Clinical performance of the Roche Cobas Liat SARS-CoV-2 & influenza A/B assay: A systematic review and meta-analysis.

Respiratory tract infections caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and influenza viruses are persistent and critical. The Cobas Liat SARS-CoV-2 & influenza A/B assay (Multiplex Liat), the FDA-authorized point-of-care reverse transcriptase polymerase chain reaction (RT-PCR) assay, has a turnaround time of 20 min and high accuracy. This study evaluates the pooled performance of this assay to provide practical information. This meta-analysis was registered in PROSPERO (registration number: CRD42023467579). A systematic literature search was conducted within PubMed, Ovid-EMBASE, and the Cochrane Library for articles evaluating the accuracy of the Multiplex Liat assay through September 2023. A random-effects model was used to calculate the pooled diagnostic values with real-time RT-PCR (rRT-PCR) as a reference test. A total of 4,705 samples from eight studies were included in the primary meta-analysis. The overall pooled sensitivity and specificity of Multiplex Liat were 100.0 % (95 % confidence interval [CI] = 96.7 %-100.0 %) and 99.7 % (95 % CI = 98.7 %-99.9 %), respectively. The presence of variants of concern or in-house rRT-PCR assays as reference standards did not significantly affect the pooled diagnostic performance of the Multiplex Liat. When 5,333 samples from nine studies were assessed for sensitivity, the pooled sensitivity was 100.0 % (95 % CI = 85.8 %-100.0 %) without a significant difference. This meta-analysis demonstrates the usefulness of Multiplex Liat for the detection of SARS-CoV-2 based on pooled diagnostic values. These practical findings may facilitate appropriate settings for the diagnosis and management of patients with respiratory tract infections.

Impact of Infection Prevention Programs on Catheter-Associated Urinary Tract Infections Analyzed in Multicenter Study.

BACKGROUND: Catheter-associated urinary tract infections (CAUTIs) account for a large proportion of healthcare-associated infections and have a significant impact on morbidity, length of hospital stay, and mortality. Adherence to the recommended infection prevention practices can effectively reduce the incidence of CAUTIs. This study aimed to assess the characteristics of CAUTIs and the efficacy of prevention programs across hospitals of various sizes. METHODS: Intervention programs, including training, surveillance, and monitoring, were implemented. Data on the microorganisms responsible for CAUTIs, urinary catheter utilization ratio, rate of CAUTIs per 1,000 device days, and factors associated with the use of indwelling catheters were collected from 2017 to 2019. The incidence of CAUTIs and associated data were compared between university hospitals and small- and medium-sized hospitals. RESULTS: Thirty-two hospitals participated in the study, including 21 university hospitals and 11 small- and medium-sized hospitals. The microorganisms responsible for CAUTIs and their resistance rates did not differ between the two groups. In the first quarter of 2018, the incidence rate was 2.05 infections/1,000 device-days in university hospitals and 1.44 infections/1,000 device-days in small- and medium-sized hospitals. After implementing interventions, the rate gradually decreased in the first quarter of 2019, with 1.18 infections/1,000 device-days in university hospitals and 0.79 infections/1,000 device-days in small- and medium-sized hospitals. However, by the end of the study, the infection rate increased to 1.74 infections/1,000 device-days in university hospitals and 1.80 infections/1,000 device-days in small- and medium-sized hospitals. CONCLUSION: We implemented interventions to prevent CAUTIs and evaluated their outcomes. The incidence of these infections decreased in the initial phases of the intervention when adequate support and personnel were present. The rate of these infections may be reduced by implementing active interventions such as consistent monitoring and adherence to guidelines for preventing infections.

Trends in the detection of viruses causing gastroenteritis over a 10-year period and impact of nonpharmaceutical interventions.

BACKGROUND: Viral gastroenteritis continues to be a leading cause of death in low-income countries. The impact of nonpharmaceutical interventions (NPIs) on the transmission of gastroenteritis-causing viruses during the COVID-19 pandemic is understudied. OBJECTIVES: To investigate the 10-year trends of enteric viruses and estimate the impact of implementing and mitigating NPIs. STUDY DESIGN: Data regarding norovirus, rotavirus, adenovirus, astrovirus, and sapovirus detection were collected from five Korean hospitals between January 2013 and April 2023. We compared positivity between the pre-pandemic, pandemic, and post-pandemic periods. The causal effects of implementing and mitigating NPIs were quantified using the Bayesian Structural Time Series (BSTS) model. RESULTS: Norovirus was most frequently detected (9.9 %), followed by rotavirus (6.7 %), adenovirus (3.3 %), astrovirus (1.4 %), and sapovirus (0.6 %). During the pandemic, the positivity of all five viruses decreased, ranging from -1.0 % to -8.1 %, with rotavirus showing the greatest decrease. In the post-pandemic period, positivity rebounded for all viruses except for rotavirus. The BSTS model revealed that NPI implementation negatively affected the detection of all five viruses, resulting in reductions ranging from -73.0 % to -91.0 % compared to the prediction, with rotavirus being the least affected. Conversely, NPI mitigation positively affected the detection of all viruses, ranging from 79.0 % to 200.0 %, except for rotavirus. CONCLUSIONS: Trends observed over 10 years show that NPIs have had a major impact on changes in enteric virus detection. The effect of vaccines, in addition to NPIs, on rotavirus detection requires further investigation. Our findings emphasize the importance of NPIs in infection control and prevention.

Performance Evaluation of the Roche Cobas 5800 HBV and HCV Tests: Comparison of the 200 and 500 µL Protocols.

Background: Clinical management of patients infected with hepatitis B virus (HBV) or hepatitis C virus (HCV) relies on the viral load (VL). The Cobas 5800 system (Roche Diagnostics) can determine VLs in 200 and 500 µL samples, but the performance of each protocol has not been compared. We evaluated the performance of both protocols for the HBV and HCV tests. Methods: Precision and linearity were verified using commercial panels. Probit analyses were used to determine limits of detection (LoDs). The results obtained with 336 samples were compared using the 200 and 500 µL protocols. Data from 6,737 retrospective HBV and 768 HCV samples were compared to estimate the effects of the different LoDs on the diagnostic results of the protocols. Correlations between protocols were tested with Spearman's rank correlation coefficients (rho). Results: The precision and linearity of both protocols were verified. The LoDs for the 200 and 500 µL protocols were 6.5 and 2.7 IU/mL for HBV and 29.7 and 8.2 IU/mL for HCV, respectively. The agreement between the protocols ranged from 0.8 to 1.0. The results obtained with the HBV and HCV tests showed a strong correlation (rho=0.994). Only 0.4% of HBV and 0.4% of HCV test results were affected by the LoDs of the 200 µL protocol. Conclusions: The Cobas 5800 200 and 500 µL protocols for the HBV DNA and HCV RNA tests demonstrated excellent performance. These findings establish the 200 µL protocol as a new option for low-volume samples, especially for pediatric and difficult-to-bleed patients.

Variant Analysis of the Thymidine Kinase and DNA Polymerase Genes of Herpes Simplex Virus in Korea: Frequency of Acyclovir Resistance Mutations.

The thymidine kinase (TK) and DNA polymerase (pol) genes of the herpes simplex viruses type 1 (HSV-1) and type 2 (HSV-2) are two important genes involved in antiviral resistance. We investigated the genetic polymorphisms of the HSV-TK and pol genes in clinical isolates from Korean HSV-infected patients using next-generation sequencing (NGS) for the first time in Korea. A total of 81 HSV-1 and 47 HSV-2 isolates were examined. NGS was used to amplify and sequence the TK and pol genes. Among the 81 HSV-1 isolates, 12 and 17 natural polymorphisms and 9 and 23 polymorphisms of unknown significance in TK and pol were found, respectively. Two HSV-1 isolates (2.5%) exhibited the E257K amino acid substitution in TK, associated with antiviral resistance. Out of 47 HSV-2 isolates, 8 natural polymorphisms were identified in TK, and 9 in pol, with 13 polymorphisms of unknown significance in TK and 10 in pol. No known resistance-related mutations were observed in HSV-2. These findings contribute to our understanding of the genetic variants associated with antiviral resistance in HSV-1 and HSV-2 in Korea, with frequencies of known antiviral resistance-related mutations of 2.5% and 0% in HSV-1 and HSV-2, respectively.

Immune-checkpoint proteins, cytokines, and microbiome impact on patients with cervical insufficiency and preterm birth.

Background: Microenvironmental factors, including microbe-induced inflammation and immune-checkpoint proteins that modulate immune cells have been associated with both cervical insufficiency and preterm delivery. These factors are incompletely understood. This study aimed to explore and compare interactions among microbiome and inflammatory factors, such as cytokines and immune-checkpoint proteins, in patients with cervical insufficiency and preterm birth. In particular, factors related to predicting preterm birth were identified and the performance of the combination of these factors was evaluated. Methods: A total of 220 swab samples from 110 pregnant women, prospectively recruited at the High-Risk Maternal Neonatal Intensive Care Center, were collected between February 2020 and March 2021. This study included 63 patients with cervical insufficiency receiving cerclage and 47 control participants. Endo- and exocervical swabs and fluids were collected simultaneously. Shotgun metagenomic sequencing for the microbiome and the measurement of 34 immune-checkpoint proteins and inflammatory cytokines were performed. Results: First, we demonstrated that immune-checkpoint proteins, the key immune-regulatory molecules, could be measured in endocervical and exocervical samples. Secondly, we identified significantly different microenvironments in cervical insufficiency and preterm birth, with precise cervical locations, to provide information about practically useful cervical locations in clinical settings. Finally, the presence of Moraxella osloensis (odds ratio = 14.785; P = 0.037) and chemokine CC motif ligand 2 levels higher than 73 pg/mL (odds ratio = 40.049; P = 0.005) in endocervical samples were associated with preterm birth. Combining M. osloensis and chemokine CC motif ligand 2 yielded excellent performance for predicting preterm birth (area under the receiver operating characteristic curve = 0.846, 95% confidence interval = 0.733-0.925). Conclusion: Multiple relationships between microbiomes, immune-checkpoint proteins, and inflammatory cytokines in the cervical microenvironment were identified. We focus on these factors to aid in the comprehensive understanding and therapeutic modulation of local microbial and immunologic compositions for the management of cervical insufficiency and preterm birth.

Evaluation of the BD Phoenix CPO Detect Panel for Detection and Classification of Carbapenemase Producing Enterobacterales.

Carbapenem-resistant Enterobacterales (CRE) pose a serious public health threat due to their resistance to most antibiotics. Rapid and correct detection of carbapenemase producing organisms (CPOs) can help inform clinician decision making on antibiotic therapy. The BD Phoenix™ CPO detect panel, as part of antimicrobial susceptibility testing (AST), detects carbapenemase activity (P/N) and categorizes CPOs according to Ambler classes. We evaluated a CPO detect panel against 109 carbapenemase producing Enterobacterales (CPE) clinical isolates from Korea. The panel correctly detected carbapenemases production in 98.2% (n = 107/109) isolates and identified 78.8% (n = 26/33) class A, 65.9% (n = 29/44) class B, and 56.3% (n = 18/32) class D carbapenemase producers as harboring their corresponding Ambler classes. Specifically, the panel correctly classified 81.3% (n = 13/16) of K. pneumoniae KPC isolates to class A. However, the panel failed to classify 40.0% (n = 4/10) IMP and 63.6% (n = 7/11) VIM isolates to class B. Despite 27.5% (n = 30/109) CPE not being assigned Ambler classes, all of them tested carbapenemase positive. Our results demonstrate that the CPO detect panel is a sensitive test for detecting CPE and classifying KPC as class A, helping with antibiotics selection, but one-third of CPE remained unclassified for Ambler classes.

Tracking the Genomic Evolution of SARS-CoV-2 for 29 Months in South Korea.

The pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has continued, with the persistent emergence of variants of concern (VOCs). Therefore, this study aimed to track the genomic evolution of SARS-CoV-2 strains by sequencing the spike protein for 29 months, which accounted for the majority of the COVID-19 pandemic period. A total of 109 swabs from patients with confirmed coronavirus disease 2019 (COVID-19) infection were randomly collected between March 2020 and July 2022. After genomic sequencing, we analyzed the naming systems and phylogenetic trees. Five surge peaks of COVID-19 cases have been reported in South Korea, resulting in 14,000,000 cumulative confirmed cases and 17,000 deaths. Among the sequenced samples, 34 wild-type strains and 75 VOCs, including 4 Alpha, 33 Delta, 2 Epsilon, and 36 Omicron VOCs, were identified. Omicron strains were comprised of 8 BA.1.1 (21 K), 27 BA.2 (21 L), and 1 BA.2.12.1 (22C). Phylogenetic analysis of the identified isolates and representative sequences of SARS-CoV-2 strains revealed clusters that presented the WHO VOCs. Specific or unique mutations for each VOC waxed and waned according to the variant waves. Our findings allowed recognition of the overall trends of SARS-CoV-2 isolates, which implicated replication advantage, immune evasion, and disease management.

Investigation of SARS-CoV-2 lineages and mutations circulating in a university-affiliated hospital in South Korea analyzed using Oxford Nanopore MinION sequencing.

OBJECTIVES: Despite the introduction of vaccines, treatments, and massive diagnostic testing, the evolution of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has continued to overcome barriers that had slowed its previous spread. As the virus evolves towards increasing fitness, it is critical to continue monitoring the occurrence of new mutations that could evade human efforts to control them. METHODS: We performed whole-genome sequencing using Oxford Nanopore MinION sequencing on 58 SARS-CoV-2 isolates collected during the ongoing coronavirus disease 2019 pandemic at a tertiary hospital in South Korea and tracked the emergence of mutations responsible for massive spikes in South Korea. RESULTS: The differences among lineages were more pronounced in the spike gene, especially in the receptor-binding domain (RBD), than in other genes. Those RBD mutations could compromise neutralization by antibodies elicited by vaccination or previous infections. We also reported multiple incidences of Omicron variants carrying mutations that could impair the diagnostic sensitivity of reverse transcription-polymerase chain reaction-based testing. CONCLUSION: These results provide an understanding of the temporal changes of variants and mutations that have been circulating in South Korea and their potential impacts on antigenicity, therapeutics, and diagnostic escape of the virus. We also showed that the utilization of the nanopore sequencing platform and the ARTIC workf low can provide convenient and accurate SARS-CoV-2 genomic surveillance even at a single hospital.

Deep learning application of the discrimination of bone marrow aspiration cells in patients with myelodysplastic syndromes.

Myelodysplastic syndromes (MDS) are a group of hematologic neoplasms accompanied by dysplasia of the bone marrow hematopoietic cells with cytopenia. Detecting dysplasia is important in the diagnosis of MDS, but it takes considerable time and effort. Also, since the assessment of dysplasia is subjective and difficult to quantify, a more efficient tool is needed for quality control and standardization of bone marrow aspiration smear interpretation. In this study, we developed and evaluated an algorithm to automatically discriminate hematopoietic cell lineages and detect dysplastic cells in bone marrow aspiration smears using deep learning technology. Bone marrow aspiration images were acquired from 34 patients diagnosed with MDS and from 24 normal bone marrow slides. In total, 8065 cells were classified into eight categories: normal erythrocytes, normal granulocytes, normal megakaryocytes, dysplastic erythrocytes, dysplastic granulocytes, dysplastic megakaryocytes, blasts, and others. The algorithm demonstrated acceptable performance in classifying dysplastic cells, with an AUC of 0.945-0.996 and accuracy of 0.912-0.993. The algorithm developed in this study could be used as an auxiliary tool for diagnosing patients with MDS and is expected to contribute to shortening the time required for MDS bone marrow aspiration diagnosis and standardizing visual reading.

Prognostic impacts of soluble immune checkpoint regulators and cytokines in patients with SARS-CoV-2 infection.

Coronavirus disease 2019 (COVID-19) has been a pandemic for the past two years. Predicting patient prognosis is critical. Although immune checkpoints (ICs) were shown to be involved in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, quantitative studies of ICs in clinical practice are limited. In this study, various soluble ICs (sICs) and cytokine levels in patients with SARS-CoV-2 infection at different time points were compared between survivors and deaths; we also examined whether sICs are useful for predicting prognosis. sICs and cytokines were measured in serum samples from 38 patients diagnosed with COVID-19 in the first and second week post-diagnosis. All assays were performed by bead-based multiplexed immunoassay system using Luminex Bio-Plex 200 system. The correlation of sICs and cytokines with laboratory markers was evaluated, and the levels of sICs in survivors were compared with those in deaths. Among the sICs, the second-week levels of soluble cluster of differentiation (sCD27, p = 0.012), sCD40 (p< 0.001), cytotoxic T-lymphocyte-associated protein 4 (sCTLA-4, p< 0.001), herpes virus entry mediator (sHVEM, p = 0.026), and T-cell immunoglobulin and mucin-domain containing-3 (sTIM-3, p = 0.002) were significantly higher in deaths than in survivors. The levels of nine cytokines assessed in the second week of deaths were significantly higher than those in survivors. The sICs sCD27, sCD40, sCTLA-4, and sTIM-3 and cytokines chemokine CC motif ligand 2 (CCL2), GM-CSF, IL-10, and IL-8 showed significant positive correlations with the levels of C-reactive protein (CRP) and procalcitonin and were negatively correlated with the absolute lymphocyte count and platelet values. Increased levels of sICs including sCD27, sCD40, sCTLA-4, and sTIM-3 and cytokines were significant factors for poor prognosis. sICs, together with cytokines and inflammatory markers, may be useful as prognostic stratification markers in SARS-CoV-2-infected patients.

Development and validation of a deep learning-based protein electrophoresis classification algorithm.

BACKGROUND: Protein electrophoresis (PEP) is an important tool in supporting the analytical characterization of protein status in diseases related to monoclonal components, inflammation, and antibody deficiency. Here, we developed a deep learning-based PEP classification algorithm to supplement the labor-intensive PEP interpretation and enhance inter-observer reliability. METHODS: A total of 2,578 gel images and densitogram PEP images from January 2018 to July 2019 were split into training (80%), validation (10%), and test (10.0%) sets. The PEP images were assessed based on six major findings (acute-phase protein, monoclonal gammopathy, polyclonal gammopathy, hypoproteinemia, nephrotic syndrome, and normal). The images underwent processing, including color-to-grayscale and histogram equalization, and were input into neural networks. RESULTS: Using densitogram PEP images, the area under the receiver operating characteristic curve (AUROC) for each diagnosis ranged from 0.873 to 0.989, and the accuracy for classifying all the findings ranged from 85.2% to 96.9%. For gel images, the AUROC ranged from 0.763 to 0.965, and the accuracy ranged from 82.0% to 94.5%. CONCLUSIONS: The deep learning algorithm demonstrated good performance in classifying PEP images. It is expected to be useful as an auxiliary tool for screening the results and helpful in environments where specialists are scarce.

STANDARD M10 SARS-CoV-2 Assay for Rapid Detection of SARS-CoV-2: Comparison of Four Real-Time PCR Assays.

The demand for assays that can rapidly and accurately detect severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) remains high. We evaluated the performance of two rapid real-time reverse transcription polymerase chain reaction (RT-qPCR) assays (STANDARD M10 SARS-CoV-2 and Xpert Xpress SARS-CoV-2) against conventional RT-qPCR assays (STANDARD M nCoV and Allplex SARS-CoV-2) for detecting SARS-CoV-2. A total of 225 swab samples were collected and tested using the four assays. The STANDARD M10 SARS-CoV-2 assay showed 97.4% positive percent agreement (PPA) and 100.0% negative percent agreement (NPA) compared to the STANDARD M nCoV assay and Allplex SARS-CoV-2 assay. STANDARD M10 exhibited high performance except in samples with low viral loads (cycle threshold (Ct) > 30). Xpert Xpress showed PPA and NPA of 100.0% compared to the two conventional RT-qPCR assays. The kappa coefficient (Κ) showed nearly almost perfect agreement between each assay and conventional RT-qPCR assays. The correlations of Ct values between the two rapid RT-qPCR and conventional RT-qPCR assays were >0.8, indicating strong correlations. All included assays could detect SARS-CoV-2 variants, such as the Alpha, Beta, and Gamma variants. The recently developed STANDARD M10 has a shorter turnaround time and random-access detection on automated devices, thereby facilitating efficient testing in emergency settings.

Quantitative Analysis of Anti-N and Anti-S Antibody Titers of SARS-CoV-2 Infection after the Third Dose of COVID-19 Vaccination.

We quantitatively analyzed SARS-CoV-2 antibody levels in patients after two doses of the ChAdOx1 nCoV-19 vaccine and the third BNT162b2 booster. We obtained 255 serum samples from 149 healthcare workers 1 and 4 months after the third dose. Of the 149 participants, 58 (38.9%) experienced COVID-19 infection during the 4-month study period, with infection occurring 7−62 days before the second blood draw. Total antibody titers against the anti-spike (anti-S) and anti-nucleocapsid (anti-N) proteins of SARS-CoV-2 were measured using Elecsys Anti-SARS-CoV-2 S and Elecsys Anti-SARS-CoV-2 assays (Roche), respectively. The median anti-S antibody titer in the non-infected groups at 4 months after the third dose was significantly decreased compared to that at 1 month after the third dose (from 17,777 to 3673 U/mL, p < 0.001). The infected group showed higher median anti-S antibody titers at 4 months (19,539 U/mL) than the non-infected group (3673 U/mL). The median anti-N antibody titer in the infected group at 4 months after the third dose was a 5.07 cut-off index (79.3% positivity). Anti-N antibody titers in the infected group were correlated with the number of days after SARS-CoV-2 infection. These data provide useful information for determining quarantine strategies and fourth vaccination requirements.

Humoral and Cellular Responses to BNT162b2 as a Booster Following Two Doses of ChAdOx1 nCov-19 Determined Using Three SARS-CoV-2 Antibody Assays and an Interferon-Gamma Release Assay: A Prospective Longitudinal Study in Healthcare Workers.

Data on humoral and cellular responses to BNT162b2 as a booster dose, following two doses of ChAdOx1 nCov-19 vaccine, have seldom been reported. The aim of this study was to assess the positivity rates of three representative antibody assays targeting total, IgG, and neutralizing antibodies, and an interferon-γ release assay (IGRA), and to determine the longitudinal changes in quantitative antibody titers after each vaccination. A total of 1027 samples were collected from healthcare workers. The number of participants after the booster dose was 153, and they all completed a questionnaire on adverse reactions. All antibody assays showed 100.0% positivity at 1 month after booster vaccination. The median antibody titers of the assays were significantly increased compared with those after the second dose (22.1-fold increase for Roche total antibody, 14.0-fold increase for Abbott IgG, and 1.1-fold increase (97.5% inhibition) for GenScript neutralizing antibody). Cellular responses determined using the IGRA were positive in 92.8% of the participants. Most participants (72.5%) reported mild adverse reactions. Correlations between the three antibody assays and IGRA were weak or negligible, indicating a difference between humoral and cellular responses. Overall, our study provides information about booster vaccine strategies and laboratory settings, which could subsequently contribute to the control of the spread of coronavirus disease 2019.

Impact of COVID-19 on Antimicrobial Consumption and Spread of Multidrug-Resistance in Bacterial Infections.

The spread of COVID-19 pandemic may have affected antibiotic consumption patterns and the prevalence of colonized or infected by multidrug-resistant (MDR) bacteria. We investigated the differences in the consumption of antibiotics easily prone to resistance and the prevalence of MDR bacteria during the COVID-19 pandemic (March 2020 to September 2021) compared to in the pre-pandemic period (March 2018 to September 2019). Data on usage of antibiotics and infections caused by methicillin-resistant Staphylococcus aureus (MRSA), vancomycin-resistant Enterococcus (VRE), carbapenem-resistant Enterobacteriaceae (CRE), carbapenem-resistant Acinetobacter baumannii (CRAB), and carbapenem-resistant Pseudomonas aeruginosa (CRPA) were obtained from hospitalized patients in four university hospitals. The consumption of penicillin with ß-lactamase inhibitors (3.4% in ward, 5.8% in intensive care unit (ICU)), and carbapenems (25.9% in ward, 12.1% in ICU) increased during the pandemic period. The prevalence of MRSA (4.7%), VRE (49.0%), CRE (22.4%), and CRPA (20.1%) isolated in clinical samples from the ward and VRE (26.7%) and CRE (36.4%) isolated in clinical samples from the ICU were significantly increased, respectively. Meanwhile, only the prevalence of CRE (38.7%) isolated in surveillance samples from the ward increased. The COVID-19 pandemic is associated with increased consumption of antibiotics and has influenced the prevalence of infections caused by MDR isolates.

Prevalence and Clinical Impact of Coinfection in Patients with Coronavirus Disease 2019 in Korea.

Coinfection rates with other pathogens in coronavirus disease 2019 (COVID-19) varied during the pandemic. We assessed the latest prevalence of coinfection with viruses, bacteria, and fungi in COVID-19 patients for more than one year and its impact on mortality. A total of 436 samples were collected between August 2020 and October 2021. Multiplex real-time PCR, culture, and antimicrobial susceptibility testing were performed to detect pathogens. The coinfection rate of respiratory viruses in COVID-19 patients was 1.4%. Meanwhile, the rates of bacteria and fungi were 52.6% and 10.5% in hospitalized COVID-19 patients, respectively. Respiratory syncytial virus, rhinovirus, Acinetobacter baumannii, Escherichia coli, Pseudomonas aeruginosa, and Candida albicans were the most commonly detected pathogens. Ninety percent of isolated A. baumannii was non-susceptible to carbapenem. Based on a multivariate analysis, coinfection (odds ratio [OR] = 6.095), older age (OR = 1.089), and elevated lactate dehydrogenase (OR = 1.006) were risk factors for mortality as a critical outcome. In particular, coinfection with bacteria (OR = 11.250), resistant pathogens (OR = 11.667), and infection with multiple pathogens (OR = 10.667) were significantly related to death. Screening and monitoring of coinfection in COVID-19 patients, especially for hospitalized patients during the pandemic, are beneficial for better management and survival.

Genotypic Distribution and Antimicrobial Susceptibilities of Carbapenemase-Producing Enterobacteriaceae Isolated From Rectal and Clinical Samples in Korean University Hospitals Between 2016 and 2019.

BACKGROUND: The emergence of carbapenemase-producing Enterobacteriaceae (CPE) represents a major clinical problem. Recently, the occurrence of CPE has increased globally, but epidemiological patterns vary across region. We report the trends in the genotypic distribution and antimicrobial susceptibility of CPE isolated from rectal and clinical samples during a four-year period. METHODS: Between January 2016 and December 2019, 1,254 nonduplicated CPE isolates were obtained from four university hospitals in Korea. Carbapenemase genotypes were determined by multiplex real-time PCR. Antimicrobial susceptibility was profiled using the Vitek 2 system (bioMérieux, Hazelwood, MO, USA) or MicroScan Walkaway-96 system (Siemens West Sacramento, CA, USA). The proportions of carbapenemase genotypes and nonsusceptibility were analyzed using Pearson's chi-square test. RESULTS: Among the 1,254 CPE isolates, 486 (38.8%), 371 (29.6%), 357 (28.5%), 8 (0.6%), 8 (0.6%), and 24 (1.9%) were Klebsiella pneumoniae carbapenemase (KPC), oxacillinase (OXA)-48-like, New Delhi metallo-ß-lactamase (NDM), imipenemase (IMP), Verona integron-encoded metallo-ß-lactamase (VIM), and multiple producers, respectively. The predominant species was K. pneumoniae (72.6%), followed by Escherichia coli (6.5%). More than 90% of the isolates harboring KPC, NDM, and OXA-48-like were nonsusceptible to cephalosporins, aztreonam, and carbapenems. CONCLUSIONS: The impact of CPE is primarily due to KPC-, NDM-, and OXA-48-like-producing K. pneumoniae isolates. Isolates carrying these carbapenemase are mostly multidrug-resistant. Control strategies based on these genotypic distributions and antimicrobial susceptibilities of CPE isolates are required.

Immature granulocyte percentage for prediction of sepsis in severe burn patients: a machine leaning-based approach.

BACKGROUND: Of the existing sepsis markers, immature granulocytes (IG) most frequently reflect the presence of an infection. The importance of IG as an early predictor of sepsis and bacteremia is evaluated differently for each study. This study aimed to evaluate the effectiveness of the Sysmex XN series' IG% as an independent prognostic indicator of sepsis using machine learning. METHODS: A total of 2465 IG% results from 117 severe burn patients in the intensive care unit of one institution were retrospectively analyzed. We evaluated the IG% for sepsis using the receiver operating characteristic, logistic regression, and partial dependence plot analyses. Clinical characteristics and other laboratory markers associated with sepsis, including WBC, procalcitonin, and C-reactive protein, were compared with the IG% values. RESULTS: Twenty-six of the 117 patients were diagnosed with sepsis. The median IG% value was 2.6% (95% CI: 1.4-3.1). The area under the receiver operating characteristic curve was 0.77 (95% CI: 0.78-0.84) and the optimal cut-off value was 3%, with a sensitivity of 76.9% and specificity of 68.1%. The partial dependence plot of IG% on predicting sepsis showed that an IG% < 4% had low predictability, but increased thereafter. The interaction plot of IG% and C-reactive protein showed an increase in sepsis probability at an IG% of 6% and C-reactive protein of 160 mg/L. CONCLUSIONS: IG% is moderately useful for predicting sepsis. However, since it can be determined from routine laboratory test results and requires no additional intervention or cost, it could be particularly useful as an auxiliary marker.

Evaluation of Combined Cancer Markers With Lactate Dehydrogenase and Application of Machine Learning Algorithms for Differentiating Benign Disease From Malignant Ovarian Cancer.

BACKGROUND: The differential diagnosis of ovarian cancer is important, and there has been ongoing research to identify biomarkers with higher performance. This study aimed to evaluate the diagnostic utility of combinations of cancer markers classified by machine learning algorithms in patients with early stage ovarian cancer, which has rarely been reported. METHODS: In total, 730 serum samples were assayed for lactate dehydrogenase (LD), neutrophil-to-lymphocyte ratio (NLR), human epididymis protein 4 (HE4), cancer antigen 125 (CA125), and risk of ovarian malignancy algorithm (ROMA). Among them, 53 were diagnosed with early stage ovarian cancer, and the remaining 677 were diagnosed with benign disease. RESULTS: The areas under the receiver operating characteristic curves (ROC-AUCs) of the ROMA, HE4, CA125, LD, and NLR for discriminating ovarian cancer from non-cancerous disease were .707, .680, .643, .657, and .624, respectively. ROC-AUC of the combination of ROMA and LD (.709) was similar to that of single ROMA in the total population. In the postmenopausal group, ROC-AUCs of HE4 and CA125 combined with LD presented the highest value (.718). When machine learning algorithms were applied to ROMA combined with LD, the ROC-AUC of random forest was higher than that of other applied algorithms in the total population (.757), showing acceptable performance. CONCLUSION: Our data suggest that the combinations of ovarian cancer-specific markers with LD classified by random forest may be a useful tool for predicting ovarian cancer, particularly in clinical settings, due to easy accessibility and cost-effectiveness. Application of an optimal combination of cancer markers and algorithms would facilitate appropriate management of ovarian cancer patients.