Background: Antimicrobial prescriptions for serious chronic or acute illness nearing its end stages raise concerns about the potential for futile use, adverse events, increased multidrugresistant organisms, and significant patient and social cost burdens. This study investigated the nationwide situation of how antibiotics are prescribed to patients during the last 14 days of life to guide future actions. Methods: This nationwide multicenter retrospective cohort study was conducted at 13 hospitals in South Korea from November 1 to December 31, 2018. All decedents were included in the study. Antibiotic use during the last two weeks of their lives was investigated. Results: A total of 1,201 (88.9%) patients received a median of two antimicrobial agents during the last two weeks of their lives. Carbapenems were prescribed to approximately half of the patients (44.4%) in the highest amount (301.2 days of therapy per 1,000 patient-days).Among the patients receiving antimicrobial agents, 63.6% were inappropriate and only 327 patients (27.2%) were referred by infectious disease specialists. The use of carbapenem (odds ratio [OR], 1.51; 95% confidence interval [CI], 1.13–2.03; P = 0.006), underlying cancer (OR, 1.56; 95% CI, 1.20–2.01, P = 0.047), underlying cerebrovascular disease (OR, 1.88; 95% CI, 1.23–2.89, P = 0.004), and no microbiological testing (OR, 1.79; 95% CI, 1.15–2.73; P = 0.010) were independent predictors for inappropriate antibiotic prescribing. Conclusion A considerable number of antimicrobial agents are administered to patients with chronic or acute illnesses nearing their end-of-life, a high proportion of which are prescribed inappropriately. Consultation with an infectious disease specialist, in addition to an antimicrobial stewardship program, may be necessary to induce the optimal use of antibiotics.

Background: A pooling test is a useful tool for mass screening of coronavirus disease 2019 (COVID-19) in the pandemic era. We aimed to optimize a simple two-step pooling test by estimating the optimal pool size using experimental and mathematical validation. Materials and Methods: Experimental pools were created by mixing one positive respiratory sample with various numbers of negative samples. We selected positive samples with cycle threshold (Ct) values greater than 32 to validate the efficiency of the pooling test assuming a high likelihood of false-negative results due to low viral loads. The positivities of the experimental pools were investigated with a single reverse-transcription polymerase chain reaction (RT-PCR) using the U-TOP™ COVID-19 Detection Kit Plus (Seasun Biomaterials, Daejeon, Korea). We used the Dorfman equation to calculate the optimal size of a pooling test mathematically. Results: Viral RNA could be detected in a pool with a size up to 11, even if the Ct value of a positive sample was about 35. The Dorfman equation showed that the optimal number of samples in a pool was 11 when the prevalence was assumed to be 0.66% based on the test positivity in Daejeon, Korea from April 1, 2020 to November 10, 2020. The efficiency of the pooling test was 6.2, which can save 83.9 of 100 individual tests. Conclusion Eleven samples in a pool were validated optimal experimentally assuming a prevalence of 0.66%. The pool size needs modification as the pandemic progresses; thus, the prevalence should be carefully estimated before pooling tests are conducted.

Background: A pooling test is a useful tool for mass screening of coronavirus disease 2019 (COVID-19) in the pandemic era. We aimed to optimize a simple two-step pooling test by estimating the optimal pool size using experimental and mathematical validation. Materials and Methods: Experimental pools were created by mixing one positive respiratory sample with various numbers of negative samples. We selected positive samples with cycle threshold (Ct) values greater than 32 to validate the efficiency of the pooling test assuming a high likelihood of false-negative results due to low viral loads. The positivities of the experimental pools were investigated with a single reverse-transcription polymerase chain reaction (RT-PCR) using the U-TOP™ COVID-19 Detection Kit Plus (Seasun Biomaterials, Daejeon, Korea). We used the Dorfman equation to calculate the optimal size of a pooling test mathematically. Results: Viral RNA could be detected in a pool with a size up to 11, even if the Ct value of a positive sample was about 35. The Dorfman equation showed that the optimal number of samples in a pool was 11 when the prevalence was assumed to be 0.66% based on the test positivity in Daejeon, Korea from April 1, 2020 to November 10, 2020. The efficiency of the pooling test was 6.2, which can save 83.9 of 100 individual tests. Conclusion Eleven samples in a pool were validated optimal experimentally assuming a prevalence of 0.66%. The pool size needs modification as the pandemic progresses; thus, the prevalence should be carefully estimated before pooling tests are conducted.

As the outbreak of coronavirus disease 2019 continues and the number of confirmed cases requiring isolation increases, there is a need for a safe and efficient system to assess patients' condition. We developed and evaluated a self-assessment questionnaire consisting of 23 symptoms with linear-scale scores from 0 to 10. Patients were asked to indicate their worst score for each symptom daily, and medical personnel assessed clinical improvement or deterioration based on the changes in scores. Focused communication on severity of specific symptoms was the primary advantage for the clinicians, and a thorough check for their symptoms was helpful for patients.

Background: Staphylococcus aureus bacteremia (SAB) is a common and serious infection with a high mortality. Patients with chronic kidney disease (CKD) are vulnerable to SAB, but there have been few studies performed on the clinical characteristics and outcomes of SAB in CKD patients stratified by dialysis. We aimed to estimate the all-cause mortality and identify its predictors in patients with CKD. Materials and Methods: We conducted a retrospective study on the patients with SAB hospitalized in a tertiary care center in Korea between March 2014 and December 2018.Kaplan-Meier analysis was performed to compare all-cause mortality following SAB among patients with non-dialysis dependent CKD (ND-CKD), those receiving dialysis, and those without CKD (non-CKD). The predictors of mortality among CKD patients were analyzed by Cox proportional hazards regression. Results: As a total, 278 SAB of 43 ND-CKD (31 males), 58 dialysis (39 males), and 177 nonCKD (112 males) patients were included. The 30-day mortality was 39.5% in ND-CKD, 27.6% in dialysis, and 7.9% in non-CKD patients. The hazard ratio of all-cause mortality following SAB in ND-CKD was 2.335 (95% confidence interval, 1.203 – 4.531; P = 0.003), compared to non-CKD patients. For methicillin-resistant S. aureus bacteremia (MRSAB), the hazard ratio of all-cause mortality in ND-CKD was 2.628 (95% CI, 1.074 – 6.435; P = 0.011), compared to dialysis patients. Appropriate antibiotics <48 h was independently related to improved survival following SAB among ND-CKD (adjusted HR, 0.304; 95% CI, 0,108 – 0.857; P = 0.024) and dialysis (adjusted HR, 0.323; 95% CI, 0,116 – 0.897; P = 0.030) patients. Conclusion ND-CKD patients demonstrated poor outcome following SAB and administration of appropriate antibiotics within 48 h could reduce the risk for mortality.

Background: Coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. This disease, which is quickly spreading worldwide, has high potential for infection and causes rapid progression of lung lesions, resulting in a high mortality rate. This study aimed to investigate the effects of SARS-CoV-2 infection on renal function in patients with COVID-19. Methods: From February 21 to April 24, 2020, 66 patients diagnosed with COVID-19 at Chungnam National University Hospital were analyzed; all patients underwent routine urinalysis and were tested for serum creatinine, urine protein to creatinine ratio (PCR), and urine albumin to creatinine ratio (ACR). Results: Acute kidney injury (AKI) occurred in 3 (4.5%) of the 66 patients, and 1 patient with AKI stage 3 underwent hemodialysis. Upon follow-up, all 3 patients recovered normal renal function. Compared with patients with mild COVID-19, AKI (n = 3) occurred in patients with severe COVID-19, of whom both urine PCR and ACR were markedly increased. Conclusion The incidence of AKI was not high in COVID-19 patients. The lower mortality rate in SARS-CoV-2 infection compared with previous Middle East respiratory syndrome and SARS-CoV infections is thought to be associated with a low incidence of dysfunction in organs other than the lungs.

Background: Observational studies of the ongoing coronavirus disease 2019 (COVID-19) outbreak suggest that a ‘cytokine storm’ is involved in the pathogenesis of severe illness.However, the molecular mechanisms underlying the altered pathological inflammation in COVID-19 are largely unknown. We report here that toll-like receptor (TLR) 4-mediated inflammatory signaling molecules are upregulated in peripheral blood mononuclear cells (PBMCs) from COVID-19 patients, compared with healthy controls (HC). Methods: A total of 48 subjects including 28 COVID-19 patients (8 severe/critical vs. 20 mild/ moderate cases) admitted to Chungnam National University Hospital, and age/sex-matched 20 HC were enrolled in this study. PBMCs from the subjects were processed for nCounter Human Immunology gene expression assay to analyze the immune related transcriptome profiles. Recombinant proteins of severe acute respiratory syndrome coronavirus-2 (SARSCoV-2) were used to stimulate the PBMCs and monocyte-derived macrophages, and real-time polymerase chain reaction was performed to quantify the mRNA expressions of the proinflammatory cytokines/chemokines. Results: Among the most highly increased inflammatory mediators in severe/critically ill patients, S100A9, an alarmin and TLR4 ligand, was found as a noteworthy biomarker, because it inversely correlated with the serum albumin levels. We also observed that recombinant S2 and nucleocapsid proteins of SARS-CoV2 significantly increased proinflammatory cytokines/chemokines and S100A9 in human primary PBMCs. Conclusion These data support a link between TLR4 signaling and pathological inflammation during COVID-19 and contribute to develop therapeutic approaches through targeting TLR4-mediated inflammation.

BACKGROUND: In patients with HIV, CD4+ T cell count and viral load are the main laboratory tests performed to assess clinical management. However, they require extensive resources. In this study, we aimed to determine whether hematological parameters measured using a hematology analyzer are useful as surrogate markers of CD4+ T cell count and viral load in HIV-infected patients. METHODS: Peripheral blood samples were obtained from 14 HIV-naïve, 105 HIV-treated, and 103 uninfected individuals. Hematological parameters were measured using the ADVIA 2120i hematology analyzer (Siemens Healthcare Diagnostics, USA). RESULTS: In HIV-naïve and -treated patients, the percentage of large unstained cells (%LUCs) was 2.5±1.6% and 1.9±0.7%, respectively, compared to 1.6±0.5% in HIV-uninfected controls. The %LUCs was higher in HIV patients with low CD4⁺ T cell count below 200/μL (2.4±1.0%) or high viral load ≥200 copies/mL (2.4±0.8%) than in other infected groups. Significant differences in lymphocyte count were observed between the HIV-naïve (1.5±0.6×10⁹/L) and uninfected (2.0±0.6×10⁹/L) groups as well as between HIV patients with CD4⁺ T cells ≥500/μL (2.5±0.6×10⁹/L) and other infected groups. Neutrophil count varied between high viral load (3.0±1.4×10⁹/L) and low viral load (3.7±1.3×10⁹/L) groups. The CD4⁺ T cell count correlated with lymphocyte count (r=0.642, P<0.0001) and %LUCs (r=-0.287, P=0.002). CONCLUSIONS: %LUCs, lymphocyte count, and neutrophil count are probable surrogate markers of CD4⁺ T cells and viral load.
Biomarkers ; Cell Count ; Delivery of Health Care ; Disease Progression* ; Hematology ; HIV Infections* ; HIV* ; Humans ; Lymphocyte Count ; Neutrophils ; T-Lymphocytes ; Viral Load

Nontyphoidal Salmonella infection often results in acute gastroenteritis, but extraintestinal infection presenting as a subdural empyema is unusual. We report a case of a 67-year-old man diagnosed with a subdural empyema caused by Salmonella group B that developed after evacuation of a subdural hematoma. To our knowledge, this is the first such case report in Korea.
Aged ; Empyema, Subdural* ; Gastroenteritis ; Hematoma, Subdural* ; Humans ; Korea ; Salmonella Infections ; Salmonella*

PURPOSE: Deep vein thrombosis (DVT) is a risk factor of pulmonary thromboembolism (PTE), however it is not clear who should be evaluated for a PTE and a DVT at the same time. The purpose of this study is to determine the clinical characteristics of PTE in patients with DVT who visited the emergency department (ED). METHODS: This was a retrospective cohort study of ED patients who visited with DVT and were simultaneously evaluated for a PTE from January 2012 to December 2013. We compared clinical characteristics between non-PTE and PTE patients with confirmed DVT in the ED. RESULTS: Of these 166 patients, 96 patients (57.8%) were confirmed PTE by computed tomography. In multivariate analysis, patients with PTE had more systemic neoplasm (OR 2.03, 95% CI 1.04-3.93, p=0.037) and right heart strain pattern in electrocardiography (OR 5.29, 95% CI 1.71-16.36, p=0.004) than patients without PTE. Femoral DVT was more likely in the non-PTE group (87.1% vs. 65.6%, p=0.002) and popliteal DVT was more likely in the PTE group (62.9% vs. 80.2%, p=0.013). However the number of DVT sites including both femoral and popliteal vein was not statistically different. CONCLUSION: In patients with systemic neoplasm or right heart strain patterns in electrocardiography, simultaneous PTE evaluation may be required in patients with DVT.
Cohort Studies ; Electrocardiography ; Emergencies* ; Emergency Service, Hospital* ; Heart ; Humans ; Multidetector Computed Tomography ; Multivariate Analysis ; Popliteal Vein ; Pulmonary Embolism* ; Retrospective Studies ; Risk Factors ; Thromboembolism ; Venous Thrombosis*