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.

OBJECTIVES: This study developed an algorithm for identifying pregnancy episodes and estimating the last menstrual period (LMP) in an administrative claims database and applied it to investigate the use of pregnancy-incompatible immunosuppressants among pregnant women with systemic lupus erythematosus (SLE). METHODS: An algorithm was developed and applied to a nationwide claims database in Korea. Pregnancy episodes were identified using a hierarchy of pregnancy outcomes and clinically plausible periods for subsequent episodes. The LMP was estimated using preterm delivery, sonography, and abortion procedure codes. Otherwise, outcome-specific estimates were applied, assigning a fixed gestational age to the corresponding pregnancy outcome. The algorithm was used to examine the prevalence of pregnancies and utilization of pregnancy-incompatible immunosuppressants (cyclophosphamide [CYC]/mycophenolate mofetil [MMF]/methotrexate [MTX]) and non-steroidal anti-inflammatory drugs (NSAIDs) during pregnancy in SLE patients. RESULTS: The pregnancy outcomes identified in SLE patients included live births (67%), stillbirths (2%), and abortions (31%). The LMP was mostly estimated with outcome-specific estimates for full-term births (92.3%) and using sonography procedure codes (54.7%) and preterm delivery diagnosis codes (37.9%) for preterm births. The use of CYC/MMF/MTX decreased from 7.6% during preconception to 0.2% at the end of pregnancy. CYC/MMF/MTX use was observed in 3.6% of women within 3 months preconception and 2.5% during 0-7 weeks of pregnancy. CONCLUSIONS This study presents the first pregnancy algorithm using a Korean administrative claims database. Although further validation is necessary, this study provides a foundation for evaluating the safety of medications during pregnancy using secondary databases in Korea, especially for rare diseases.

Background and Objectives: This study aimed to analyze the outcomes of Fontan surgery in the Republic of Korea, as there were only a few studies from Asian countries. Methods: The medical records of 1,732 patients who underwent Fontan surgery in 10 cardiac centers were reviewed. Results: Among them, 1,040 (58.8%) were men. The mean age at Fontan surgery was 4.3±4.2 years, and 395 (22.8%) patients presented with heterotaxy syndrome. According to the types of Fontan surgery, 157 patients underwent atriopulmonary (AP) type; 303, lateral tunnel (LT) type; and 1,266, extracardiac conduit (ECC) type. The overall survival rates were 91.7%, 87.1%, and 74.4% at 10, 20, and 30 years, respectively. The risk factors of early mortality were male, heterotaxy syndrome, AP-type Fontan surgery, high mean pulmonary artery pressure (mPAP) in pre-Fontan cardiac catheterization, and early Fontan surgery year. The risk factors of late mortality were heterotaxy syndrome, genetic disorder, significant atrioventricular valve regurgitation (AVVR) before Fontan surgery, high mPAP in pre-Fontan cardiac catheterization, and no fenestration. Conclusions In Asian population with a high incidence of heterotaxy syndrome, the heterotaxy syndrome was identified as the poor prognostic factors for Fontan surgery. The preoperative low mPAP and less AVVR are associated with better early and long-term outcomes of Fontan surgery.

Background and Objectives: This study aimed to analyze the outcomes of Fontan surgery in the Republic of Korea, as there were only a few studies from Asian countries. Methods: The medical records of 1,732 patients who underwent Fontan surgery in 10 cardiac centers were reviewed. Results: Among them, 1,040 (58.8%) were men. The mean age at Fontan surgery was 4.3±4.2 years, and 395 (22.8%) patients presented with heterotaxy syndrome. According to the types of Fontan surgery, 157 patients underwent atriopulmonary (AP) type; 303, lateral tunnel (LT) type; and 1,266, extracardiac conduit (ECC) type. The overall survival rates were 91.7%, 87.1%, and 74.4% at 10, 20, and 30 years, respectively. The risk factors of early mortality were male, heterotaxy syndrome, AP-type Fontan surgery, high mean pulmonary artery pressure (mPAP) in pre-Fontan cardiac catheterization, and early Fontan surgery year. The risk factors of late mortality were heterotaxy syndrome, genetic disorder, significant atrioventricular valve regurgitation (AVVR) before Fontan surgery, high mPAP in pre-Fontan cardiac catheterization, and no fenestration. Conclusions In Asian population with a high incidence of heterotaxy syndrome, the heterotaxy syndrome was identified as the poor prognostic factors for Fontan surgery. The preoperative low mPAP and less AVVR are associated with better early and long-term outcomes of Fontan surgery.

Background and Objectives: This study aimed to analyze the outcomes of Fontan surgery in the Republic of Korea, as there were only a few studies from Asian countries. Methods: The medical records of 1,732 patients who underwent Fontan surgery in 10 cardiac centers were reviewed. Results: Among them, 1,040 (58.8%) were men. The mean age at Fontan surgery was 4.3±4.2 years, and 395 (22.8%) patients presented with heterotaxy syndrome. According to the types of Fontan surgery, 157 patients underwent atriopulmonary (AP) type; 303, lateral tunnel (LT) type; and 1,266, extracardiac conduit (ECC) type. The overall survival rates were 91.7%, 87.1%, and 74.4% at 10, 20, and 30 years, respectively. The risk factors of early mortality were male, heterotaxy syndrome, AP-type Fontan surgery, high mean pulmonary artery pressure (mPAP) in pre-Fontan cardiac catheterization, and early Fontan surgery year. The risk factors of late mortality were heterotaxy syndrome, genetic disorder, significant atrioventricular valve regurgitation (AVVR) before Fontan surgery, high mPAP in pre-Fontan cardiac catheterization, and no fenestration. Conclusions In Asian population with a high incidence of heterotaxy syndrome, the heterotaxy syndrome was identified as the poor prognostic factors for Fontan surgery. The preoperative low mPAP and less AVVR are associated with better early and long-term outcomes of Fontan surgery.

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.