Retrospective analysis on the diagnostic performances and signal-to-cut-off ratios of the Elecsys Anti-HCV II assay

Background: Anti-HCV assays are widely used as a screening tool for HCV infection. However, diagnostic performances and effective signal-to-cut-off ratios (S/COs) for predicting true HCV infections would vary according to the assays used. Thus, we evaluated the diagnostic performances of the new Elecsys Anti-HCV assay. Methods: A total of 41 694 cases tested by the Elecsys Anti-HCV II assay (Roche Diagnostics, Germany) during January 2013 to December 2015 were retrospectively analyzed by comparing with the diagnosis on HCV infections determined by patients' medical records and results of laboratory tests. Results: Excluding 62 cases with unclear history of HCV infection, 430 and 41 202 cases were respectively assorted as "true infection" and "no evidence of infection," and 99.85% of the initial results by the Elecsys assay were concordant with the diagnosis on HCV infection. Sensitivity, specificity, positive and negative predictive values were respectively 99.30%, 99.86%, 88.04%, and 99.99%, where the prevalence of the HCV infection was 1.0%. The area under the receiver operating characteristics curve value of the Elecsys assay was 0.9980 (95% confidence interval [CI]=0.9944 to 1.0017). The S/CO by the Elecsys assay for predictive of a true-positive ≥95% of the time was 19.0 (95% CI=15.0 to 25.1). Conclusion: The Elecsys Anti-HCV II assay showed excellent diagnostic performances, particularly in terms of sensitivity, specificity, and NPV. However, the results obtained by this assay with S/CO lope

Major Bloodstream Infection-Causing Bacterial Pathogens and Their Antimicrobial Resistance in South Korea, 2017-2019: Phase I Report From Kor-GLASS

To monitor national antimicrobial resistance (AMR), the Korea Global AMR Surveillance System (Kor-GLASS) was established. This study analyzed bloodstream infection (BSI) cases from Kor-GLASS phase I from January 2017 to December 2019. Nine non-duplicated Kor-GLASS target pathogens, including Staphylococcus aureus, Enterococcus faecalis, Enterococcus faecium, Streptococcus pneumoniae, Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa, Acinetobacter spp., and Salmonella spp., were isolated from blood specimens from eight sentinel hospitals. Antimicrobial susceptibility testing, AMR genotyping, and strain typing were carried out. Among the 20,041 BSI cases, 15,171 cases were caused by one of the target pathogens, and 12,578 blood isolates were collected for the study. Half (1,059/2,134) of S. aureus isolates were resistant to cefoxitin, and 38.1% (333/873) of E. faecium isolates were resistant to vancomycin. Beta-lactamase-non-producing ampicillin-resistant and penicillin-resistant E. faecalis isolates by disk diffusion method were identified, but the isolates were confirmed as ampicillin-susceptible by broth microdilution method. Among E. coli, an increasing number of isolates carried the bla CTX-M-27 gene, and the ertapenem resistance in 1.4% (30/2,110) of K. pneumoniae isolates was mostly (23/30) conferred by K. pneumoniae carbapenemases. A quarter (108/488) of P. aeruginosa isolates were resistant to meropenem, and 30.5% (33/108) of those carried acquired carbapenemase genes. Over 90% (542/599) of A. baumannii isolates were imipenem-resistant, and all except one harbored the bla OXA-23 gene. Kor-GLASS provided comprehensive AMR surveillance data, and the defined molecular mechanisms of resistance helped us to better understand AMR epidemiology. Comparative analysis with other GLASS-enrolled countries is possible owing to the harmonized system provided by GLASS.ope

Current Status and Prospects of the National Antimicrobial Resistance Surveillance System, Kor-GLASS

Antimicrobial-resistant bacteria have been increasingly reported worldwide, and surveillance plays an important role in preventing the further dissemination of these organisms. The World Health Organization suggested the Global Antimicrobial Resistance Surveillance System (GLASS) as a part of a global action plan in 2015. The purpose of GLASS was to establish a worldwide surveillance system to collect standardized, comparable, and validated antimicrobial resistance (AMR) data, which would enable the comparison of AMR data by country. The Korean government established a new AMR surveillance system, namely Kor-GLASS, based on the GLASS platform in 2016. Kor-GLASS has several advantages over previous AMR systems: 1) standardized AMR data based on a strain-collection system, 2) characterization of multidrug-resistant clones by molecular epidemiologic evaluation, 3) collection of the clinical information related to bacterial isolates, and 4) an independent quality control center and the Kor-GLASS database. Based on a successful pilot program, the first phase of Kor-GLASS operated from 2017 to 2019, and the second phase (2020-2022) of the system is now underway. Kor-GLASS provides comprehensive AMR surveillance data, and the trends of AMR epidemiology are determined by molecular characterization. Furthermore, it enables a global comparison of AMR with that in other GLASS-enrolled countries owing to the harmonized platform. Kor-GLASS should be further improved to provide sustainable and reliable AMR data by establishing additional collecting centers for representativeness, covering community infection-associated AMR, and investigating emerging AMR.ope

Risk Factors of Severe Clostridioides difficile Infection; Sequential Organ Failure Assessment Score, Antibiotics, and Ribotypes

We aimed to determine whether the Sequential Organ Failure Assessment (SOFA) score predicts the prognosis of patients with Clostridioides difficile infection (CDI). In addition, the association between the type of antibiotic used and PCR ribotypes was analyzed. We conducted a propensity score (PS)-matched study and machine learning analysis using clinical data from all adult patients with confirmed CDI in three South Korean hospitals. A total of 5,337 adult patients with CDI were included in this study, and 828 (15.5%) were classified as having severe CDI. The top variables selected by the machine learning models were maximum body temperature, platelet count, eosinophil count, oxygen saturation, Glasgow Coma Scale, serum albumin, and respiratory rate. After propensity score-matching, the SOFA score, white blood cell (WBC) count, serum albumin level, and ventilator use were significantly associated with severe CDI (P < 0.001 for all). The log-rank test of SOFA score ≥ 4 significantly differentiated severe CDI patients from the non-severe group. The use of fluoroquinolone was more related to CDI patients with ribotype 018 strains than to ribotype 014/020 (P < 0.001). Even after controlling for other variables using propensity score matching analysis, we found that the SOFA score was a clinical predictor of severe CDI. We also demonstrated that the use of fluoroquinolones in hospital settings could be associated with the PCR ribotype in patients with CDI.ope

Mortality prediction of patients in intensive care units using machine learning algorithms based on electronic health records

Improving predictive models for intensive care unit (ICU) inpatients requires a new strategy that periodically includes the latest clinical data and can be updated to reflect local characteristics. We extracted data from all adult patients admitted to the ICUs of two university hospitals with different characteristics from 2006 to 2020, and a total of 85,146 patients were included in this study. Machine learning algorithms were trained to predict in-hospital mortality. The predictive performance of conventional scoring models and machine learning algorithms was assessed by the area under the receiver operating characteristic curve (AUROC). The conventional scoring models had various predictive powers, with the SAPS III (AUROC 0.773 [0.766-0.779] for hospital S) and APACHE III (AUROC 0.803 [0.795-0.810] for hospital G) showing the highest AUROC among them. The best performing machine learning models achieved an AUROC of 0.977 (0.973-0.980) in hospital S and 0.955 (0.950-0.961) in hospital G. The use of ML models in conjunction with conventional scoring systems can provide more useful information for predicting the prognosis of critically ill patients. In this study, we suggest that the predictive model can be made more robust by training with the individual data of each hospital.ope

통계자료를 활용한 농산물의 탄소배출량 산정

학위논문(석사)--아주대학교 일반대학원 :환경공학과,2014. 8이 연구는 통계자료를 활용한 농산물의 온실가스 배출량 산정 방법 개발 및 농산물별 평균 온실가스 배출량 산정을 통해, 향후 탄소라벨링 관련 제도에서 농산물의 온실가스 배출계수로 활용되는 것을 목적으로 한다. 특정 농산물을 재배하는 전국의 모든 농가 또는 샘플링을 통해 선정된 일부 농가로부터 재배관련 데이터를 수집하고, 이를 바탕으로 통계데이터를 산출하는 것은 현실적으로 많은 어려움이 예상되었기에, 이 연구에서는 농촌진흥청에서 발간한 “농축산물소득자료집” 및 통계청에서 제공하는 “농산물생산비조사” 통계자료를 활용하였다. 통계자료를 통해 비료 및 경유, 전기, 기타농자재의 사용량 데이터를 수집하였으며, 작물보호제의 사용량은 한국작물보호협에서 발간한 “농약연보”를 활용하여 데이터를 수집하였다. 농산물의 온실가스 배출량 산정 기준은 농산물 관련 통계의 특성을 고려한 ”1,000㎡·1기작“과 농자재 투입에 따른 농산물 생산성을 고려한 ”농산물1kg“ 두 가지로 설정하였다. 농산물의 특성을 고려하여 농자재 생산 및 화석연료 연소, 기타농자재 폐기, 질소비료 사용, 논의 물대기에 의한 온실가스 배출량을 산정하였으며, 각 농산물의 최근 5년간 온실가스 배출량을 평균하여 63개 농산물의 평균 온실가스 배출량을 산정하였다. 본 연구에서는 통계자료를 도출하기 위해 활용된 최초 수집데이터가 아닌 가공이 완료되어 공개된 최종 데이터를 활용하였기에, 통계자료에 대한 연구가 부족한 것이 한계점으로 드러났다. 이에 따라 통계자료의 최초 수집데이터에 대한 불확도 분석 및 데이터 선별 연구가 필요하다.제1장 개요 1 제1절 연구 배경 1 제2절 연구 목적 2 제3절 연구 내용 및 방법 3 제2장 연구동향 4 제1절 국내 농산물 온실가스 배출량 연구 동향 4 1. 농자재 및 농산물 LCI DB 구축 4 2. 저탄소 농축산물 인증제 시범사업 추진 4 제2절 해외 온실가스 배출계수 연구 동향 5 1. 국외 LCI DB 구축 현황 5 2. 스위스 농업부문 LCI DB 구축 현황 7 3. 덴마크 농업부문 LCI DB 구축 현황 8 4. 일본 농업부문 LCI DB 구축 현황 9 제3장 농산물 온실가스배출량 산정 10 제1절 시스템경계 설정 10 제2절 데이터 수집 11 1. 데이터 출처 11 2. 데이터 수집 대상 및 수집기간 설정 12 3. 데이터 수집 17 제3절 농산물별 평균 온실가스 배출량 산정 30 1. 온실가스 배출계수 조사 및 선정 30 2. 기타농자재 폐기 시나리오 개발 30 3. 농산물 온실가스 배출량 산정 32 제4장 결론 및 향후 연구 46 제5장 참고 문헌 48 Abstract 49Maste

공중보건에 부담이 되는 Clostridium difficile 지역사회 감염의 증가

Background: Increasing rates of Clostridium difficile infection (CDI) have been reported mainly in Europe and North America; however, only limited reports have originated in Korea. The current epidemiology of CDI in the community could help to understand the outpatient healthcare environment and to extend infection control measures to outpatient settings. Methods: C. difficile isolates in NHIS Ilsan Hospital from 2012 to 2014 were included in this study. Clinical characteristics, acquisition types, and previous antimicrobial therapy were obtained via Electronic Medical Records. C. difficile culture was performed only in unformed stool. Toxin was positive by enzyme-linked fluorescent immunoassay (ELFA) in 247 specimens. In addition, toxin B and binary toxin gene were detected by PCR in 57 specimens. CDI was defined by toxigenic C. difficile isolation in unformed stool. Results: In the previous 3 years, 251 unduplicated C. difficile cases have been detected; 168 healthcare facility- associated hospital onset (HCFA-HO), 45 healthcare facility-associated community onset (HCFA-CO), and 38 community-associated (CA). Toxin positive rates by ELFA for toxin A&B were HCFA-HO 50.6% (84/166), HCFA-CO 41.9% (18/43), and CA 42.1% (16/38). Toxin positive rate by PCR for tcdB were HCFA-HO 62.9% (22/35), HCFA-CO 69.2% (9/13), and CA 100% (9/9). No binary toxin (cdtA/cdtB) was detected in 57 cases. Conclusion: Community-associated CDI may be underestimated in Goyang province, Korea, especially by commonly used ELFA toxin assay. The spread of community-associated CDI should be recognized as an increasing burden of public health.ope

An Automatic Diagnose Method for Radiographic Bone Loss and Periodontitis Staging Using Deep Learning

In this study, a deep learning hybrid framework was developed to automatically stage periodontitis in dental panoramic radiographs. The framework was proposed to automatically quantify the periodontal bone loss and classify periodontitis for each individual tooth into four stages according to the criteria that was proposed at the 2017 World Workshop. Radiographic bone level (or CEJ level) was detected using deep learning with a simple structure of the entire jaw in panoramic radiographs. Next, the percent ratio analysis of the radiographic bone loss combined the tooth long-axis with periodontal bone and CEJ levels. The percentage ratios can be used to automatically classify periodontal bone loss. Additionally, the number of missing teeth was quantified by detecting the position of the missing teeth in the panoramic radiographs. A multi-device study was also performed to verify the generality of the developed method. The mean absolute difference (MAD) between periodontitis stages by the automatic method and by the radiologists was 0.31 overall for all the teeth in the whole jaw. The MADs for the images from the multiple devices were 0.25, 0.34, and 0.35 for devices 1, 2, and 3, respectively. The developed method had a high accuracy, reliability, and generality when automatically diagnosing periodontal bone loss and the staging of periodontitis by the multi-device study.N

Construction of cooperative research center and application system development

유전체 협력연구 거점 구축 및 활용시스템 개발KGM413141