kidney disease (HR, 2.23; 95% CI, 2.09 to 2.38). There was a stepwise increase in the risk of death across the glucose spectrum (P for trend <0.0001). Stroke, heart failure, or chronic kidney disease increased the risk of all-cause mortality at every stage of glucose intolerance. @*Conclusion@#A dose-dependent association between the risk of mortality from various causes and severity of glucose tolerance was noted in the elderly population.
ABSTRACT
Background@#It remains unclear whether a combination of glycemic variability and glycated hemoglobin (HbA1c) status leads to a higher incidence of cardiovascular disease (CVD).Therefore, to investigate CVD risk according to the glucose control status during early diabetes, we examined visit-to-visit HbA1c variability among patients with type 2 diabetes (T2DM). @*Methods@#In this 9-year retrospective study, we measured HbA1c levels at each visit and tracked the change in HbA1c levels for 3 years after the first presentation (observation window) in newly diagnosed T2DM patients. We later assessed the occurrence of CVD in the last 3 years (target outcome window) of the study period after allowing a 3-year buffering window. The HbA1c variability score (HVS; divided into quartiles, HVS_Q1–4) was used to determine visit-to-visit HbA1c variability. @*Results@#Among 4,817 enrolled T2DM patients, the mean HbA1c level was < 7% for the first 3 years. The group with the lowest HVS had the lowest rate of CVD (9.4%; 104/1,109 patients).The highest incidence of CVD of 26.7% (8/30 patients) was found in HVS [≥ 9.0%]_Q3, which was significantly higher than that in HVS [6.0–6.9%]_Q1 (P = 0.006), HVS [6.0–6.9%]_Q2 (P = 0.013), HVS [6.0–6.9%]_Q3 (P = 0.018), and HVS [7.0–7.9%]_Q3 (P = 0.040). @*Conclusion@#To our knowledge, this is the first long-term study to analyze the importance of both HbA1c change and visit-to-visit HbA1c variability during outpatient visits within the first 3 years. Lowering glucose levels during early diabetes may be more critical than reducing visit-to-visit HbA1c variability.
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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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Almost every Korean (97%) is enrolled in the National Health Insurance program, and most receive medical treatment at least once a year. Data are collected by the Health Insurance Review and Assessment Service (HIRA), and the results of the review are sent to the National Health Insurance Service (NHIS). The data handled by NHIS and HIRA cover almost the entire population and can be used for various research purposes. NHIS and HIRA support research by making these data available to researchers. The greatest advantage of these data is that they are the only data which include virtually the entire population. Both HIRA and NHIS data are provided in the form of sample data and all (customized) data. NHIS and HIRA data are similar but exhibit minor differences. HIRA data consists of five tables, including general specification details, in-hospital treatment details, disease details, out-of-hospital prescription details, and nursing institution information. NHIS data include death records (including cause of death), some medical examination records, and the socio-economic variables of the subject, such as income, in addition to all the HIRA data. Clinical results of treatments are not recorded in NHIS or HIRA. However, because public data are used for billing purposes, actual research has thus far been limited. Therefore, researchers must develop a study design that can minimize the errors or bias occurring during the course of the study. Therefore, it is necessary to clearly understand the structure and characteristics of NHIS and HIRA data when initiating research.
ABSTRACT
Background@#There are many models for predicting diabetes mellitus (DM), but their clinical implication remains vague. Therefore, we aimed to create various DM prediction models using easily accessible health screening test parameters. @*Methods@#Two sets of variables were used to develop eight DM prediction models. One set comprised 62 easily accessible examination results of commonly used variables from a tertiary university hospital. The second set comprised 27 of the 62 variables included in the national routine health checkups. Gradient boosting and random forest algorithms were used to develop the models. Internal validation was performed using the stratified 10-fold cross-validation method. @*Results@#The area under the receiver operating characteristic curve (ROC-AUC) for the 62-variable DM model making 12-month predictions for subjects without diabetes was the largest (0.928) among those of the eight DM prediction models. The ROC-AUC dropped by more than 0.04 when training with the simplified 27-variable set but still showed fairly good performance with ROC-AUCs between 0.842 and 0.880. The accuracy was up to 11.5% higher (from 0.807 to 0.714) when fasting glucose was included. @*Conclusion@#We created easily applicable diabetes prediction models that deliver good performance using parameters commonly assessed during tertiary university hospital and national routine health checkups. We plan to perform prospective external validation, hoping that the developed DM prediction models will be widely used in clinical practice.
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Drug repositioning is a strategy for identifying new applications of an existing drug that has been previously proven to be safe. Based on several examples of drug repositioning, we aimed to determine the methodologies and relevant steps associated with drug repositioning that should be pursued in the future. Reports on drug repositioning, retrieved from PubMed from January 2011 to December 2020, were classified based on an analysis of the methodology and reviewed by experts. Among various drug repositioning methods, the network-based approach was the most common (38.0%, 186/490 cases), followed by machine learning/deep learningbased (34.3%, 168/490 cases), text mining-based (7.1%, 35/490 cases), semantic-based (5.3%, 26/490 cases), and others (15.3%, 75/490 cases). Although drug repositioning offers several advantages, its implementation is curtailed by the need for prior, conclusive clinical proof. This approach requires the construction of various databases, and a deep understanding of the process underlying repositioning is quintessential. An in-depth understanding of drug repositioning could reduce the time, cost, and risks inherent to early drug development, providing reliable scientific evidence. Furthermore, regarding patient safety, drug repurposing might allow the discovery of new relationships between drugs and diseases.
ABSTRACT
Background@#Elevated γ-glutamyl transferase (γ-GTP) level is associated with metabolic syndrome, impaired glucose tolerance, and insulin resistance, which are risk factors for type 2 diabetes. We aimed to investigate the association of cumulative exposure to high γ-GTP level with risk of diabetes. @*Methods@#Using nationally representative data from the Korean National Health Insurance system, 346,206 people who were free of diabetes and who underwent 5 consecutive health examinations from 2005 to 2009 were followed to the end of 2018. High γ-GTP level was defined as those in the highest quartile, and the number of exposures to high γ-GTP level ranged from 0 to 5. Hazard ratio (HR) and 95% confidence interval (CI) for diabetes were analyzed using the multivariable Cox proportional-hazards model. @*Results@#The mean follow-up duration was 9.2±1.0 years, during which 15,183 (4.4%) patients developed diabetes. There was a linear increase in the incidence rate and the risk of diabetes with cumulative exposure to high γ-GTP level. After adjusting for possible confounders, the HR of diabetes in subjects with five consecutive high γ-GTP levels were 2.60 (95% CI, 2.47 to 2.73) in men and 3.05 (95% CI, 2.73 to 3.41) in women compared with those who never had a high γ-GTP level. Similar results were observed in various subgroup and sensitivity analyses. @*Conclusion@#There was a linear relationship between cumulative exposure to high γ-GTP level and risk of diabetes. Monitoring and lowering γ-GTP level should be considered for prevention of diabetes in the general population.
ABSTRACT
Smart healthcare systems are being designed to provide medical services to and improve the daily lives of older adults. However, most research has been focused on technical issues, despite a need to conduct in-depth studies on related ethical issues. Therefore, this study aimed to examine ethical issues in smart healthcare for older adults. We reviewed published literature using PubMed. In total, 292 documents were analyzed by applying the scoping review method. Finally, 29 articles were selected from the 292 articles. Ethical issues in smart healthcare for older adults were analyzed in terms of the themes of responsibility/autonomy (n=10), privacy (n=9), and digital divide (n=10). Technical help provided by smart healthcare may infringe on the autonomy of tacit choice for older adults. This pose a potential ethical issue as the subject of responsibility here is unclear. Privacy is a concern as smart technology may intrude the personal life of the user. The digital divide is a challenge because of low responsiveness from older adults to technological changes. The future development and application of smart healthcare systems must take these ethical aspects into account to enable their efficient and effective use in supplementing healthcare for older adults. Critical discussions to identify ethical issues and customize ethical requirements for specific user needs are necessary among smart healthcare providers.
ABSTRACT
With the introduction of electronic medical records (EMRs), it has become possible to accumulate massive amounts of qualitative medical data. As such, EMRs have become increasingly used in clinical decision support systems (CDSSs). While CDSSs aim to reduce medical errors normally occurring in the process of treating patients by physicians, technical maturity and the completeness of CDSSs do not meet standards for medical use yet. As data further accumulates, CDSS algorithms must be continuously updated to allow CDSSs to perform their core functions. Doing so, however, requires extensive time and manpower investments. In current practice, computational systems already perform a wide variety of functions in medical settings to allow medical staff to focus on other tasks. However, no prior research has evaluated the potential effectiveness of future CDSSs nor analyzed possibilities for their further development. In this article, we evaluate CDSS technology with the consideration that medical staff also understand the core functions of such systems.
ABSTRACT
Personal medical information is an essential resource for research; however, there are laws that regulate its use, and it typically has to be pseudonymized or anonymized. When data are anonymized, the quantity and quality of extractable information decrease significantly.From the perspective of a clinical researcher, a method of achieving pseudonymized data without degrading data quality while also preventing data loss is proposed herein. As the level of pseudonymization varies according to the research purpose, the pseudonymization method applied should be carefully chosen. Therefore, the active participation of clinicians is crucial to transform the data according to the research purpose. This can contribute to data security by simply transforming the data through secondary data processing. Case studies demonstrated that, compared with the initial baseline data, there was a clinically significant difference in the number of datapoints added with the participation of a clinician (from 267,979 to 280,127 points, P < 0.001). Thus, depending on the degree of clinician participation, data anonymization may not affect data quality and quantity, and proper data quality management along with data security are emphasized. Although the pseudonymization level and clinical use of data have a trade-off relationship, it is possible to create pseudonymized data while maintaining the data quality required for a given research purpose. Therefore, rather than relying solely on security guidelines, the active participation of clinicians is important.
ABSTRACT
Background@#We analyzed hemoglobin A1c (HbA1c) levels and various lung function test results in healthy individuals after a 6-year follow-up period to explore the influence of lung function changes on glycemic control. @*Methods@#Subjects whose HbA1c levels did not qualify as diabetes mellitus (DM) and who had at least two consecutive lung function tests were selected among the people who visited a health promotion center. Lung function parameters, including forced expiratory volume in 1 second (FEV1), forced vital capacity (FVC), FEV/FVC ratio, and forced expiratory flow 25% to 75% (FEF25%−75%), were divided into four groups based on their baseline quantiles. To evaluate future DM onset risk in relation to lung function changes, the correlation between baseline HbA1c levels and changes in lung function parameters after a 6-year follow-up period was analyzed. @*Results@#Overall, 17,568 individuals were included; 0.9% of the subjects were diagnosed with DM. The individuals included in the quartile with FEV1/FVC ratio values of 78% to 82% had lower risk of DM than those in the quartile with FEV1/FVC ratio values of ≥86% after adjusting for age, sex, and body mass index (P=0.04). Baseline percent predicted FEV1, FVC, FEV1/FVC ratio, and FEF25%−75%, and differences in the FEV1/FVC ratio or FEF25%−75%, showed negative linear correlations with baseline HbA1c levels. @*Conclusion@#Healthy subjects with FEV1/FVC ratio values between 78% and 82% had 40% lower risk for future DM. Smaller differences and lower baseline FEV1/FVC ratio or FEF25%−75% values were associated with higher baseline HbA1c levels. These findings suggest that airflow limitation affects systemic glucose control and that the FEV1/FVC ratio could be one of the factors predicting future DM risk in healthy individuals.
ABSTRACT
The importance of adopting healthy exercise routines has been repeatedly emphasized to individuals with diabetes mellitus (DM). However, knowledge about the risk of exercise-induced hypoglycemia is limited. Regular exercise reduces and delays the onset of DM-related complications particularly in individuals who already have DM. However, an excessive exercise can lead to hypoglycemia. Excessive exercise in the evening can cause hypoglycemia while sleeping. Furthermore, if individuals with DM want to have a greater amount of exercise, the exercise duration rather than intensity must be increased. In weight resistance exercises, it is beneficial to first increase the number of repetitions, followed by the number of sets and gradually the weight of resistance. When performing intermittent high-intensity training within a short time period, hypoglycemia may develop for an extended period after exercise. In addition to adjusting exercise regimens, the medication doses must be modified accordingly. Delaying exercise, adjusting the number of snacks consumed prior to exercise, reducing insulin dose before exercise, and injecting insulin into the abdomen rather than the limbs prevent exercise-induced hypoglycemia prior to a spontaneous exercise. Ultimately, with personal knowledge on how to prevent hypoglycemia, the effects of exercise can be maximized in individuals with DM, and a healthy lifestyle can prevent future complications.
ABSTRACT
Hypoglycemia is one of the severe complications of diabetes. To prevent hypoglycemia, an emphasis is placed on maintaining an appropriate balance between nutrition, activity, and treatment, which can be achieved by the repetition of self-trials based on self-monitoring. Clinicians routinely focus on patients’ contribution, including timely intake of an adequate amount of carbohydrates, physical activity, antidiabetic medication, and abstinence from alcohol. Recently, many guidelines have highlighted the importance of clinicians’ factors and recommend individualized treatments according to lifestyle patterns and specific needs following the de-intensification of treatment. The optimal value of hemoglobin A1c (HbA1c) levels for blood glucose level regulation remains controversial among countries, but it generally does not exceed 8.0%. In populations that are at a risk of hypoglycemia, such as the older adults, it is advisable to adjust the target blood glucose level to less than 8.0%. Meanwhile, a blood glucose level of 7.0%–7.5% is generally recommended for healthy older adults. If the expected lifetime is shorter than 10 years or in patients with chronic kidney disease and severe cardiovascular disease, the HbA1c level target can be increased to 7.5%–8.0%. For even shorter lifetime expectancy, the target can be adjusted up to 8.0%–9.0%. To prevent hypoglycemia, the target blood glucose level needs to be adjusted, particularly in older adult patients. Ultimately, it is important to identify the maximum blood glucose levels that do not cause hypoglycemia and the minimum blood glucose levels that do not cause hyperglycemia-associated complications.
ABSTRACT
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.
ABSTRACT
Due to the coronavirus disease 2019 (COVID-19) outbreak, consultation and prescription via telemedicine were temporarily allowed in the Korean population. However, at this point, it is difficult to determine whether telemedicine fulfills its role as a health care strategy. Arguably, if we had enough previous experience with telemedicine or sufficient preparation for its application, telemedicine could be more smoothly and flexibly adopted in the medical field.As it is still not possible to predict when the COVID-19 pandemic will end, phone consultation and prescription are likely to continue for some time. Hence, it is expected that telemedicine will naturally settle in the medical field in the near future. However, as we have noticed during this outbreak, improvised telemedicine without adequate guidance can be confusing to both patients and health professionals, thus reducing the benefit to patients. Medical staff requires preparation on how to appropriately use telemedicine. Thus, here we present some suggestions on implementing and preparing for telemedicine in the medical community.
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Objectives@#Digital healthcare is expected to play a pivotal role in patient-centered healthcare. It empowers patients by informing, communicating, and motivating them. However, a pragmatic evaluation of the present status of digital healthcare has not been presented; therefore, we aimed to examine the status of digital healthcare in Korea. @*Methods@#This article discusses digital healthcare, examples of assessment in Korea and other countries, the implications of past examples, and future directions for development. @*Results@#Over the years, various clinical studies have used clinical evidence to assess the feasibility of digital healthcare. If feasible, it is actually clinically effective. If it is effective, can it be commercialized at an acceptable cost? These questions have been investigated in various evidence-based studies. In addition, great efforts are being made to secure ample evidence to assess various aspects of digital healthcare, such as safety, quality, end-user experience, and equity. @*Conclusions@#Digital healthcare requires a deep understanding of both the technical and medical aspects. To strengthen the competence of the medical aspect, medical staff, patients, and the government must work together with continuous interest in this goal.
ABSTRACT
The importance of adopting healthy exercise routines has been repeatedly emphasized to individuals with diabetes mellitus (DM). However, knowledge about the risk of exercise-induced hypoglycemia is limited. Regular exercise reduces and delays the onset of DM-related complications particularly in individuals who already have DM. However, an excessive exercise can lead to hypoglycemia. Excessive exercise in the evening can cause hypoglycemia while sleeping. Furthermore, if individuals with DM want to have a greater amount of exercise, the exercise duration rather than intensity must be increased. In weight resistance exercises, it is beneficial to first increase the number of repetitions, followed by the number of sets and gradually the weight of resistance. When performing intermittent high-intensity training within a short time period, hypoglycemia may develop for an extended period after exercise. In addition to adjusting exercise regimens, the medication doses must be modified accordingly. Delaying exercise, adjusting the number of snacks consumed prior to exercise, reducing insulin dose before exercise, and injecting insulin into the abdomen rather than the limbs prevent exercise-induced hypoglycemia prior to a spontaneous exercise. Ultimately, with personal knowledge on how to prevent hypoglycemia, the effects of exercise can be maximized in individuals with DM, and a healthy lifestyle can prevent future complications.
ABSTRACT
Hypoglycemia is one of the severe complications of diabetes. To prevent hypoglycemia, an emphasis is placed on maintaining an appropriate balance between nutrition, activity, and treatment, which can be achieved by the repetition of self-trials based on self-monitoring. Clinicians routinely focus on patients’ contribution, including timely intake of an adequate amount of carbohydrates, physical activity, antidiabetic medication, and abstinence from alcohol. Recently, many guidelines have highlighted the importance of clinicians’ factors and recommend individualized treatments according to lifestyle patterns and specific needs following the de-intensification of treatment. The optimal value of hemoglobin A1c (HbA1c) levels for blood glucose level regulation remains controversial among countries, but it generally does not exceed 8.0%. In populations that are at a risk of hypoglycemia, such as the older adults, it is advisable to adjust the target blood glucose level to less than 8.0%. Meanwhile, a blood glucose level of 7.0%–7.5% is generally recommended for healthy older adults. If the expected lifetime is shorter than 10 years or in patients with chronic kidney disease and severe cardiovascular disease, the HbA1c level target can be increased to 7.5%–8.0%. For even shorter lifetime expectancy, the target can be adjusted up to 8.0%–9.0%. To prevent hypoglycemia, the target blood glucose level needs to be adjusted, particularly in older adult patients. Ultimately, it is important to identify the maximum blood glucose levels that do not cause hypoglycemia and the minimum blood glucose levels that do not cause hyperglycemia-associated complications.