In Support of Universal Admission Testing for SARS-CoV-2 During Significant Community Transmission.

Many hospitals have stopped or are considering stopping universal admission testing for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We discuss reasons why admission testing should still be part of a layered system to prevent hospital-acquired SARS-CoV-2 infections during times of significant community transmission. These include the morbidity of SARS-CoV-2 in vulnerable patients, the predominant contribution of presymptomatic and asymptomatic people to transmission, the high rate of transmission between patients in shared rooms, and data suggesting surveillance testing is associated with fewer nosocomial infections. Preferences of diverse patient populations, particularly the hardest-hit communities, should be surveyed and used to inform prevention measures. Hospitals' ethical responsibility to protect patients from serious infections should predominate over concerns about costs, labor, and inconvenience. We call for more rigorous data on the incidence and morbidity of nosocomial SARS-CoV-2 infections and more research to help determine when to start, stop, and restart universal admission testing and other prevention measures.

Risk of Misleading Conclusions in Observational Studies of Time-to-Antibiotics and Mortality in Suspected Sepsis.

BACKGROUND: Influential studies conclude that each hour until antibiotics increases mortality in sepsis. However, these analyses often (1) adjusted for limited covariates, (2) included patients with long delays until antibiotics, (3) combined sepsis and septic shock, and (4) used linear models presuming each hour delay has equal impact. We evaluated the effect of these analytic choices on associations between time-to-antibiotics and mortality. METHODS: We retrospectively identified 104 248 adults admitted to 5 hospitals from 2015-2022 with suspected infection (blood culture collection and intravenous antibiotics ≤24 h of arrival), including 25 990 with suspected septic shock and 23 619 with sepsis without shock. We used multivariable regression to calculate associations between time-to-antibiotics and in-hospital mortality under successively broader confounding-adjustment, shorter maximum time-to-antibiotic intervals, stratification by illness severity, and removing assumptions of linear hourly associations. RESULTS: Changing covariates, maximum time-to-antibiotics, and severity stratification altered the magnitude, direction, and significance of observed associations between time-to-antibiotics and mortality. In a fully adjusted model of patients treated ≤6 hours, each hour was associated with higher mortality for septic shock (adjusted odds ratio [aOR]: 1.07; 95% CI: 1.04-1.11) but not sepsis without shock (aOR: 1.03; .98-1.09) or suspected infection alone (aOR: .99; .94-1.05). Modeling each hour separately confirmed that every hour of delay was associated with increased mortality for septic shock, but only delays >6 hours were associated with higher mortality for sepsis without shock. CONCLUSIONS: Associations between time-to-antibiotics and mortality in sepsis are highly sensitive to analytic choices. Failure to adequately address these issues can generate misleading conclusions.

Real-Time Investigation of a Large Nosocomial Influenza A Outbreak Informed by Genomic Epidemiology.

BACKGROUND: Nosocomial respiratory virus outbreaks represent serious public health challenges. Rapid and precise identification of cases and tracing of transmission chains is critical to end outbreaks and to inform prevention measures. METHODS: We combined conventional surveillance with influenza A virus (IAV) genome sequencing to identify and contain a large IAV outbreak in a metropolitan healthcare system. A total of 381 individuals, including 91 inpatients and 290 healthcare workers (HCWs), were included in the investigation. RESULTS: During a 12-day period in early 2019, infection preventionists identified 89 HCWs and 18 inpatients as cases of influenza-like illness (ILI), using an amended definition without the requirement for fever. Sequencing of IAV genomes from available nasopharyngeal specimens identified 66 individuals infected with a nearly identical strain of influenza A H1N1pdm09 (43 HCWs, 17 inpatients, and 6 with unspecified affiliation). All HCWs infected with the outbreak strain had received the seasonal influenza virus vaccination. Characterization of 5 representative outbreak viral isolates did not show antigenic drift. In conjunction with IAV genome sequencing, mining of electronic records pinpointed the origin of the outbreak as a single patient and a few interactions in the emergency department that occurred 1 day prior to the index ILI cluster. CONCLUSIONS: We used precision surveillance to delineate a large nosocomial IAV outbreak, mapping the source of the outbreak to a single patient rather than HCWs as initially assumed based on conventional epidemiology. These findings have important ramifications for more-effective prevention strategies to curb nosocomial respiratory virus outbreaks.

A Complete Genome Screening Program of Clinical Methicillin-Resistant Staphylococcus aureus Isolates Identifies the Origin and Progression of a Neonatal Intensive Care Unit Outbreak.

Whole-genome sequencing (WGS) of Staphylococcus aureus is increasingly used as part of infection prevention practices. In this study, we established a long-read technology-based WGS screening program of all first-episode methicillin-resistant Staphylococcus aureus (MRSA) blood infections at a major urban hospital. A survey of 132 MRSA genomes assembled from long reads enabled detailed characterization of an outbreak lasting several months of a CC5/ST105/USA100 clone among 18 infants in a neonatal intensive care unit (NICU). Available hospital-wide genome surveillance data traced the origins of the outbreak to three patients admitted to adult wards during a 4-month period preceding the NICU outbreak. The pattern of changes among complete outbreak genomes provided full spatiotemporal resolution of its progression, which was characterized by multiple subtransmissions and likely precipitated by equipment sharing between adults and infants. Compared to other hospital strains, the outbreak strain carried distinct mutations and accessory genetic elements that impacted genes with roles in metabolism, resistance, and persistence. This included a DNA recognition domain recombination in the hsdS gene of a type I restriction modification system that altered DNA methylation. Transcriptome sequencing (RNA-Seq) profiling showed that the (epi)genetic changes in the outbreak clone attenuated agr gene expression and upregulated genes involved in stress response and biofilm formation. Overall, our findings demonstrate the utility of long-read sequencing for hospital surveillance and for characterizing accessory genomic elements that may impact MRSA virulence and persistence.

Comprehensive innate immune profiling of chikungunya virus infection in pediatric cases.

Chikungunya virus (CHIKV) is a mosquito-borne alphavirus that causes global epidemics of debilitating disease worldwide. To gain functional insight into the host cellular genes required for virus infection, we performed whole-blood RNA-seq, 37-plex mass cytometry of peripheral blood mononuclear cells (PBMCs), and serum cytokine measurements of acute- and convalescent-phase samples obtained from 42 children naturally infected with CHIKV Semi-supervised classification and clustering of single-cell events into 57 sub-communities of canonical leukocyte phenotypes revealed a monocyte-driven response to acute infection, with the greatest expansions in "intermediate" CD14++CD16+ monocytes and an activated subpopulation of CD14+ monocytes. Increases in acute-phase CHIKV envelope protein E2 expression were highest for monocytes and dendritic cells. Serum cytokine measurements confirmed significant acute-phase upregulation of monocyte chemoattractants. Distinct transcriptomic signatures were associated with infection timepoint, as well as convalescent-phase anti-CHIKV antibody titer, acute-phase viremia, and symptom severity. We present a multiscale network that summarizes all observed modulations across cellular and transcriptomic levels and their interactions with clinical outcomes, providing a uniquely global view of the biomolecular landscape of human CHIKV infection.

Genome Plasticity of agr-Defective Staphylococcus aureus during Clinical Infection.

Therapy for bacteremia caused by Staphylococcus aureus is often ineffective, even when treatment conditions are optimal according to experimental protocols. Adapted subclones, such as those bearing mutations that attenuate agr-mediated virulence activation, are associated with persistent infection and patient mortality. To identify additional alterations in agr-defective mutants, we sequenced and assembled the complete genomes of clone pairs from colonizing and infected sites of several patients in whom S. aureus demonstrated a within-host loss of agr function. We report that events associated with agr inactivation result in agr-defective blood and nares strain pairs that are enriched in mutations compared to pairs from wild-type controls. The random distribution of mutations between colonizing and infecting strains from the same patient, and between strains from different patients, suggests that much of the genetic complexity of agr-defective strains results from prolonged infection or therapy-induced stress. However, in one of the agr-defective infecting strains, multiple genetic changes resulted in increased virulence in a murine model of bloodstream infection, bypassing the mutation of agr and raising the possibility that some changes were selected. Expression profiling correlated the elevated virulence of this agr-defective mutant to restored expression of the agr-regulated ESAT6-like type VII secretion system, a known virulence factor. Thus, additional mutations outside the agr locus can contribute to diversification and adaptation during infection by S. aureus agr mutants associated with poor patient outcomes.

Genetic Basis of Emerging Vancomycin, Linezolid, and Daptomycin Heteroresistance in a Case of Persistent Enterococcus faecium Bacteremia.

Whole-genome sequencing was used to examine a persistent Enterococcus faecium bacteremia that acquired heteroresistance to three antibiotics in response to prolonged multidrug therapy. A comparison of the complete genomes before and after each change revealed the emergence of known resistance determinants for vancomycin and linezolid and suggested that a novel mutation in fabF, encoding a fatty acid synthase, was responsible for daptomycin nonsusceptibility. Plasmid recombination contributed to the progressive loss of vancomycin resistance after withdrawal of the drug.

Interpreting cancer genomes using systematic host network perturbations by tumour virus proteins.

Genotypic differences greatly influence susceptibility and resistance to disease. Understanding genotype-phenotype relationships requires that phenotypes be viewed as manifestations of network properties, rather than simply as the result of individual genomic variations. Genome sequencing efforts have identified numerous germline mutations, and large numbers of somatic genomic alterations, associated with a predisposition to cancer. However, it remains difficult to distinguish background, or 'passenger', cancer mutations from causal, or 'driver', mutations in these data sets. Human viruses intrinsically depend on their host cell during the course of infection and can elicit pathological phenotypes similar to those arising from mutations. Here we test the hypothesis that genomic variations and tumour viruses may cause cancer through related mechanisms, by systematically examining host interactome and transcriptome network perturbations caused by DNA tumour virus proteins. The resulting integrated viral perturbation data reflects rewiring of the host cell networks, and highlights pathways, such as Notch signalling and apoptosis, that go awry in cancer. We show that systematic analyses of host targets of viral proteins can identify cancer genes with a success rate on a par with their identification through functional genomics and large-scale cataloguing of tumour mutations. Together, these complementary approaches increase the specificity of cancer gene identification. Combining systems-level studies of pathogen-encoded gene products with genomic approaches will facilitate the prioritization of cancer-causing driver genes to advance the understanding of the genetic basis of human cancer.

How next-generation sequencing and multiscale data analysis will transform infectious disease management.

Recent reviews have examined the extent to which routine next-generation sequencing (NGS) on clinical specimens will improve the capabilities of clinical microbiology laboratories in the short term, but do not explore integrating NGS with clinical data from electronic medical records (EMRs), immune profiling data, and other rich datasets to create multiscale predictive models. This review introduces a range of "omics" and patient data sources relevant to managing infections and proposes 3 potentially disruptive applications for these data in the clinical workflow. The combined threats of healthcare-associated infections and multidrug-resistant organisms may be addressed by multiscale analysis of NGS and EMR data that is ideally updated and refined over time within each healthcare organization. Such data and analysis should form the cornerstone of future learning health systems for infectious disease.

Whole-genome sequencing identifies emergence of a quinolone resistance mutation in a case of Stenotrophomonas maltophilia bacteremia.

Whole-genome sequences for Stenotrophomonas maltophilia serial isolates from a bacteremic patient before and after development of levofloxacin resistance were assembled de novo and differed by one single-nucleotide variant in smeT, a repressor for multidrug efflux operon smeDEF. Along with sequenced isolates from five contemporaneous cases, they displayed considerable diversity compared against all published complete genomes. Whole-genome sequencing and complete assembly can conclusively identify resistance mechanisms emerging in S. maltophilia strains during clinical therapy.

ChromoZoom: a flexible, fluid, web-based genome browser.

UNLABELLED: Current web-based genome browsers require repetitious user input to scroll over long distances, alter the drawing density of elements or zoom through multiple orders of magnitude. Generally, either the server or the client is responsible for the majority of data processing, resulting in either servers having to receive and handle data relevant only to one user, or clients redundantly processing widely viewed data. ChromoZoom pre-renders and caches general-use tracks into tiled images on the server and serves them in an interactive web interface with inertial scrolling and precise, fluent zooming via the mouse wheel or trackpad. Custom tracks in several formats can be rendered by client-side code alongside the pre-rendered tracks, minimizing server load because of user-specific rendering and eliminating the need to transmit private data. ChromoZoom thereby enables rapid and simultaneous exploration of curated, experimental and personal genomic datasets. AVAILABILITY: Human and yeast genome researchers may browse recent assemblies within ChromoZoom at http://chromozoom.org/. Source code is available at http://github.com/rothlab/chromozoom/.

Timing and Spectrum of Antibiotic Treatment for Suspected Sepsis and Septic Shock: Why so Controversial?

Sepsis guidelines and mandates encourage increasingly aggressive time-to-antibiotic targets for broad-spectrum antimicrobials for suspected sepsis and septic shock. This has caused considerable controversy due to weaknesses in the underlying evidence and fear that overly strict antibiotic deadlines may harm patients by perpetuating or escalating overtreatment. Indeed, a third or more of patients currently treated for sepsis and septic shock have noninfectious or nonbacterial conditions. These patients risk all the potential harms of antibiotics without their possible benefits. Updated Surviving Sepsis Campaign guidelines now emphasize the importance of tailoring antibiotics to each patient's likelihood of infection, risk for drug-resistant pathogens, and severity-of-illness.

PathoSPOT genomic epidemiology reveals under-the-radar nosocomial outbreaks.

BACKGROUND: Whole-genome sequencing (WGS) is increasingly used to map the spread of bacterial and viral pathogens in nosocomial settings. A limiting factor for more widespread adoption of WGS for hospital infection prevention practices is the availability of standardized tools for genomic epidemiology. METHODS: We developed the Pathogen Sequencing Phylogenomic Outbreak Toolkit (PathoSPOT) to automate integration of genomic and medical record data for rapid detection and tracing of nosocomial outbreaks. To demonstrate its capabilities, we applied PathoSPOT to complete genome surveillance data of 197 MRSA bacteremia cases from two hospitals during a 2-year period. RESULTS: PathoSPOT identified 8 clonal clusters encompassing 33 patients (16.8% of cases), none of which had been recognized by standard practices. The largest cluster corresponded to a prolonged outbreak of a hospital-associated MRSA clone among 16 adults, spanning 9 wards over a period of 21 months. Analysis of precise timeline and location data with our toolkit suggested that an initial exposure event in a single ward led to infection and long-term colonization of multiple patients, followed by transmissions to other patients during recurrent hospitalizations. CONCLUSIONS: We demonstrate that PathoSPOT genomic surveillance enables the detection of complex transmission chains that are not readily apparent from epidemiological data and that contribute significantly to morbidity and mortality, enabling more effective intervention strategies.

Discontinuation of Universal Admission Testing for SARS-CoV-2 and Hospital-Onset COVID-19 Infections in England and Scotland.

This quality improvement study examines the association between the discontinuation of universal admission testing for SARS-CoV-2 infections and hospital-onset SARS-CoV-2 infections in England and Scotland.

Estimating Local Costs Associated With Clostridium difficile Infection Using Machine Learning and Electronic Medical Records.

BACKGROUND Reported per-patient costs of Clostridium difficile infection (CDI) vary by 2 orders of magnitude among different hospitals, implying that infection control officers need precise, local analyses to guide rational decision making between interventions. OBJECTIVE We sought to comprehensively estimate changes in length of stay (LOS) attributable to CDI at a single urban tertiary-care facility using only data automatically extractable from the electronic medical record (EMR). METHODS We performed a retrospective cohort study of 171,938 visits spanning a 7-year period. In total, 23,968 variables were extracted from EMR data recorded within 24 hours of admission to train elastic-net regularized logistic regression models for propensity score matching. To address time-dependent bias (reverse causation), we separately stratified comparisons by time of infection, and we fit multistate models. RESULTS The estimated difference in median LOS for propensity-matched cohorts varied from 3.1 days (95% CI, 2.2-3.9) to 10.1 days (95% CI, 7.3-12.2) depending on the case definition; however, dependency of the estimate on time to infection was observed. Stratification by time to first positive toxin assay, excluding probable community-acquired infections, showed a minimum excess LOS of 3.1 days (95% CI, 1.7-4.4). Under the same case definition, the multistate model averaged an excess LOS of 3.3 days (95% CI, 2.6-4.0). CONCLUSIONS In this study, 2 independent time-to-infection adjusted methods converged on similar excess LOS estimates. Changes in LOS can be extrapolated to marginal dollar costs by multiplying by average costs of an inpatient day. Infection control officers can leverage automatically extractable EMR data to estimate costs of CDI at their own institutions. Infect Control Hosp Epidemiol. 2017;38:1478-1486.

High-dimensional CyTOF analysis of dengue virus-infected human DCs reveals distinct viral signatures.

Dengue virus (DENV) is the most prevalent mosquito-borne virus causing human disease. Of the 4 DENV serotypes, epidemiological data suggest that DENV-2 secondary infections are associated with more severe disease than DENV-4 infections. Mass cytometry by time-of-flight (CyTOF) was used to dissect immune changes induced by DENV-2 and DENV-4 in human DCs, the initial targets of primary infections that likely affect infection outcomes. Strikingly, DENV-4 replication peaked earlier and promoted stronger innate immune responses, with increased expression of DC activation and migration markers and increased cytokine production, compared with DENV-2. In addition, infected DCs produced higher levels of inflammatory cytokines compared with bystander DCs, which mainly produced IFN-induced cytokines. These high-dimensional analyses during DENV-2 and DENV-4 infections revealed distinct viral signatures marked by different replication strategies and antiviral innate immune induction in DCs, which may result in different viral fitness, transmission, and pathogenesis.

Tuning molecule-surface interactions with sub-nanometer-thick covalently bound organic monolayers.

Measurements of the thermal desorption of methyl bromide (MeBr) from bare and RS-functionalized GaAs(110), where R = CH3 and CH3CH2, reveal marked systematic changes in molecule-surface interactions. As the thickness of the organic spacer layer is increased, the electrostatic MeBr-GaAs(110) interaction decreases, lowering the activation energy for desorption, Ed, as well as decreasing the critical coverage required for nucleation of bulklike MeBr. On the CH3CH2S-functionalized surface, Ed is lowered to a value roughly equal to that for desorption from three-dimensional (3-D) clusters; because the kinetics of desorption of isolated molecules differs from that for desorption from clusters, desorption of isolated molecules from the organic surface occurs at a lower temperature than desorption from the clusters. Thus, the "monolayer" desorption wave occurs at a lower temperature than the "multilayer" desorption wave. These results illustrate the role that organic chain length in nanometer-scale thin films can play in alteration of the delicate balance of interfacial interactions.