Positive effects of audit and feedback on antimicrobial use in hospitalized patients limited to audited patients.

Objective: Audit and feedback is an antimicrobial stewardship (AMS) strategy, with the potential to also optimize antimicrobial use in non-audited patients. This study aimed to determine whether audit and feedback reduce antimicrobial use in both audited and non-audited patients. Design: Before-after trial with a 1-year intervention period and 2.5-year historical cohort. Setting: 750-bed community hospital in the Netherlands. Patients: All patients admitted to the urology wards during the 3.5-year study period were observed. Patients were classified as using antimicrobials if any antimicrobial was used for therapeutic reasons. Patients using antimicrobials prophylactically were excluded from measurements. Intervention: The AMS team provided audit and feedback on antimicrobial use for patients using antimicrobials for 2 days. Retrospectively, antimicrobial use and length of stay (LOS) were compared with the historical cohort. Results: Audits modified antimicrobial treatment in 52.8% of the cases. De-escalating, stopping, and switching from intravenous to oral treatment accounted for 72% of these modifications. Compared to patients from the cohort, who also used antimicrobials for 2 days, antimicrobial use decreased from 14.21 DDD/patient (95% CI, 13.08-15.34) to 11.45 DDD/patient (95% CI, 8.26-14.64; P = .047) for audited patients. Furthermore, mean LOS decreased from 7.42 days (95% CI, 6.79-8.06) to 6.13 days (95% CI, 5.38-6.89; P = .031). However, looking at all patients admitted to the urology wards, the percentage of patients using antimicrobials and total antimicrobial use remained unchanged. Conclusions: Audit and feedback reduce antimicrobial use and LOS, but only for audited patients. Positive effects are not automatically transferred to patients for whom no audits have been performed.

Automated multiclass segmentation, quantification, and visualization of the diseased aorta on hybrid PET/CT-SEQUOIA.

BACKGROUND: Cardiovascular disease is the most common cause of death worldwide, including infection and inflammation related conditions. Multiple studies have demonstrated potential advantages of hybrid positron emission tomography combined with computed tomography (PET/CT) as an adjunct to current clinical inflammatory and infectious biochemical markers. To quantitatively analyze vascular diseases at PET/CT, robust segmentation of the aorta is necessary. However, manual segmentation is extremely time-consuming and labor-intensive. PURPOSE: To investigate the feasibility and accuracy of an automated tool to segment and quantify multiple parts of the diseased aorta on unenhanced low-dose computed tomography (LDCT) as an anatomical reference for PET-assessed vascular disease. METHODS: A software pipeline was developed including automated segmentation using a 3D U-Net, calcium scoring, PET uptake quantification, background measurement, radiomics feature extraction, and 2D surface visualization of vessel wall calcium and tracer uptake distribution. To train the 3D U-Net, 352 non-contrast LDCTs from (2-[18F]FDG and Na[18F]F) PET/CTs performed in patients with various vascular pathologies with manual segmentation of the ascending aorta, aortic arch, descending aorta, and abdominal aorta were used. The last 22 consecutive scans were used as a hold-out internal test set. The remaining dataset was randomly split into training (n = 264; 80%) and validation (n = 66; 20%) sets. Further evaluation was performed on an external test set of 49 PET/CTs. The dice similarity coefficient (DSC) and Hausdorff distance (HD) were used to assess segmentation performance. Automatically obtained calcium scores and uptake values were compared with manual scoring obtained using clinical softwares (syngo.via and Affinity Viewer) in six patient images. intraclass correlation coefficients (ICC) were calculated to validate calcium and uptake values. RESULTS: Fully automated segmentation of the aorta using a 3D U-Net was feasible in LDCT obtained from PET/CT scans. The external test set yielded a DSC of 0.867 ± 0.030 and HD of 1.0 [0.6-1.4] mm, similar to an open-source model with a DSC of 0.864 ± 0.023 and HD of 1.4 [1.0-1.8] mm. Quantification of calcium and uptake values were in excellent agreement with clinical software (ICC: 1.00 [1.00-1.00] and 0.99 [0.93-1.00] for calcium and uptake values, respectively). CONCLUSIONS: We present an automated pipeline to segment the ascending aorta, aortic arch, descending aorta, and abdominal aorta on LDCT from PET/CT and to accurately provide uptake values, calcium scores, background measurement, radiomics features, and a 2D visualization. We call this algorithm SEQUOIA (SEgmentation, QUantification, and visualizatiOn of the dIseased Aorta) and is available at https://github.com/UMCG-CVI/SEQUOIA. This model could augment the utility of aortic evaluation at PET/CT studies tremendously, irrespective of the tracer, and potentially provide fast and reliable quantification of cardiovascular diseases in clinical practice, both for primary diagnosis and disease monitoring.

Identifying the need for infection-related consultations in intensive care patients using machine learning models.

Infection-related consultations on intensive care units (ICU) have a positive impact on quality of care and clinical outcome. However, timing of these consultations is essential and to date they are typically event-triggered and reactive. Here, we investigate a proactive approach to identify patients in need for infection-related consultations by machine learning models using routine electronic health records. Data was retrieved from a mixed ICU at a large academic tertiary care hospital including 9684 admissions. Infection-related consultations were predicted using logistic regression, random forest, gradient boosting machines, and long short-term memory neural networks (LSTM). Overall, 7.8% of admitted patients received an infection-related consultation. Time-sensitive modelling approaches performed better than static approaches. Using LSTM resulted in the prediction of infection-related consultations in the next clinical shift (up to eight hours in advance) with an area under the receiver operating curve (AUROC) of 0.921 and an area under the precision recall curve (AUPRC) of 0.541. The successful prediction of infection-related consultations for ICU patients was done without the use of classical triggers, such as (interim) microbiology reports. Predicting this key event can potentially streamline ICU and consultant workflows and improve care as well as outcome for critically ill patients with (suspected) infections.

Better antimicrobial resistance data analysis and reporting in less time.

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 < 0.001). Average task completion per round changed from 56% to 96% (P < 0.05). The proportion of correct answers in the available results increased from 37.9% in the first to 97.9% in the second round (P < 0.001). Usability of the new tools was rated with a median of 83.8 (out of 100) on the SUS. 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.

Added value of semi-quantitative analysis of [18F]FDG PET/CT for the diagnosis of device-related infections in patients with a left ventricular assist device.

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.

Host DNA depletion can increase the sensitivity of Mycobacterium spp. detection through shotgun metagenomics in sputum.

Identification and phenotypic drug-susceptibility testing for mycobacteria are time-consuming and challenging but essential for managing mycobacterial infections. Next-generation sequencing (NGS) technologies can increase diagnostic speed and quality, but standardization is still lacking for many aspects (e.g., unbiased extraction, host depletion, bioinformatic analysis). Targeted PCR approaches directly on sample material are limited by the number of targets that can be included. Unbiased shotgun metagenomics on direct material is hampered by the massive amount of host DNA, which should be removed to improve the microbial detection sensitivity. For this reason, we developed a method for NGS-based diagnosis of mycobacteria directly from patient material. As a model, we used the non-tuberculous mycobacterium (NTM) Mycobacterium abscessus. We first compared the efficiency of three different DNA extraction kits for isolating DNA (quality and concentration). The two most efficient kits were then used in a follow-up study using artificial sputum. Finally, one extraction kit was selected and further evaluated for DNA isolation from a patients' sputum mixture spiked with M. abscessus at three concentrations (final concentrations 108, 107, 106 CFU/ml). The spiked sputum samples were processed with and without saponin treatment (ST) in combination with DNAse treatment prior to bacterial DNA extraction to evaluate the recovery of bacteria and depletion of host DNA by PCR and Illumina sequencing. While Ct values of the qPCR targeting mycobacterial ITS DNA remained rather stable, Ct values in the qPCR targeting the human ß-actin gene increased by five Ct values in ST samples. In subsequent Illumina sequencing, a decrease of 89% of reads mapped to the human genome was observed in ST samples. The percentage of reads mapped to M. abscessus (108 CFU/ml) increased by 89%, and the sequencing depth increased two times when undergoing ST. In conclusion, the sensitivity of M. abscessus detection in artificial sputum was increased using a saponin pre-treatment step. The saponin followed by the DNase I treatment approach could be efficiently applied to detect and characterize mycobacterial infections, including tuberculosis, directly from sputum.

[Current inpatient treatment options for COVID-19: an update]. / Medicamenteuze behandeling covid-19 in de tweede lijn.

In this article we provide an overview of the current treatment recommendations for COVID-19. These recommendations are made by the SWAB (StichtingWerkgroepAntibioticabeleid), in cooperation with the FMS (FederatieMedischSpecialisten (online: swab.nl/nl/covid-19.). Treatment options for patients in both ambulatory care and admitted to the hospital are listed. These treatment options include both antiinflammatory and antiviral therapy.

Mapping twenty years of antimicrobial resistance research trends.

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.

The Visual Dictionary of Antimicrobial Stewardship, Infection Control, and Institutional Surveillance Data.

Objectives: Data 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 (i.e., graphical representation of quantitative information) in stewardship research. Methods: A random sample of 150 data visualizations from published research articles on stewardship were assessed (excluding geographical maps and flowcharts). 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. Results: Despite 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 color scheme mismatches, double y-axis, hidden data points through overlaps, and chart junk. Recommendations were derived that can help to clarify visual communication, improve color use for grouping/stratifying, improve the display of magnitude, and match visualizations to scientific standards. Conclusion: Results 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 toward more effective visualizations that improve data insights, knowledge, and clinical decision-making.

Toward Reliable Uptake Metrics in Large Vessel Vasculitis Studies.

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.

18F-FDG-Uptake in Mediastinal Lymph Nodes in Suspected Prosthetic Valve Endocarditis: Predictor or Confounder?

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: X 2: 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.

The tripartite insurance model (TIM): a financial incentive to prevent outbreaks of infections due to multidrug-resistant microorganisms in hospitals.

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.

[The medicinal treatment of COVID-19: a brief update]. / Medicamenteuze behandeling van COVID-19.

Much has changed in the medical treatment of COVID-19 after the first patient with an infection with SARS-CoV-2 in the Netherlands was diagnosed in February 2020. On the basis of limited data, at first only off-label use of (hydroxy)chloroquine seemed to be a treatment option. However, now based on the findings of several randomized studies, other medicines have been included in the Dutch guidelines about the treatment of COVID-19. In this article, we will briefly discuss the current state of affairs with regard to the drugs (hydroxy) chloroquine, remdesivir and corticosteroids. Again, it appears that only well-executed randomized clinical trials can determine the status of various supposedly effective drugs.

Relationship between 18F-FDG Uptake in the Oral Cavity, Recent Dental Treatments, and Oral Inflammation or Infection: A Retrospective Study of Patients with Suspected Endocarditis.

[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.

Imaging infective endocarditis: Adherence to a diagnostic flowchart and direct comparison of imaging techniques.

BACKGROUND: Multimodality imaging is recommended to diagnose infective endocarditis. Value of additional imaging to echocardiography in patients selected by a previously proposed flowchart has not been evaluated. METHODS: An observational single-center study was performed. Adult patients suspected of endocarditis/device infection were prospectively and consecutively enrolled from March 2016 to August 2017. Adherence to a diagnostic imaging-in-endocarditis-flowchart was evaluated in 176 patients. Imaging techniques were compared head-to-head in 46 patients receiving echocardiography (transthoracic plus transesophageal), multi-detector computed tomography angiography (MDCTA), and 18F-fluorodeoxyglucose positron emission tomography (FDG-PET/CT). RESULTS: 69% of patients (121/176) adhered to the flowchart. Sensitivity of echocardiography, MDCTA, FDG-PET/CT in patients without prosthesis was 71%, 57%, 29% (86% when combined), while specificity was 100%, 75%, 100%, respectively. Sensitivity in patients with prosthesis was 75%, 75%, 83%, respectively (100% when combined), while specificity was 86% for all three modalities. Echocardiography performed best in the assessment of vegetations, morphological valve abnormalities/dehiscence, septum defects, and fistula formation. MDCTA performed best in the assessment of abscesses and ventricular assist device infection. FDG-PET/CT performed best in the assessment of cardiac device infection, extracardiac infectious foci, and alternative diagnoses. CONCLUSIONS: This study demonstrates that the evaluated imaging-in-endocarditis-flowchart is applicable in daily clinical practice. Echocardiography, MDCTA, and FDG-PET/CT provide relevant complementary diagnostic information, particularly in patients with intracardiac prosthetic material.

Rapid Analysis of Diagnostic and Antimicrobial Patterns in R (RadaR): Interactive Open-Source Software App for Infection Management and Antimicrobial Stewardship.

BACKGROUND: Analyzing process and outcome measures for all patients diagnosed with an infection in a hospital, including those suspected of having an infection, requires not only processing of large datasets but also accounting for numerous patient parameters and guidelines. Substantial technical expertise is required to conduct such rapid, reproducible, and adaptable analyses; however, such analyses can yield valuable insights for infection management and antimicrobial stewardship (AMS) teams. OBJECTIVE: The aim of this study was to present the design, development, and testing of RadaR (Rapid analysis of diagnostic and antimicrobial patterns in R), a software app for infection management, and to ascertain whether RadaR can facilitate user-friendly, intuitive, and interactive analyses of large datasets in the absence of prior in-depth software or programming knowledge. METHODS: RadaR was built in the open-source programming language R, using Shiny, an additional package to implement Web-app frameworks in R. It was developed in the context of a 1339-bed academic tertiary referral hospital to handle data of more than 180,000 admissions. RESULTS: RadaR enabled visualization of analytical graphs and statistical summaries in a rapid and interactive manner. It allowed users to filter patient groups by 17 different criteria and investigate antimicrobial use, microbiological diagnostic use and results including antimicrobial resistance, and outcome in length of stay. Furthermore, with RadaR, results can be stratified and grouped to compare defined patient groups on the basis of individual patient features. CONCLUSIONS: AMS teams can use RadaR to identify areas within their institutions that might benefit from increased support and targeted interventions. It can be used for the assessment of diagnostic and therapeutic procedures and for visualizing and communicating analyses. RadaR demonstrated the feasibility of developing software tools for use in infection management and for AMS teams in an open-source approach, thus making it free to use and adaptable to different settings.

A Comparison of Three Different Bioinformatics Analyses of the 16S-23S rRNA Encoding Region for Bacterial Identification.

Rapid and reliable identification of bacterial pathogens directly from patient samples is required for optimizing antimicrobial therapy. Although Sanger sequencing of the 16S ribosomal RNA (rRNA) gene is used as a molecular method, species identification and discrimination is not always achievable for bacteria as their 16S rRNA genes have sometimes high sequence homology. Recently, next generation sequencing (NGS) of the 16S-23S rRNA encoding region has been proposed for reliable identification of pathogens directly from patient samples. However, data analysis is laborious and time-consuming and a database for the complete 16S-23S rRNA encoding region is not available. Therefore, a better, faster, and stronger approach is needed for NGS data analysis of the 16S-23S rRNA encoding region. We compared speed and diagnostic accuracy of different data analysis approaches: de novo assembly followed by Basic Local Alignment Search Tool (BLAST), operational taxonomic unit (OTU) clustering, or mapping using an in-house developed 16S-23S rRNA encoding region database for the identification of bacterial species. De novo assembly followed by BLAST using the in-house database was superior to the other methods, resulting in the shortest turnaround time (2 h and 5 min), approximately 2 h less than OTU clustering and 4.5 h less than mapping, and a sensitivity of 80%. Mapping was the slowest and most laborious data analysis approach with a sensitivity of 60%, whereas OTU clustering was the least laborious approach with 70% sensitivity. Although the in-house database requires more sequence entries to improve the sensitivity, the combination of de novo assembly and BLAST currently appears to be the optimal approach for data analysis.

Clonal clusters and virulence factors of methicillin-resistant Staphylococcus Aureus: Evidence for community-acquired methicillin-resistant Staphylococcus Aureus infiltration into hospital settings in Chennai, South India.

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.

Marked Changes in Gut Microbiota in Cardio-Surgical Intensive Care Patients: A Longitudinal Cohort Study.

Background: Virtually no studies on the dynamics of the intestinal microbiota in patients admitted to the intensive care unit (ICU) are published, despite the increasingly recognized important role of microbiota on human physiology. Critical care patients undergo treatments that are known to influence the microbiota. However, dynamics and extent of such changes are not yet fully understood. To address this topic, we analyzed the microbiota before, during and after planned major cardio surgery that, for the first time, allowed us to follow the microbial dynamics of critical care patients. In this prospective, observational, longitudinal, single center study, we analyzed the fecal microbiota using 16S rRNA gene sequencing. Results: Samples of 97 patients admitted between April 2015 and November 2016 were included. In 32 patients, data of all three time points (before, during and after admission) were available for analysis. We found a large intra-individual variation in composition of gut microbiota. During admission, a significant change in microbial composition occurred in most patients, with a significant increase in pathobionts combined with a decrease in strictly anaerobic gut bacteria, typically beneficial for health. A lower bacterial diversity during admission was associated with longer hospitalization. In most patients analyzed at all three time points, the change in microbiota during hospital stay reverted to the original composition post-discharge. Conclusions: Our study shows that, even with a short ICU stay, patients present a significant change in microbial composition shortly after admission. The unique longitudinal setup of this study displayed a restoration of the microbiota in most patients to baseline composition post-discharge, which demonstrated its great restorative capacity. A relative decrease in benign or even beneficial bacteria and increase of pathobionts shifts the microbial balance in the gut, which could have clinical relevance. In future studies, the microbiota of ICU patients should be considered a good target for optimisation.