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Aims Left ventricular assist devices (LVADs) improve quality of life and survival in patients with advanced heart failure, but device-related infections (DRIs) remain cumbersome. We evaluated the diagnostic capability of [18F]FDG PET/CT, factors affecting its accuracy, and the additive value of semi-quantitative analysis for the diagnosis of DRI. Methods and results LVAD recipients undergoing [18F]FDG PET/CT between 2012 and 2020 for suspected DRI were retrospectively included. [18F]FDG PET/CT was performed and evaluated in accordance with EANM guidelines. The final diagnosis of DRI, based on multidisciplinary consensus and findings during surgery, whenever performed, was used as the reference for diagnosis. 41 patients were evaluated for 59 episodes of suspected DRI. The clinical evaluation established driveline infection in 32 (55%) episodes, central device infection in 6 (11%), and combined infection in 2 (4%). Visual analysis of [18F]FDG PET/CT achieved a sensitivity and specificity for driveline infections of 0.79 and 0.71, respectively, whereas semi-quantitative analysis achieved a sensitivity and specificity of 0.94 and 0.83, respectively. For central device component infection, visual analysis of [18F]FDG PET/CT achieved a sensitivity and specificity of 0.75 and 0.60, respectively. Semi-quantitative analysis using SUVratio achieved a sensitivity and specificity of 1.0 and 0.8, respectively. The increase of specificity for central component infection was statistically significant (P = 0.05). Conclusions [18F]FDG PET/CT reliably predicts the presence of DRI in LVAD recipients. Semi-quantitative analysis may increase the specificity of [18F]FDG PET/CT for the analysis of central device component infection and should be considered in equivocal cases after visual analysis.

Objectives Insights about local antimicrobial resistance (AMR) levels and epidemiology are essential to guide decision-making processes in antimicrobial use. However, dedicated tools for reliable and reproducible AMR data analysis and reporting are often lacking. We aimed to compare traditional data analysis and reporting versus a new approach for reliable and reproducible AMR data analysis in a clinical setting. Methods Ten professionals who routinely work with AMR data were provided with blood culture test results including antimicrobial susceptibility results. Participants were asked to perform a detailed AMR data analysis in a two-round process: first using their software of choice and next using our newly developed software tool. Accuracy of the results and time spent were compared between both rounds. Finally, participants rated the usability using the System Usability Scale (SUS). Results The mean time spent on creating the AMR report reduced from 93.7 to 22.4 min (P Conclusions This study demonstrated the significant improvement in efficiency and accuracy in standard AMR data analysis and reporting workflows through open-source software. Integrating these tools in clinical settings can democratize the access to fast and reliable insights about local microbial epidemiology and associated AMR levels. Thereby, our approach can support evidence-based decision-making processes in the use of antimicrobials.

Background Left ventricular assist devices (LVADs) are increasingly used for the treatment of advanced heart failure. LVADs improve quality of life and decrease mortality, but the driveline carries substantial risk for major infections. These device-related LVAD and driveline infections are difficult to diagnose with conventional imaging. We reviewed and analysed the current literature on the additive value of 18F-fluorodeoxyglucose positron emission tomography combined with computed tomography (FDG-PET/CT) imaging for the diagnosis of LVAD-related infections.” Materials/methods We performed a systematic literature review using several databases from their inception until the 31st of December, 2019. Studies investigating the diagnostic performance of FDG-PET/CT in patients with suspected LVAD infection were retrieved. After a bias risk assessment using QUADAS-2, a study-aggregate meta-analysis was performed on a per examination-based analysis. Results A total of 10 studies were included in the systematic review, eight of which were also eligible for study-aggregate meta-analysis. For the meta-analysis, a total of 256 FDG-PET/CT scans, examining pump/pocket and/or driveline infection, were acquired in 230 patients. Pooled sensitivity of FDG-PET/CT was 0.95 (95% confidence interval (CI) 0.89–0.97) and pooled specificity was 0.91 (95% CI 0.54–0.99) for the diagnosis of device-related infection. For pump/pocket infection, sensitivity and specificity of FDG-PET/CT were 0.97 (95%CI 0.69–1.00) and 0.93 (95%CI 0.64–0.99), respectively. For driveline infection, sensitivity and specificity were 0.96 (95%CI 0.88–0.99) and 0.99 (95%CI 0.13–1.00) respectively. Significant heterogeneity existed across studies for specificity, mostly caused by differences in scan procedures. Predefined criteria for suspicion of LVAD and/or driveline infection were lacking in all included studies. Conclusions FDG-PET/CT is a valuable tool for assessment of device-related infection in LVAD patients, with high sensitivity and high, albeit variable, specificity. Standardization of FDG-PET/CT procedures and criteria for suspected device-related LVAD infections are needed for consistent reporting of FDG-PET/CT scans.

Background Machine learning (ML) is increasingly being used in many areas of health care. Its use in infection management is catching up as identified in a recent review in this journal. We present here a complementary review to this work. Objectives To support clinicians and researchers in navigating through the methodological aspects of ML approaches in the field of infection management. Sources A Medline search was performed with the keywords artificial intelligence, machine learning, infection∗, and infectious disease∗ for the years 2014–2019. Studies using routinely available electronic hospital record data from an inpatient setting with a focus on bacterial and fungal infections were included. Content Fifty-two studies were included and divided into six groups based on their focus. These studies covered detection/prediction of sepsis (n = 19), hospital-acquired infections (n = 11), surgical site infections and other postoperative infections (n = 11), microbiological test results (n = 4), infections in general (n = 2), musculoskeletal infections (n = 2), and other topics (urinary tract infections, deep fungal infections, antimicrobial prescriptions; n = 1 each). In total, 35 different ML techniques were used. Logistic regression was applied in 18 studies followed by random forest, support vector machines, and artificial neural networks in 18, 12, and seven studies, respectively. Overall, the studies were very heterogeneous in their approach and their reporting. Detailed information on data handling and software code was often missing. Validation on new datasets and/or in other institutions was rarely done. Clinical studies on the impact of ML in infection management were lacking. Implications Promising approaches for ML use in infectious diseases were identified. But building trust in these new technologies will require improved reporting. Explainability and interpretability of the models used were rarely addressed and should be further explored. Independent model validation and clinical studies evaluating the added value of ML approaches are needed.

The existing literature has considered licensing of a patented cost-reducing innovation either in a homogeneous good industry or in a differentiated goods industry. We consider a new model of patent licensing between producers of a homogeneous good when there is competition from producers of another brand in the market. We find that for drastic innovation, the optimal intra-brand patent licensing involves only fixed fee when the degree of inter-brand differentiation is low and a two-part tariff otherwise. For non-drastic innovation, the optimal intra-brand patent licensing involves only royalty when the degree of inter-brand differentiation is high, a two-part tariff when it is intermediate, and finally a fixed fee alone when it is low. We also establish that the innovator has more incentive for innovation as an insider than an outsider of this market.

Whole-genome sequencing (WGS) of Mycobacterium tuberculosis (MTB) isolates can be used to get an accurate diagnosis, to guide clinical decision making, to control tuberculosis (TB) and for outbreak investigations. We evaluated the performance of long-read (LR) and/or short-read (SR) sequencing for anti-TB drug-resistance prediction using the TBProfiler and Mykrobe tools, the fraction of genome recovery, assembly accuracies and the robustness of two typing approaches based on core-genome SNP (cgSNP) typing and core-genome multi-locus sequence typing (cgMLST). Most of the discrepancies between phenotypic drug-susceptibility testing (DST) and drug-resistance prediction were observed for the first-line drugs rifampicin, isoniazid, pyrazinamide and ethambutol, mainly with LR sequence data. Resistance prediction to second-line drugs made by both TBProfiler and Mykrobe tools with SR- and LR-sequence data were in complete agreement with phenotypic DST except for one isolate. The SR assemblies were more accurate than the LR assemblies, having significantly (P

Introduction: Noninvasive imaging techniques play a central role in identification, stratification, and follow-up of infectious and inflammatory vascular disease. Manual or semi-automatic quantification of these diseases is time consuming. Besides that, most fully automated analysis software fails to integrate information obtained from hybrid nuclear medicine images like PET/CT. The aim of this study is to develop an accurate, fast, and fully automated algorithm to segment the aorta on low dose CT (LDCT) as a navigator for PET-assessed vascular disease. Methods: Eighteen non-contrast LDCT scans from [ 18 fluor]fluordeoxyglucose ([ 18 F]FDG) PET/CT scans were used. The aorta was manually delineated from aortic valve to iliac bifurcation and used as ground truth. Split, rotation, and noise addition augmentation was used on fifteen scans to create 100 3D patches, which were split into a training (n=80) and validation (n=20) set. Three scans were used as test set. Cropping, normalization, and zero-centering were used for image preprocessing to speed up and improve accuracy of the training. No down sampling was used. A U-Net architecture was modified to process 3D inputs and outputs. The Dice Similarity Metric (DSM) was used to assess algorithm performance. Results: Fully automated segmentation of the aorta was feasible training a modified 3D U-Net on non-contrast LDCT obtained from PET/CT scans. The test set yielded a DSM of 0.846 ± 0.020 (mean ± SD) for the entire aorta. The run time for the test set with the trained 3D U-Net was on average 2 seconds per patient, whereas the manual delineation was in the order of 30 minutes per patient. Conclusions: Comparisons of ground truth and automatic segmentations using the trained 3D U-Net demonstrated excellent concordance. This method can be used for faster and more in-depth analyses of PET tracer uptake in the vascular wall and potentially applied for fast and accurate quantification of inflammatory and infectious vascular diseases.

Whole-genome sequencing (WGS) of Mycobacterium tuberculosis (MTB) isolates can be used to get an accurate diagnosis, to guide clinical decision making, to control tuberculosis (TB) and for outbreak investigations. We evaluated the performance of long-read (LR) and/or short-read (SR) sequencing for anti-TB drug-resistance prediction using the TBProfiler and Mykrobe tools, the fraction of genome recovery, assembly accuracies and the robustness of two typing approaches based on core-genome SNP (cgSNP) typing and core-genome multi-locus sequence typing (cgMLST). Most of the discrepancies between phenotypic drug-susceptibility testing (DST) and drug-resistance prediction were observed for the first-line drugs rifampicin, isoniazid, pyrazinamide and ethambutol, mainly with LR sequence data. Resistance prediction to second-line drugs made by both TBProfiler and Mykrobe tools with SR- and LR-sequence data were in complete agreement with phenotypic DST except for one isolate. The SR assemblies were more accurate than the LR assemblies, having significantly (P

Background: Left ventricular assist devices (LVADs) improve quality of life and long-term survival in advanced heart failure, but device related infections (DRI) remain cumbersome. We evaluated the diagnostic capability of FDG-PET/CT and the additive value of semi-quantitative analysis for the diagnosis of DRI. Methods: LVAD recipients undergoing FDG-PET/CT between December 2012 and August 2020 for suspected DRI were retrospectively included. FDG-PET/CT was performed and evaluated according to EANM guidelines and assessors were blinded to the clinical context of included patients. Final clinical diagnosis of driveline infection and/or central device infection, based on multidisciplinary consensus and findings during surgery whenever performed, was used as the reference for the diagnosis. Results: 44 patients were evaluated for 62 episodes of suspected DRI. Clinical evaluation established driveline infection in 33 (54%) episodes, central device infection in 8 (14%) and combined infection in 2 (4%). Visual analysis of FDG-PET/CT achieved a sensitivity and specificity of 0.83 and 0.73, respectively, for driveline infections, while semi-quantitative analysis found comparable results (p=0.77). For central device component infections, visual analysis of FDG-PET/CT achieved a sensitivity and specificity of 1.0 and 0.26, respectively, while semi-quantitative analysis using SUVratio (background liver) achieved a sensitivity and specificity of 1.0 and 0.8, respectively. Both SUVmax and SUVratio outperformed visual analysis (pConclusions: FDG-PET/CT is a valuable tool for the assessment of DRI in LVAD recipients. Semiquantitative analysis significantly increases specificity of FDG-PET/CT for the analysis of central device components and should be considered in equivocal cases after visual analysis.

The aim of this study is to investigate the influence of sex, age, fat mass, fasting blood glucose level (FBGL), and estimated glomerular filtration rate (eGFR) on blood pool activity in patients with large vessel vasculitis (LVV). Blood pool activity was measured in the superior caval vein using mean, maximum, and peak standardized uptake values corrected for body weight (SUVs) and lean body mass (SULs) in 41 fluorodeoxyglucose positron emission tomography/computed tomography (FDG-PET/CT) scans of LVV patients. Sex influence on the blood pool activity was assessed with t-tests, while linear correlation analyses were used for age, fat mass, FBGL, and eGFR. Significantly higher SUVs were found in women compared with men, whereas SULs were similar between sexes. In addition, higher fat mass was associated with increased SUVs (r = 0.56 to 0.65; all p < 0.001) in the blood pool, but no correlations were found between SULs and fat mass (r = −0.25 to −0.15; all p > 0.05). Lower eGFR was associated with a higher FDG blood pool activity for all uptake values. In FDG-PET/CT studies with LVV patients, we recommend using SUL over SUV, while caution is advised in interpreting SUV and SUL measures when patients have impaired kidney function.

Introduction: Prosthetic valve endocarditis (PVE) is a serious disease affecting ~0.4% of prosthetic valve recipients per year. 18F-FDG-PET/CT has high sensitivity and specificity for PVE and is included as major criterion for the diagnosis in recent guidelines of the European Society of Cardiology. We addressed the question whether increased FDG-uptake in mediastinal lymph nodes could help to support the visual diagnostic assessment of PVE.Methods: In this sub-analysis of a previously published retrospective multicentre study, 160 unique patients were identified who underwent 18F-FDG-PET/CT for evaluation of suspected PVE. 18F-FDG-PET/CT was performed in adherence to the European Association of Nuclear Medicine guidelines of 2015 and scans were assessed for signs of mediastinal lymph node activity by 2 experienced nuclear medicine physicians who were blinded to clinical context. Clinical diagnosis of PVE had been established based on surgical findings or multidisciplinary consensus after a 1-year follow-up in 80 of 160 patients (50%).Results: In total, 52 patients showed increased mediastinal lymph node activity. Mediastinal lymph node activity on 18F-FDG-PET/CT did not increase diagnostic accuracy when added to the visual analysis of scans for signs of PVE: X2: 0.118, p = 0.731). After excluding patients with known confounders for 18F-FDG-PET/CT, namely use of Bioglue® during prosthetic valve implantation and C-reactive protein levels below 40 mg/L, mediastinal lymph node activity was still not of additional diagnostic value compared to visual analysis alone (X2:0.129, p = 0.723).Discussion: Assessment of mediastinal lymph node activity did not improve 18F-FDG-PET/CT diagnostic accuracy for suspected PVE compared to visual assessment of the valve alone, as it seems to be a rather a specific finding, that might be caused by sternal wound or mediastinal infections or even by subclinical respiratory infections. Future studies might elucidate whether increased FDG active lymph nodes indicate a high-risk patient group and whether more detailed assessment of mediastinal lymph nodes could improve their additional diagnostic benefit.

Purpose of Review Additional imaging modalities, such as FDG-PET/CT, have been included into the workup for patients with suspected infective endocarditis, according to major international guidelines published in 2015. The purpose of this review is to give an overview of FDG-PET/CT indications and standardized approaches in the setting of suspected infective endocarditis. Recent Findings There are two main indications for performing FDG-PET/CT in patients with suspected infective endocarditis: (i) detecting intracardiac infections and (ii) detection of (clinically silent) disseminated infectious disease. The diagnostic performance of FDG-PET/CT for intracardiac lesions depends on the presence of native valves, prosthetic valves, or implanted cardiac devices, with a sensitivity that is poor for native valve endocarditis and cardiac device-related lead infections, but much better for prosthetic valve endocarditis and cardiac device-related pocket infections. Specificity is high for all these indications. The detection of disseminated disease may also help establish the diagnosis and/or impact patient management. Summary Based on current evidence, FDG-PET/CT should be considered for detection of disseminated disease in suspected endocarditis. Absence of intracardiac lesions on FDG-PET/CT cannot rule out native valve endocarditis, but positive findings strongly support the diagnosis. For prosthetic valve endocarditis, standard use of FDG-PET/CT is recommended because of its high sensitivity and specificity. For implanted cardiac devices, FDG-PET/CT is also recommended, but should be evaluated with careful attention to clinical context, because its sensitivity is high for pocket infections, but low for lead infections. In patients with prosthetic valves with or without additional aortic prosthesis, combination with CTA should be considered. Optimal timing of FDG-PET/CT is important, both during clinical workup and technically (i.e., post tracer injection). In addition, procedural standardization is key and encompasses patient preparation, scan acquisition, reconstruction, subsequent analysis, and clinical interpretation. The recommendations discussed here will hopefully contribute to improved standardization and enhanced performance of FDG-PET/CT in the clinical management of patients with suspected infective endocarditis.

It is usually believed that the presence of labour unions is detrimental for the consumers since it raises wage. We show that this view may not be true if we endogenise the impacts of labour unions on firms' corporate strategies such as merger and acquisition. The presence of labour unions may benefit the consumers by deterring merger.

Background and Objective: Staphylococcus aureus is one of the major pathogens of nosocomial infections as wells as community-acquired (CA) infections worldwide. So far, large-scale comprehensive molecular and epidemiological characterisation of S. aureus from very diverse settings has not been carried out in India. The objective of this study is to evaluate the molecular, epidemiological and virulence characteristics of S. aureus in both community and hospital settings in Chennai, southern India. Methods: S. aureus isolates were obtained from four different groups (a) healthy individuals from closed community settings, (b) inpatients from hospitals, (c) outpatients from hospitals, representing isolates of hospital-community interface and (d) HIV-infected patients to define isolates associated with the immunocompromised. Antibiotic susceptibility testing, multiplex polymerase chain reactions for detection of virulence and resistance determinants, molecular typing including Staphylococcal cassette chromosome mec (SCCmec) and agr typing, were carried out. Sequencing-based typing was done using spa and multilocus sequence typing (MLST) methods. Clonal complexes (CC) of hospital and CA methicillin-resistant S. aureus (MRSA) were identified and compared for virulence and resistance. Results and Conclusion: A total of 769 isolates of S. aureus isolates were studied. The prevalence of MRSA was found to be 7.17%, 81.67%, 58.33% and 22.85% for groups a, b, c and d, respectively. Of the four SCCmec types (I, III, IV and V) detected, SCCmec V was found to be predominant. Panton-Valentine leucocidin toxin genes were detected among MRSA isolates harbouring SCCmec IV and V. A total of 78 spa types were detected, t657 being the most prevalent. 13 MLST types belonging to 9 CC were detected. CC1 (ST-772, ST-1) and CC8 (ST238, ST368 and ST1208) were found to be predominant among MRSA. CA-MRSA isolates with SCCmec IV and V were isolated from all study groups including hospitalised patients and were found to be similar by molecular tools. This shows that CA MRSA has probably infiltrated into the hospital settings.

Funding Acknowledgements Type of funding sources: Private company. Main funding source(s): Partnership UMCG Siemens for building the future of Health (PUSH) Background Left ventricular assist devices (LVADs) are increasingly used for the treatment of end-stage heart failure. LVADs improve quality of life and long-term survival, but device specific infections remain cumbersome. These infections can lead to life threatening complications and are difficult to diagnose with conventional radiological imaging. In this study, FDG-PET/CT accuracy for the diagnosis of LVAD specific infections was evaluated with specific focus on potential confounders and the additive value of semi-quantitative analysis. Materials/methods All LVAD recipients with an available FDG-PET/CT from September 2013 until August 2020 in two LVAD centres in the Netherlands, with a suspicion of driveline and/or central device infection were included. FDG-PET/CT was performed according to European Association of Nuclear Medicine (EANM) guidelines. Potential confounders were documented and assessors were blinded to the clinical context of included patients. Assessment of FDG-PET/CT was performed visually and semi-quantitatively with 6 regions of interest alongside the driveline and central device components. The final clinical diagnosis of either driveline infection or central device infection, based on findings during surgical intervention or multidisciplinary consensus, was used as the reference for diagnosis. Results In total 38 patients (average age 55 years, 84% males) were evaluated for a total of 55 episodes of suspected device-specific infection. Clinical evaluation established driveline infection in 28 cases, central device infection in 7 and combined infection in 4. Visual analysis obtained a sensitivity and specificity of 0.83 and 0.75 respectively, in differentiation between infected and non-infected drivelines. Visual analysis of FDG-PET/CT of central device components showed excellent sensitivity: 1.0, but suffered from poor specificity: 0.23. Semi-quantitative analysis using a SUVmax was comparable to visual analysis for establishing driveline infections, with a sensitivity and specificity of 0.80 and 0.84 respectively, while for central device infections, semi-quantitative analysis using a SUVratio outperformed visual analysis, with a sensitivity and specificity reaching 0.88 and 0.90 respectively. Conclusions FDG-PET/CT is a valuable tool for the assessment of device-specific infections in LVAD recipients. Semi-quantitative analysis can significantly increase diagnostic accuracy of FDG-PET/CT for the analysis of the central device components and should be considered in cases where the diagnosis cannot be rejected based on visual analysis.

ObjectivesData and data visualization are integral parts of (clinical) decision-making in general and stewardship (antimicrobial stewardship, infection control, and institutional surveillance) in particular. However, systematic research on the use of data visualization in stewardship is lacking. This study aimed at filling this gap by creating a visual dictionary of stewardship through an assessment of data visualization in stewardship research.MethodsA random sample of 150 data visualizations from published research articles on stewardship were assessed. The visualization vocabulary (content) and design space (design elements) were combined to create a visual dictionary. Additionally, visualization errors, chart junk, and quality were assessed to identify problems in current visualizations and to provide improvement recommendations.ResultsDespite a heterogeneous use of data visualization, distinct combinations of graphical elements to reflect stewardship data were identified. In general, bar (n=54; 36.0%) and line charts (n=42; 28.1%) were preferred visualization types. Visualization problems comprised colour scheme mismatches, double y-axis, hidden data points through overlaps, and chart junk. Recommendations were derived that can help to clarify visual communication, improve colour use for grouping/stratifying, improve the display of magnitude, and match visualizations to scientific standards.ConclusionsResults of this study can be used to guide data visualization creators in designing visualizations that fit the data and visual habits of the stewardship target audience. Additionally, the results can provide the basis to further expand the visual dictionary of stewardship towards more effective visualizations that improve data insights, knowledge, and clinical decision-making.

OBJECTIVE: Antimicrobial resistance (AMR) is a global threat to health and healthcare. In response to the growing AMR burden, research funding also increased. However, a comprehensive overview of the research output, including conceptual, temporal, and geographical trends, is missing. Therefore, this study uses topic modelling, a machine learning approach, to reveal the scientific evolution of AMR research and its trends, and provides an interactive user interface for further analyses.METHODS: Structural topic modelling (STM) was applied on a text corpus resulting from a PubMed query comprising AMR articles (1999-2018). A topic network was established and topic trends were analysed by frequency, proportion, and importance over time and space.RESULTS: In total, 88 topics were identified in 158,616 articles from 166 countries. AMR publications increased by 450% between 1999 and 2018, emphasizing the vibrancy of the field. Prominent topics in 2018 were Strategies for emerging resistances and diseases, Nanoparticles, and Stewardship. Emerging topics included Water and environment, and Sequencing. Geographical trends showed prominence of Multidrug-resistant tuberculosis (MDR-TB) in the WHO African Region, corresponding with the MDR-TB burden. China and India were growing contributors in recent years, following the United States of America as overall lead contributor.CONCLUSION: This study provides a comprehensive overview of the AMR research output thereby revealing the AMR research response to the increased AMR burden. Both the results and the publicly available interactive database serve as a base to inform and optimise future research.

ObjectivesInfection-related consultations on intensive care units (ICU) build an important cornerstone in the care for critically ill patients with (suspected) infections. The positive impact of consultations on quality of care and clinical outcome has previously been demonstrated. However, timing is essential and to date consultations are typically event-triggered and reactive. Here, we investigate a proactive approach by predicting infection-related consultations using machine learning models and routine electronic health records (EHR).MethodsWe used data from a mixed ICU at a large academic tertiary care hospital including 9684 admissions. EHR data comprised demographics, laboratory results, point-of-care tests, vital signs, line placements, and prescriptions. Consultations were performed by clinical microbiologists. The predicted target outcome (occurrence of a consultation) was modelled using random forest (RF), gradient boosting machines (RF), and long short-term memory neural networks (LSTM).ResultsOverall, 7.8 % of all admission received a consultation. Time-sensitive modelling approaches and increasing numbers of patient features (parameters) performed better than static approaches in predicting infection-related consultations at the ICU. Splitting a patient admission into eight-hour intervals and using LSTM resulted in the accurate prediction of consultations up to eight hours in advance with an area under the receiver operator curve of 0.921 and an area under precision recall curve of 0.673.ConclusionWe could successfully predict of infection-related consultations on an ICU up to eight hours in advance, even without using classical triggers, such as (interim) microbiology reports. Predicting this key event can potentially streamline ICU and consultant workflows and improve care and outcome for critically ill patients with (suspected) infections.

SummaryBackgroundAntimicrobial resistance (AMR) is a global threat to health and healthcare. In response to the growing AMR burden, research funding also increased. However, a comprehensive overview of the research output, including conceptual, temporal, and geographical trends, is missing. Therefore, this study uses topic modelling, a machine learning approach, to reveal the scientific evolution of AMR research and its trends, and provides an interactive user interface for further analyses.MethodsStructural topic modelling (STM) was applied on a text corpus resulting from a PubMed query comprising AMR articles (1999-2018). A topic network was established and topic trends were analysed by frequency, proportion, and importance over time and space.FindingsIn total, 88 topics were identified in 158616 articles from 166 countries. AMR publications increased by 450% between 1999 and 2018, emphasizing the vibrancy of the field. Prominent topics in 2018 were Strategies for emerging resistances and diseases, Nanoparticles, and Stewardship. Emerging topics included Water and environment, and Sequencing. Geographical trends showed prominence of Multidrug-resistant tuberculosis (MDR-TB) in the WHO African Region, corresponding with the MDR-TB burden. China and India were growing contributors in recent years, following the United States of America as overall lead contributor.InterpretationThis study provides a comprehensive overview of the AMR research output thereby revealing the AMR research response to the increased AMR burden. Both the results and the publicly available interactive database serve as a base to inform and optimise future research.FundingINTERREG-VA EurHealth-1Health (202085); European Commission Horizon 2020 FrameworkResearch in contextEvidence before this studyPrior to this study, PubMed, Web of Science, Scopus, and IEEE Xplore were queried to find studies providing a conceptual overview of antimicrobial resistance (AMR) research over time and space. The search string included keywords (“antimicrobial” OR antibiotic*) AND (resistan*) AND (“science mapping” OR bibliometric OR scientometric) in the title and abstract and focused on articles published before 2019 without language restrictions. Few studies were found relying on scientometric and bibliometric methods to assess either subfields of AMR research (e.g., AMR among uropathogens) or AMR-related fields (e.g., microbiology). No studies were found that focus on the entire AMR field. Therefore, this science mapping study using topic modelling was performed to provide an overview of the AMR field by identifying and assessing topics, trends, and geographical differences over time.Added value of this studyTo the best of our knowledge, this study is the first to use a science mapping approach to provide a comprehensive overview of the entire AMR research field, covering over 150 thousand articles published between 1999 and 2018. Our findings revealed important (e.g., Strategies for emerging resistances and diseases, Nanoparticles, and Stewardship) and emerging (e.g., Water and environment, and Sequencing) topics in AMR research. Lastly, this study resulted in an interactive user interface where all data are presented for further analyses.Implications of all the available evidenceOur comprehensive overview of the AMR field, including its conceptual structure, and temporal and geographical trends revealed the response of the research community to the AMR burden. The results and the openly available supporting data provide the base to guide future funding and research directions to tackle AMR.

Background: Antimicrobial resistance (AMR) is a global threat to health and healthcare. In response to the growing AMR burden, research funding also increased. However, a comprehensive overview of the research output, including conceptual, temporal, and geographical trends, is missing. Therefore, this study uses topic modelling, a machine learning approach, to reveal the scientific evolution of AMR research and its trends, and provides an interactive user interface for further analyses. Methods: Structural topic modelling (STM) was applied on a text corpus resulting from a PubMed query comprising AMR articles (1999-2018). A topic network was established and topic trends were analysed by frequency, proportion, and importance over time and space. Findings: In total, 88 topics were identified in 158616 articles from 166 countries. AMR publications increased by 450% between 1999 and 2018, emphasizing the vibrancy of the field. Prominent topics in 2018 were Strategies for emerging resistances and diseases, Nanoparticles, and Stewardship. Emerging topics included Water and environment, and Sequencing. Geographical trends showed prominence of Multidrug-resistant tuberculosis (MDR-TB) in the WHO African Region, corresponding with the MDR-TB burden. China and India were growing contributors in recent years, following the United States of America as overall lead contributor. Interpretation: This study provides a comprehensive overview of the AMR research output thereby revealing the AMR research response to the increased AMR burden. Both the results and the publicly available interactive database serve as a base to inform and optimise future research. Funding: INTERREG-VA EurHealth-1Health (202085); European Commission Horizon 2020 Framework Declaration of Interests: We declare no conflict of interests.

[18F]-fluorodeoxyglucose positron emission tomography ([18F]FDG PET/CT) has proven to be a useful diagnostic tool in patients with suspected infective endocarditis (IE), but is conflicting in relation to dental procedures. Questions: Is there a correlation between [18F]FDG PET/CT findings, recent dental treatment, and an affected oral cavity? (2) Is there a correlation between infective endocarditis (IE), oral health status, and (extra)cardiac findings on [18F]FDG PET/CT? Methods: This retrospective study included 52 patients. All [18F]FDG PET/CT scans were examined visually by pattern recognition using a three-point scale and semi-quantified within the volume of interest (VOI) using SUVmax. Results: 19 patients were diagnosed with IE (group 1), 14 with possible IE (group 2), and 19 without IE based on the modified Duke criteria (group 3). No correlation was found between visual PET and SUVmax and sites of oral inflammation and infection. The visual PET scores and SUVmax were not significantly different between all groups. A significant difference in the SUVmax of the valve between all groups was observed. Conclusions: This study suggests that no correlation exists between the PET findings in the oral cavity and dental treatments or inflammation/infection. No correlation between IE, actual oral health status, and extra-cardiac findings was demonstrated. Additional research is needed to conclude whether [18F]FDG PET/CT imaging is a reliable diagnostic modality for oral inflammation and infection sites.

Healthcare-associated infections caused by multidrug-resistant organisms (MDROs) constitute a major challenge worldwide, but care providers are often not sufficiently incentivized to implement recommended infection prevention measures to prevent the spread of such infections. We propose a new approach which creates incentives for hospitals, external laboratories and insurers to collaborate on preventing MDRO outbreaks by testing more and implementing infection prevention measures. This tripartite insurance model (TIM) redistributes the costs of preventing and combating MDRO outbreaks in a way that all parties benefit from reducing the number of outbreaks. (C) 2021 The Authors. Published by Elsevier Ltd on behalf of European Society of Clinical Microbiology and Infectious Diseases.