Background: This study aimed to identify the prevalence of workplace hazards and organizational protection resources according to the size of the enterprise in the manufacturing industry of the Republic of Korea. Methods: We analyzed data of waged workers (weighted N = 5,879) from the Fifth Korean Working Conditions Survey (2017). Enterprise sizes were categorized as “micro enterprises” (less than five employees), “small enterprises” (5–49 employees) and “medium-large enterprises” (50 or more employees). Self-reported exposure to 18 physical, chemical, ergonomic, and psychological hazards were measured. The presence of organizational protection resources such as a labor union, a safety delegate working at the company, designated spaces to deal with safety, and the provision of health and safety information was evaluated. Results: Compared to workers in medium-large enterprises, those in micro enterprises showed a higher proportion of exposure to most of physical, chemical, ergonomic, and psychological hazards, except for exposure to solvents, prolonged sitting, and experiencing a state of emotional unrest. On the other hand, workers in micro enterprises had the lowest proportion of access to organizational protection resources. Conclusion Our study demonstrates that manufacturing workers at the micro enterprise in the Republic of Korea are exposed to the most hazardous work environment and yet have access to the fewest organizational protection resources.

Purpose: Diffuse large B-cell lymphoma (DLBCL) is the most common hematologic malignancy worldwide. Although substantial improvement has been achieved by the frontline rituximab-based chemoimmunotherapy, up to 40%-50% of patients will eventually have relapsed or refractory disease, whose prognosis is extremely dismal. Materials and Methods: We have carried out two prospective cohort studies that include over 1,500 DLBCL patients treated with rituximab plus CHOP (#NCT01202448 and #NCT02474550). In the current report, we describe the outcomes of refractory DLBCL patients. Patients were defined to have refractory DLBCL if they met one of the followings, not achieving at least partial response after 4 or more cycles of R-CHOP; not achieving at least partial response after 2 or more cycles of salvage therapy; progressive disease within 12 months after autologous stem cell transplantation. Results: Among 1,581 patients, a total of 260 patients met the criteria for the refractory disease after a median time to progression of 9.1 months. The objective response rate of salvage treatment was 26.4%, and the complete response rate was 9.6%. The median overall survival (OS) was 7.5 months (95% confidence interval, 6.4 to 8.6), and the 2-year survival rate was 22.1%±2.8%. The median OS for each refractory category was not significantly different (p=0.529). Conclusion In line with the previous studies, the outcomes of refractory DLBCL patients were extremely poor, which necessitates novel approaches for this population.

Purpose: There are clinical unmet needs in predicting therapeutic response and precise strategy for the patient with advanced biliary tract cancer (BTC). We aimed to identify genomic alterations predicting therapeutic response and resistance to gemcitabine and cisplatin (Gem/Cis)-based chemotherapy in advanced BTC. Materials and Methods: Genomic analysis of advanced BTC multi-institutional cohorts was performed using targeted panel sequencing. Genomic alterations were analyzed integrating patients’ clinicopathologic data, including clinical outcomes of Gem/Cis-based therapy. Significance of genetic alterations was validated using clinical next-generation sequencing (NGS) cohorts from public repositories and drug sensitivity data from cancer cell lines. Results: 193 BTC patients from three cancer centers were analyzed. Most frequent genomic alterations were TP53 (55.5%), KRAS (22.8%), ARID1A (10.4%) alterations, and ERBB2 amplification (9.8%). Among 177 patients with BTC receiving Gem/Cis-based chemotherapy, ARID1A alteration was the only independent predictive molecular marker of primary resistance showing disease progression for 1st-line chemotherapy in the multivariate regression model (odds ratio, 3.12; p=0.046). In addition, ARID1A alteration was significantly correlated with inferior progression-free survival on Gem/Cis-based chemotherapy in the overall patient population (p=0.033) and in patients with extrahepatic cholangiocarcinoma (CCA) (p=0.041). External validation using public repository NGS revealed that ARID1A mutation was a significant predictor for poor survival in BTC patients. Investigation of multi-OMICs drug sensitivity data from cancer cell lines revealed that cisplatin-resistance was exclusively observed in ARID1A mutant bile duct cancer cells. Conclusion Integrative analysis with genomic alterations and clinical outcomes of the first-line Gem/Cis-based chemotherapy in advanced BTC revealed that patients with ARID1Aalterations showed a significant worse clinical outcome, especially in extrahepatic CCA. Well-designed prospective studies are mandatory to validate the predictive role of ARID1Amutation.

Background: Fractional flow reserve (FFR) based on computed tomography (CT) has been shown to better identify ischemia-causing coronary stenosis. However, this current technology requires high computational power, which inhibits its widespread implementation in clinical practice. This prospective, multicenter study aimed at validating the diagnostic performance of a novel simple CT based fractional flow reserve (CT-FFR) calculation method in patients with coronary artery disease. Methods: Patients who underwent coronary CT angiography (CCTA) within 90 days and invasive coronary angiography (ICA) were prospectively enrolled. A hemodynamically significant lesion was defined as an FFR ≤ 0.80, and the area under the receiver operating characteristic curve (AUC) was the primary measure. After the planned analysis for the initial algorithm A, we performed another set of exploratory analyses for an improved algorithm B. Results: Of 184 patients who agreed to participate in the study, 151 were finally analyzed.Hemodynamically significant lesions were observed in 79 patients (52.3%). The AUC was 0.71 (95% confidence interval [CI], 0.63–0.80) for CCTA, 0.65 (95% CI, 0.56–0.74) for CT-FFR algorithm A (P = 0.866), and 0.78 (95% CI, 0.70–0.86) for algorithm B (P = 0.112). Diagnostic accuracy was 0.63 (0.55–0.71) for CCTA alone, 0.66 (0.58–0.74) for algorithm A, and 0.76 (0.68–0.82) for algorithm B. Conclusion This study suggests the feasibility of automated CT-FFR, which can be performed on-site within several hours. However, the diagnostic performance of the current algorithm does not meet the a priori criteria for superiority. Future research is required to improve the accuracy.

Purpose: We aimed to investigate manifestations and patterns of care for patients with brain metastasis (BM) from breast cancer (BC) and compared their overall survival (OS) from 2005 through 2014 in Korea. Materials and Methods: We retrospectively reviewed 600 BC patients with BM diagnosed between 2005 and 2014. The median follow-up duration was 12.5 months. We categorized the patients into three groups according to the year when BM was initially diagnosed (group I [2005-2008], 98 patients; group II [2009-2011], 200 patients; and group III [2012-2014], 302 patients). Results: Over time, the median age at BM diagnosis increased by 2.2 years (group I, 49.0 years; group II, 48.3 years; and group III, 51.2 years; p=0.008). The percentage of patients with extracranial metastasis was 73.5%, 83.5%, and 86.4% for group I, II, and III, respectively (p=0.011). The time interval between BC and BM was prolonged in patients with stage III primary BC (median, 2.4 to 3 years; p=0.029). As an initial brain-directed treatment, whole-brain radiotherapy alone decreased from 80.0% in 2005 to 41.1% in 2014. Meanwhile, stereotactic radiosurgery or fractionated stereotactic radiotherapy alone increased from 13.3% to 34.7% during the same period (p=0.005). The median OS for group I, II, and III was 15.6, 17.9, and 15.0 months, respectively, with no statistical significance. Conclusion The manifestations of BM from BC and the pattern of care have changed from 2005 to 2014 in Korea. However, the OS has remained relatively unchanged over the 10 years.

Purpose: Febrile neutropenia (FN) can cause suboptimal treatment and treatment-related mortality (TRM) in diffuse large B-cell lymphoma (DLBCL) patients treated with rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisolone (R-CHOP). Materials and methods: We conducted a prospective cohort study to evaluate the effectiveness of pegfilgrastim prophylaxis in DLBCL patients receiving R-CHOP, and we compared them with the PROCESS cohort (n=485). Results: Since January 2015, 986 patients with DLBCL were enrolled. Pegfilgrastim was administered at least once in 930 patients (94.3%), covering 90.3% of all cycles. FN developed in 137 patients (13.9%) in this cohort (23.7% in the PROCESS cohort, p<0.001), and 4.2% of all cycles (10.2% in the PROCESS cohort, p<0.001). Dose delay was less common (≥3 days: 18.1% vs. 23.7%, p=0.015; ≥5 days: 12.0% vs. 18.3%, p=0.023) in this cohort than in the PROCESS cohort. The incidence of TRM (3.2% vs. 5.6%, p=0.047) and infection-related death (1.8% vs. 4.5%, p=0.004) was lower in this cohort than in the PROCESS cohort. The 4-year overall survival (OS) and progression-free survival (PFS) rates of the two cohorts were not different (OS: 73.0% vs. 71.9%, p=0.545; PFS: 69.5% vs. 68.8%, p=0.616). However, in patients aged ≥75 years, the 4-year OS and PFS rates were higher in this cohort than in the PROCESS cohort (OS: 49.6% vs. 33.7%, p=0.032; PFS: 44.2% vs. 30.3% p=0.047). Conclusion Pegfilgrastim prophylaxis is effective in the prevention of FN and infection-related death in DLBCL patients receiving R-CHOP, and it also improves OS in patients aged ≥75 years.

BACKGROUND: Severe fever with thrombocytopenia syndrome (SFTS) is an emerging infectious disease with high mortality in East Asia. This study aimed to develop, for primary care providers, a prediction score using initial symptoms and basic laboratory blood tests to differentiate between SFTS and other endemic zoonoses in Korea.METHODS: Patients aged ≥ 18 years diagnosed with endemic zoonoses during a 3-year period (between January 2015 and December 2017) were retrospectively enrolled from 4 tertiary university hospitals. A prediction score was built based on multivariate logistic regression analyses.RESULTS: Of 84 patients, 35 with SFTS and 49 with other endemic zoonoses were enrolled. In multivariate logistic regression analysis, independent predictors of SFTS included neurologic symptoms (odds ratio [OR], 12.915; 95% confidence interval [CI], 2.173–76.747), diarrhea (OR, 10.306; 95% CI, 1.588–66.895), leukopenia (< 4,000/mm3) (OR, 19.400; 95% CI, 3.290–114.408), and normal C-reactive protein (< 0.5 mg/dL) (OR, 24.739; 95% CI, 1.812–337.742). We set up a prediction score by assigning one point to each of these four predictors. A score of ≥ 2 had 82.9% sensitivity (95% CI, 71.7%–87.5%) and 95.9% specificity (95% CI, 88.0%–99.2%). The area under the curve of the clinical prediction score was 0.950 (95% CI, 0.903–0.997).CONCLUSION: This study finding suggests a simple and useful scoring system to predict SFTS in patients with endemic zoonoses. We expect this strategic approach to facilitate early differentiation of SFTS from other endemic zoonoses, especially by primary care providers, and to improve the clinical outcomes.

BACKGROUND: Severe fever with thrombocytopenia syndrome (SFTS) is an emerging infectious disease with high mortality in East Asia. This study aimed to develop, for primary care providers, a prediction score using initial symptoms and basic laboratory blood tests to differentiate between SFTS and other endemic zoonoses in Korea. METHODS: Patients aged ≥ 18 years diagnosed with endemic zoonoses during a 3-year period (between January 2015 and December 2017) were retrospectively enrolled from 4 tertiary university hospitals. A prediction score was built based on multivariate logistic regression analyses. RESULTS: Of 84 patients, 35 with SFTS and 49 with other endemic zoonoses were enrolled. In multivariate logistic regression analysis, independent predictors of SFTS included neurologic symptoms (odds ratio [OR], 12.915; 95% confidence interval [CI], 2.173–76.747), diarrhea (OR, 10.306; 95% CI, 1.588–66.895), leukopenia (< 4,000/mm3) (OR, 19.400; 95% CI, 3.290–114.408), and normal C-reactive protein (< 0.5 mg/dL) (OR, 24.739; 95% CI, 1.812–337.742). We set up a prediction score by assigning one point to each of these four predictors. A score of ≥ 2 had 82.9% sensitivity (95% CI, 71.7%–87.5%) and 95.9% specificity (95% CI, 88.0%–99.2%). The area under the curve of the clinical prediction score was 0.950 (95% CI, 0.903–0.997). CONCLUSION This study finding suggests a simple and useful scoring system to predict SFTS in patients with endemic zoonoses. We expect this strategic approach to facilitate early differentiation of SFTS from other endemic zoonoses, especially by primary care providers, and to improve the clinical outcomes.

In recent years, artificial intelligence (AI) technologies have greatly advanced and become a reality in many areas of our daily lives. In the health care field, numerous efforts are being made to implement the AI technology for practical medical treatments. With the rapid developments in machine learning algorithms and improvements in hardware performances, the AI technology is expected to play an important role in effectively analyzing and utilizing extensive amounts of health and medical data. However, the AI technology has various unique characteristics that are different from the existing health care technologies. Subsequently, there are a number of areas that need to be supplemented within the current health care system for the AI to be utilized more effectively and frequently in health care. In addition, the number of medical practitioners and public that accept AI in the health care is still low;moreover, there are various concerns regarding the safety and reliability of AI technologyimplementations. Therefore, this paper aims to introduce the current research and application status of AI technology in health care and discuss the issues that need to be resolved.