This manuscript represents the official position of the Korean Society of Echocardiography on valvular heart diseases.This position paper focuses on the clinical management of valvular heart diseases with reference to the guidelines recently published by the American College of Cardiology/American Heart Association and the European Society of Cardiology. The committee tried to reflect the recently published results on the topic of valvular heart diseases and Korean data by a systematic literature search based on validity and relevance. In part I of this article, we will review and discuss the current position of aortic valve disease in Korea.

This manuscript represents the official position of the Korean Society of Echocardiography on valvular heart diseases.This position paper focuses on the diagnosis and management of valvular heart diseases with referring to the guide‑ lines recently published by the American College of Cardiology/American Heart Association and the European Society of Cardiology. The committee sought to reflect national data on the topic of valvular heart diseases published to date through a systematic literature search based on validity and relevance. In the part II of this article, we intend to pre‑ sent recommendations for diagnosis and treatment of mitral valve disease and tricuspid valve disease.

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.

This study aimed to evaluate the differences in the baseline characteristics and patterns of antibiotic usage among hospitals based on their participation in the Korea National Antimicrobial Use Analysis System (KONAS). We obtained claims data from the National Health Insurance for inpatients admitted to all secondary- and tertiary-care hospitals between January 2020 and December 2021 in Korea. 15.9% (58/395) of hospitals were KONAS participants, among which the proportion of hospitals with > 900 beds (31.0% vs.2.6%, P < 0.001) and tertiary care (50.0% vs. 5.2%, P < 0.001) was higher than that among non-participants. The consumption of antibiotics targeting antimicrobial-resistant gram positive bacteria (33.7 vs. 27.1 days of therapy [DOT]/1,000 patient-days, P = 0.019) and antibiotics predominantly used for resistant gram-negative bacteria (4.8 vs. 3.7 DOT/1,000 patient-days, P = 0.034) was higher in KONAS-participating versus -non-participating hospitals. The current KONAS data do not fully represent all secondary- and tertiary-care hospitals in Korea; thus, the KONAS results should be interpreted with caution.

The current guidelines for therapeutic drug monitoring (TDM) of vancomycin suggest a target 24-hour area under the curve (AUC 0-24 ) of 400 to 600 mg*h/L for serious methicillinresistant Staphylococcus aureus infections. In this study, the predictabilities of acute kidney injury (AKI) of various TDM target parameters, target levels, and sampling methods were evaluated in patients who underwent TDM from January 2020 to December 2020. The AUC 0-24 and trough values were calculated by both one- and two-point sampling methods, and were evaluated for the predictability of AKI. Among the AUC 0-24 cutoff comparisons, the threshold value of 500 mg*h/L in the two sampling methods was statistically significant (P = 0.042) when evaluated for the predictability of AKI. Analysis by an receiver operating characteristic curve estimated an AUC 0-24 cutoff value of 563.45 mg*h/L as a predictor of AKI, and was proposed as the upper limit of TDM target.

Despite the availability of direct-acting antivirals (DAAs) for chronic hepatitis C virus (HCV) infection in Korea, need remains for pangenotypic regimens that can be used in the presence of hepatic impairment, comorbidities, or prior treatment failure. We investigated the efficacy and safety of sofosbuvir–velpatasvir and sofosbuvir–velpatasvir–voxilaprevir for 12 weeks in HCV-infected Korean adults. Methods: This Phase 3b, multicenter, open-label study included 2 cohorts. In Cohort 1, participants with HCV genotype 1 or 2 and who were treatment-naive or treatment-experienced with interferon-based treatments, received sofosbuvir–velpatasvir 400/100 mg/day. In Cohort 2, HCV genotype 1 infected individuals who previously received an NS5A inhibitor-containing regimen ≥ 4 weeks received sofosbuvir–velpatasvir–voxilaprevir 400/100/100 mg/day. Decompensated cirrhosis was an exclusion criterion. The primary endpoint was SVR12, defined as HCV RNA < 15 IU/mL 12 weeks following treatment. Results: Of 53 participants receiving sofosbuvir–velpatasvir, 52 (98.1%) achieved SVR12. The single participant who did not achieve SVR12 experienced an asymptomatic Grade 3 ASL/ALT elevation on day 15 and discontinued treatment. The event resolved without intervention. All 33 participants (100%) treated with sofosbuvir–velpatasvir–voxilaprevir achieved SVR 12. Overall, sofosbuvir–velpatasvir and sofosbuvir–velpatasvir–voxilaprevir were safe and well tolerated. Three participants (5.6%) in Cohort 1 and 1 participant (3.0%) in Cohort 2 had serious adverse events, but none were considered treatment-related. No deaths or grade 4 laboratory abnormalities were reported. Conclusions: Treatment with sofosbuvir–velpatasvir or sofosbuvir–velpatasvir–voxilaprevir was safe and resulted in high SVR12 rates in Korean HCV patients.

These guidelines were developed as a part of the 2021 Academic R&D Service Project of the Korea Disease Control and Prevention Agency in response to requests from healthcare professionals in clinical practice for guidance on developing antimicrobial stewardship programs (ASPs). These guidelines were developed by means of a systematic literature review and a summary of recent literature, in which evidence-based intervention methods were used to address key questions about the appropriate use of antimicrobial agents and ASP expansion. These guidelines also provide evidence of the effectiveness of ASPs and describe intervention methods applicable in Korea.