Diabetes mellitus is a heterogeneous disease that encompasses a wide range of conditions, from mild cases to severe conditions where survival depends on insulin therapy. The Korean Diabetes Association Task Force Team for Diabetes with β-Cell Failure has established the term to classify severe refractory disease with β-cell failure. Individuals with β-cell failure are at high risk of diabetes-related complications. We propose that diabetes with β-cell failure can be diagnosed when individuals treated with multiple daily insulin injections or insulin pumps meet at least one of the following criteria: fasting C-peptide ≤ 0.6 ng/mL, non-fasting C-peptide ≤ 1.8 ng/mL, 24-hour urine C-peptide < 30 μg/day, or spot urine C-peptide/creatinine ratio ≤ 0.6 nmol/mmol. Among cases of diabetes with β-cell failure, β-cell failure with absolute insulin deficiency can be diagnosed when at least one of the following criteria is met: fasting C-peptide < 0.24 ng/mL, non-fasting C-peptide < 0.6 ng/mL, or spot urine C-peptide/ creatinine ratio < 0.2 nmol/mmol. Multiple daily insulin injections with long-acting insulin analogs and rapid-acting insulin analogs or insulin pumps are required for treatment of diabetes with β-cell failure. Continuous glucose monitoring and an automated insulin delivery system, sensor-augmented pump, or smart insulin pen, along with structured education, are necessary. We call for improvements in the relevant systems to ensure that such treatments can be provided.

Diabetes mellitus is a heterogeneous disease that encompasses a wide range of conditions, from mild cases to severe conditions where survival depends on insulin therapy. The Korean Diabetes Association Task Force Team for Diabetes with β-Cell Failure has established the term to classify severe refractory disease with β-cell failure. Individuals with β-cell failure are at high risk of diabetes-related complications. We propose that diabetes with β-cell failure can be diagnosed when individuals treated with multiple daily insulin injections or insulin pumps meet at least one of the following criteria: fasting C-peptide ≤ 0.6 ng/mL, non-fasting C-peptide ≤ 1.8 ng/mL, 24-hour urine C-peptide < 30 μg/day, or spot urine C-peptide/creatinine ratio ≤ 0.6 nmol/mmol. Among cases of diabetes with β-cell failure, β-cell failure with absolute insulin deficiency can be diagnosed when at least one of the following criteria is met: fasting C-peptide < 0.24 ng/mL, non-fasting C-peptide < 0.6 ng/mL, or spot urine C-peptide/ creatinine ratio < 0.2 nmol/mmol. Multiple daily insulin injections with long-acting insulin analogs and rapid-acting insulin analogs or insulin pumps are required for treatment of diabetes with β-cell failure. Continuous glucose monitoring and an automated insulin delivery system, sensor-augmented pump, or smart insulin pen, along with structured education, are necessary. We call for improvements in the relevant systems to ensure that such treatments can be provided.

Diabetes mellitus is a heterogeneous disease that encompasses a wide range of conditions, from mild cases to severe conditions where survival depends on insulin therapy. The Korean Diabetes Association Task Force Team for Diabetes with β-Cell Failure has established the term to classify severe refractory disease with β-cell failure. Individuals with β-cell failure are at high risk of diabetes-related complications. We propose that diabetes with β-cell failure can be diagnosed when individuals treated with multiple daily insulin injections or insulin pumps meet at least one of the following criteria: fasting C-peptide ≤ 0.6 ng/mL, non-fasting C-peptide ≤ 1.8 ng/mL, 24-hour urine C-peptide < 30 μg/day, or spot urine C-peptide/creatinine ratio ≤ 0.6 nmol/mmol. Among cases of diabetes with β-cell failure, β-cell failure with absolute insulin deficiency can be diagnosed when at least one of the following criteria is met: fasting C-peptide < 0.24 ng/mL, non-fasting C-peptide < 0.6 ng/mL, or spot urine C-peptide/ creatinine ratio < 0.2 nmol/mmol. Multiple daily insulin injections with long-acting insulin analogs and rapid-acting insulin analogs or insulin pumps are required for treatment of diabetes with β-cell failure. Continuous glucose monitoring and an automated insulin delivery system, sensor-augmented pump, or smart insulin pen, along with structured education, are necessary. We call for improvements in the relevant systems to ensure that such treatments can be provided.

Diabetes mellitus is a heterogeneous disease that encompasses a wide range of conditions, from mild cases to severe conditions where survival depends on insulin therapy. The Korean Diabetes Association Task Force Team for Diabetes with β-Cell Failure has established the term to classify severe refractory disease with β-cell failure. Individuals with β-cell failure are at high risk of diabetes-related complications. We propose that diabetes with β-cell failure can be diagnosed when individuals treated with multiple daily insulin injections or insulin pumps meet at least one of the following criteria: fasting C-peptide ≤ 0.6 ng/mL, non-fasting C-peptide ≤ 1.8 ng/mL, 24-hour urine C-peptide < 30 μg/day, or spot urine C-peptide/creatinine ratio ≤ 0.6 nmol/mmol. Among cases of diabetes with β-cell failure, β-cell failure with absolute insulin deficiency can be diagnosed when at least one of the following criteria is met: fasting C-peptide < 0.24 ng/mL, non-fasting C-peptide < 0.6 ng/mL, or spot urine C-peptide/ creatinine ratio < 0.2 nmol/mmol. Multiple daily insulin injections with long-acting insulin analogs and rapid-acting insulin analogs or insulin pumps are required for treatment of diabetes with β-cell failure. Continuous glucose monitoring and an automated insulin delivery system, sensor-augmented pump, or smart insulin pen, along with structured education, are necessary. We call for improvements in the relevant systems to ensure that such treatments can be provided.

Diabetes mellitus is a heterogeneous disease that encompasses a wide range of conditions, from mild cases to severe conditions where survival depends on insulin therapy. The Korean Diabetes Association Task Force Team for Diabetes with β-Cell Failure has established the term to classify severe refractory disease with β-cell failure. Individuals with β-cell failure are at high risk of diabetes-related complications. We propose that diabetes with β-cell failure can be diagnosed when individuals treated with multiple daily insulin injections or insulin pumps meet at least one of the following criteria: fasting C-peptide ≤ 0.6 ng/mL, non-fasting C-peptide ≤ 1.8 ng/mL, 24-hour urine C-peptide < 30 μg/day, or spot urine C-peptide/creatinine ratio ≤ 0.6 nmol/mmol. Among cases of diabetes with β-cell failure, β-cell failure with absolute insulin deficiency can be diagnosed when at least one of the following criteria is met: fasting C-peptide < 0.24 ng/mL, non-fasting C-peptide < 0.6 ng/mL, or spot urine C-peptide/ creatinine ratio < 0.2 nmol/mmol. Multiple daily insulin injections with long-acting insulin analogs and rapid-acting insulin analogs or insulin pumps are required for treatment of diabetes with β-cell failure. Continuous glucose monitoring and an automated insulin delivery system, sensor-augmented pump, or smart insulin pen, along with structured education, are necessary. We call for improvements in the relevant systems to ensure that such treatments can be provided.

Background: It is well known that a large number of patients with diabetes also have dyslipidemia, which significantly increases the risk of cardiovascular disease (CVD). This study aimed to evaluate the efficacy and safety of combination drugs consisting of metformin and atorvastatin, widely used as therapeutic agents for diabetes and dyslipidemia. Methods: This randomized, double-blind, placebo-controlled, parallel-group and phase III multicenter study included adults with glycosylated hemoglobin (HbA1c) levels >7.0% and <10.0%, low-density lipoprotein cholesterol (LDL-C) >100 and <250 mg/dL. One hundred eighty-five eligible subjects were randomized to the combination group (metformin+atorvastatin), metformin group (metformin+atorvastatin placebo), and atorvastatin group (atorvastatin+metformin placebo). The primary efficacy endpoints were the percent changes in HbA1c and LDL-C levels from baseline at the end of the treatment. Results: After 16 weeks of treatment compared to baseline, HbA1c showed a significant difference of 0.94% compared to the atorvastatin group in the combination group (0.35% vs. −0.58%, respectively; P<0.0001), whereas the proportion of patients with increased HbA1c was also 62% and 15%, respectively, showing a significant difference (P<0.001). The combination group also showed a significant decrease in LDL-C levels compared to the metformin group (−55.20% vs. −7.69%, P<0.001) without previously unknown adverse drug events. Conclusion The addition of atorvastatin to metformin improved HbA1c and LDL-C levels to a significant extent compared to metformin or atorvastatin alone in diabetes and dyslipidemia patients. This study also suggested metformin’s preventive effect on the glucose-elevating potential of atorvastatin in patients with type 2 diabetes mellitus and dyslipidemia, insufficiently controlled with exercise and diet. Metformin and atorvastatin combination might be an effective treatment in reducing the CVD risk in patients with both diabetes and dyslipidemia because of its lowering effect on LDL-C and glucose.

Cytomegalovirus (CMV) is the most important opportunistic viral pathogen in solid organ transplant (SOT) recipients.The Korean guideline for the prevention of CMV infection in SOT recipients was developed jointly by the Korean Society for Infectious Diseases and the Korean Society of Transplantation. CMV serostatus of both donors and recipients should be screened before transplantation to best assess the risk of CMV infection after SOT. Seronegative recipients receiving organs from seropositive donors face the highest risk, followed by seropositive recipients. Either antiviral prophylaxis or preemptive therapy can be used to prevent CMV infection. While both strategies have been demonstrated to prevent CMV infection post-transplant, each has its own advantages and disadvantages. CMV serostatus, transplant organ, other risk factors, and practical issues should be considered for the selection of preventive measures. There is no universal viral load threshold to guide treatment in preemptive therapy. Each institution should define and validate its own threshold.Valganciclovir is the favored agent for both prophylaxis and preemptive therapy. The evaluation of CMV-specific cellmediated immunity and the monitoring of viral load kinetics are gaining interest, but there was insufficient evidence to issue recommendations. Specific considerations on pediatric transplant recipients are included.

Background: Worldwide, sepsis is the leading cause of death in hospitals. If mortality rates in patients with sepsis can be predicted early, medical resources can be allocated efficiently. We constructed machine learning (ML) models to predict the mortality of patients with sepsis in a hospital emergency department. Methods: This study prospectively collected nationwide data from an ongoing multicenter cohort of patients with sepsis identified in the emergency department. Patients were enrolled from 19 hospitals between September 2019 and December 2020. For acquired data from 3,657 survivors and 1,455 deaths, six ML models (logistic regression, support vector machine, random forest, extreme gradient boosting [XGBoost], light gradient boosting machine, and categorical boosting [CatBoost]) were constructed using fivefold cross-validation to predict mortality. Through these models, 44 clinical variables measured on the day of admission were compared with six sequential organ failure assessment (SOFA) components (PaO 2 /FIO 2 [PF], platelets (PLT), bilirubin, cardiovascular, Glasgow Coma Scale score, and creatinine).The confidence interval (CI) was obtained by performing 10,000 repeated measurements via random sampling of the test dataset. All results were explained and interpreted using Shapley’s additive explanations (SHAP). Results: Of the 5,112 participants, CatBoost exhibited the highest area under the curve (AUC) of 0.800 (95% CI, 0.756–0.840) using clinical variables. Using the SOFA components for the same patient, XGBoost exhibited the highest AUC of 0.678 (95% CI, 0.626–0.730). As interpreted by SHAP, albumin, lactate, blood urea nitrogen, and international normalization ratio were determined to significantly affect the results. Additionally, PF and PLTs in the SOFA component significantly influenced the prediction results. Conclusion Newly established ML-based models achieved good prediction of mortality in patients with sepsis. Using several clinical variables acquired at the baseline can provide more accurate results for early predictions than using SOFA components. Additionally, the impact of each variable was identified.