Community Onset Research Articles

1864. Trends in Staphylococcus aureus Bacteremia Rates among U.S. Acute Care Hospitals, January 2017- June 2021

Abstract Background Previous estimates of methicillin-resistant and -sensitive Staphylococcus aureus (MRSA, MSSA) bacteremia rates in hospitalized patients showed decreases in hospital-onset (HO) MRSA, no changes in community-onset (CO) MRSA and HO MSSA, and slight increases in CO MSSA rates from 2012–2017; more recent trends have not been well characterized. Methods We calculated monthly S. aureus bacteremia rates using microbiology data from hospitals reporting antibiotic susceptibility results in the Premier Healthcare Database from January 2017- June 2021. S. aureus blood isolates resistant to methicillin, oxacillin, or cefoxitin were categorized as MRSA. Positive blood cultures collected on or before hospital day 3 were categorized as CO; those collected on day 4 or later were categorized as HO. Annual rate differences were assessed using generalized estimating equation models with a negative binomial distribution adjusting for hospital bed size, teaching status, urban/rural designation, discharge month, census division, distributions of patient age, sex and race and hospital-level clustering. Results Among 11 million discharges from 356 hospitals, we identified 5,627 HO S. aureus bacteremia events: 2,558 (45%) MRSA and 3,069 (55%) MSSA. In 2017, we observed HO MRSA bacteremia rates of 0.41 and HO MSSA rates of 0.49 per 10,000 patient-days. Compared with 2017, adjusted rates of HO MRSA and MSSA bacteremia were significantly higher in 2019, 2020, and 2021 (Figure 1). Of 42,587 CO S. aureus bacteremia events, 19,107 (45%) were MRSA and 23,480 (55%) MSSA. CO MRSA bacteremia rates were lowest in 2017 (1.52 per 1,000 discharges), and adjusted rates were significantly higher in all later years (Figure 2); we observed significant regional variability in annual differences (p=0.018). The observed CO MSSA bacteremia rate was 2.03 per 1,000 discharges in 2017, and adjusted rates were significantly higher in 2020 and 2021. Figure 1.Hospital-Onset MRSA and MSSA Bacteremia Incidence Rate Ratios* with 95% confidence intervals, 2017 – 2021**.*Reference year =2017.**2021 includes data during January – June.***Statistically significant at p<0.05.Figure 2.Community-Onset MRSA and MSSA Bacteremia Incidence Rate Ratios* with 95% confidence intervals, 2017 - 2021***Reference year =2017.**2021 includes data during January – June.***Statistically significant at p<0.05. Conclusion In contrast with previous trends, recent S. aureus bacteremia rates in this cohort have increased in comparison to 2017. Potential explanations warranting exploration include differences in regional trends and pandemic-associated changes in inpatient risk, severity of illness, length of stay, and hospital utilization Disclosures All Authors: No reported disclosures.

2056. Trends in the Rates of Multidrug-resistant Bacteria Commonly Associated with Healthcare in U.S. Acute Care Hospitals, 2019-June 2021

Abstract Background Several studies demonstrated an increase in hospital-onset (HO) infections during the COVID-19 pandemic. We examined trends in the rates of multidrug-resistant (MDR) infections among acute care hospitals in the United States (U.S.) for bacteria commonly associated with healthcare: methicillin-resistant Staphylococcus aureus (MRSA), extended-spectrum cephalosporin resistance in Enterobacteriaceae suggestive of extended-spectrum beta-lactamase production (ESBL), vancomycin resistant Enterococcus (VRE), MDR Pseudomonas aeruginosa, carbapenem resistant Enterobacteriaceae (CRE), and carbapenem-resistant Acinetobacter species (CRAB). Methods We measured incidence of clinical cultures yielding the bacterial species of interest among hospitalized patients in a dynamic cohort of hospitals submitting data to the Premier Healthcare Database Special COVID-19 Release during 2019-June 2021. Community-onset (CO) cultures were obtained ≤ day 3 of hospitalization; HO were obtained ≥ day 4. We determined monthly hospital-specific rates for each species. We used generalized estimating equations (GEE) multivariable negative binomial regression models adjusting for hospital characteristics to examine trends. Results From 2019-June 2021, the overall number of hospitals contributing data was 318. Rates for each pathogen are shown in Figures 1 and 2. All pathogens’ HO rates were significantly higher in 2021 compared with 2019. The rates of ESBL, VRE, and CRE were also significantly higher in 2021 when compared to 2020 (Table 1a). For CO rates, we found that MRSA rates decreased significantly during the study period, while ESBL, VRE, and CRE increased with varying degree of significance (Table 1b). Rates of CO MDR Pseudomonas and CO CRAB did not significantly change. Conclusion Our study confirmed that the rates of several MDR infections increased during the COVID-19 pandemic through June 2021, especially HO infections. CO MRSA was the only pathogen with consistent and significant decline. As infections caused by MDR pathogens represent a serious threat to patient safety, further study of factors contributing to the emerging trends may inform prevention strategies during a pandemic. Disclosures All Authors: No reported disclosures.

2049. National Trends in Infections caused by Pseudomonas aeruginosa and Carbapenem Resistant Pseudomonas aeruginosa, 2017 – 2020

Abstract Background Pseudomonas aeruginosa is an opportunistic pathogen commonly found in the environment, including water and plumbing, which may serve as a reservoir of spread. We examined P. aeruginosa and carbapenem resistant P. aeruginosa (CRPA) rates and trends in recent years. Methods We assessed rates of P. aeruginosa using microbiology data from over 265 hospitals in the Premier Healthcare Database from January 2017 – December 2020. We identified a cohort of inpatients with any clinical culture yielding an isolate of P. aeruginosa with accompanying susceptibility testing results. Any isolate with at least 1 resistant result to imipenem, meropenem, or doripenem was considered CRPA. Cases were defined as community-onset (CO) when the culture was obtained immediately preceding admission or within the first 3 days of hospitalization, and hospital-onset (HO) when the culture was obtained on day 4 or later. Using monthly hospital level data we modeled cases per discharge, controlling for hospital characteristics, month of discharge, proportion of patients in specific age groups, and proportion of male patients. We developed weights using a raking procedure to match the American Hospital Association distribution for acute care hospitals to produce national estimates. Weighted multivariable logistic regression models were used to estimate national trends in rates per 10,000 discharges. Results Weighted rates of overall, HO, and CO P. aeruginosa and CRPA are shown in Figure 1. From 2019 – 2020, P. aeruginosa adjusted rates increased 7% (p=0.037) (Figure 2). HO P. aeruginosa rates declined 15% from 2017 – 2018 (p=0.014), however HO rates increased 29% in 2020 compared with 2019 (p< .0001). HO CRPA rates did not change during 2018 – 2019 compared with 2017, however HO rates increased 44% in 2020 compared with 2019 (p=0.001). Figure 1.Weighted Rates per 10,000 discharges, 2017 – 2020Figure 2.Pseudomonas aeruginosa and Carbapenem Resistant Pseudomonas aeruginosa Non-Linear Trend Comparisons, 2017 – 2020*Indicates statistical significance at p<0.05. Conclusion We observed an increase in the overall rate of P. aeruginosa in 2020 compared with 2019, driven by an increase in HO P. aeruginosa rates. HO CRPA rates also increased. Further evaluation of drivers of increasing HO P. aeruginosa infections is warranted, including exploration of the hypothesis that pandemic-associated changes in water use and management, such as intermittent closure and reopening of hospital units, may have increased exposure to water harboring P. aeruginosa. Disclosures All Authors: No reported disclosures.

382. Are errors of omission falsely inflating hospital-onset Clostridioides difficile infection numbers?

Abstract Background Identification of C. difficile infection (CDI) as community onset (CO) versus hospital onset (HO) is based upon the timing of the laboratory testing. Any lab diagnosis made after day 3 of hospitalization is classified as HO-CDI, even if there is clinical evidence that the patient had CDI before lab testing was sent on day 4 or later. Capturing CDI infection early in hospitalization is essential to proper diagnosis and management of CDI. Methods This is a single center retrospective study of all HO-CDI cases identified from January 2018 through January 2022. All cases were reviewed to determine if patients had loose stools during days 1-3 which were not tested, as these may have been missed opportunities to diagnose CO-CDI. We used nursing flowsheet determine if the patient had soft, loose, watery, liquid or pasty stool bowel movements (BM). Results We identified 302 unique patients diagnosed with HO-CDI during the 4 year study period. 181 (60%) were men. The mean age 57 (range 3-98 years), with increasing case numbers with increasing age groups: age 0-18 (19/6.3%), 19-39 (39/12.9%), 40-59 (73/24.2%), ≥ 60 (171/56.6%). Mean time of HO-CDI diagnosis was 12.4 days (range 4-122 days) with a skew towards early onset HO-CDI diagnosis as 86/302 (28.5%) testing positive on days 4-6. Overall, 119/302 (39.0%) of patients had identified missed opportunities for testing stools during days 1-3 of hospitalization. 38/86 (44.2%) of patients with HO-CDI diagnosed on days 4-6 had missed opportunities for testing. 40/302 (13.2%) patients expired during the index hospitalization. Conclusion Better tracking of patients' symptoms, number and consistency of BMs could improve recognition of patients presenting with CO-CDI. In turn, this would help to institute earlier isolation and treatment of these patients, as well as decreasing HO-CDI resulting from late recognition of CO-CDI which may falsely inflate HO-CDI case numbers, and/or lead to secondary hospital transmission from delayed isolation of patients with CDI. Our findings likely represent a significant under estimation of the magnitude of the errors of omission of missing opportunities for early diagnosis of CDI since it based on review of nursing flowsheets which often miss the number and consistency of patients' BMs. Disclosures Jorge Paiva Parada, MD MPH, Shionogi: Honoraria.

2174. Development and Validation of a Clinical Prediction Model for Community-Onset Pneumonia caused by Extended-Spectrum Beta-lactamase-Producing Enterobacteriaceae

Abstract Background The study aims to identify specific risk factors associated with developing pneumonia cause by ESBL producing organisms and develop a Clinical scoring model base on the risk factors identified to be able to provide guidance on appropriate empiric antibiotic therapy Methods We conducted a single-center retrospective cohort study in a tertiary hospital on Pasig City, Philippines including 150 patients admitted with Community onset Pneumonia from 2018 to 2019 Baseline Characteristics Independent variables included in the study Results Among 150 patients admitted with Community onset Pneumonia, 70.2% their respiratory isolates were K. Pneumoniae followed be E. coli at 14.57%. The Total ESBL rate among all the respiratory isolate was 45.3%. Univariate analysis of all the risk factors showed significant statistical difference in female Sex (OR 0.49; 5% CI: 0.26-0.96), Charlson Score (OR 26.00; 95% CI: 10.66-63.39), Recent Hospitalization (OR: 29.35; 95% CI: 11.53-74.46), Recent Antibiotic Use (OR 84.49; 95% CI: 28.66 – 251.87), Prior History of ESBL (OR: 57.14; 95% CI: 12.96 – 525.01), and Frequent ED visits (OR:45.88; 95% CI; 15.92 – 132.23). Multivariate analysis was done to generate the final model. The results shows that Charlson Score (OR 11.86, 95%CI: 2.55 – 55.18, p value 0.002), Recent Antibiotic Use (OR: 31.09, 95%CI: 5.36-180.44, p value < 0.001). and Frequent ED visits (OR 6.52; 95% CI 1.04-41.07) were found to be significant predictor of ESBL. The final model of the three risk factors has a performance AU ROC Curve of 0.9780 at distinguishing between the positive and negative samples. Sensitivity, Specificity, PPV, NPV and ROC area of each of the variables included in the final model Conclusion The results of this study suggest a simple and easy-to-use prediction model to predict Community-onset pneumonia caused by ESBL-producing Enterobacteriaceae. Disclosures All Authors: No reported disclosures.

496. Epidemiology, virulence and antimicrobial resistance of hypervirulent Klebsiella pneumoniae and K. pneumoniae complex member in western New York, 2017-2020

Abstract Background Klebsiella pneumoniae is a leading cause of nosocomial infections, and hypervirulent K. pneumoniae (hvKp) is an increasingly recognized community-associated pathogen. However, the epidemiology and pathogenicity of hvKp and K. pneumoniae complex infections in the US remains poorly understood. We characterized the tissue tropism, antimicrobial resistance, and virulence factors associated with K. pneumoniae complex members and hvKp using whole genome sequencing. Methods We analyzed all Klebsiella pneumoniae isolates from liver sources from 2017-2020 and isolates from other sterile tissue sites from 2018-2019 in a western New York regional academic microbiology laboratory by whole genome sequencing. Only one isolate per patient was included. Basic clinical and demographic data was obtained from the medical chart. Virulence factors and antibitoic resistance genes were identified using Kleborate version 2.0. Hypervirulence was defined as the presence of rmpA or rmpA2 and the aerobactin gene cluster (iuc). Results We sequenced 25 Klebsiella pneumoniae isolates from liver sources from 2017-2020 and an additional 29 isolates from other non-blood sterile sites from 2018-2019. 80% of the hepatic K. pneumoniae complex isolates were K. pneumoniae sensu stricto (n=20). The remaining isolates were identified as K. quasipneumoniae (n=3) and K. variicola (n=2). Non-hepatic tissue isolates contained only one K. variicola; the remainder were K. pneumoniae sensu stricto. Seven isolates met criteria for hypervirulence; all were K. pneumoniae sensu stricto isolated from liver abscesses (28% of such isolates). All were community onset infections, and only 2 patients with hvKp had prior international travel identified. None had additional sites of infection documented. Four isolates were identified as ESBL phenotypically and all had blaCTX-M-15 detected; one of which was K. quasipneumoniae. Conclusion This study suggests that hypervirulent K. pneumoniae hepatic abscesses may be more prevalent in the US than previously appreciated. Non-sensu stricto K. pneumoniae complex members were infrequently isolated from bodily tissues, less likely to carry hypervirulence genes but may harbor extended antibiotic resistance. Disclosures All Authors: No reported disclosures.

527. Antimicrobial stewardship for empirical treatment of bloodstream infection using machine learning clinical decision support

Abstract Background Undertreatment of sepsis is associated with increased mortality, but only 12 to 18% of people require broad-spectrum Gram negative or positive coverage. Unnecessary use of broad-spectrum treatments represents a modifiable driver for antimicrobial resistance (AMR). Here, we test the hypothesis that a recommendation algorithm built on ML models trained on electronic health record data can safely reduce the spectrum of activity for empiric antibiotic treatments relative to clinicians for patients with community-onset sepsis. Methods We included 29,508 people with a community-onset Adult Sepsis Event in the Mass General Brigham health system. We split the data into a training and test set by time, June 2016 to June 2020 and July 2020 to February 2021, respectively. We extracted 1,200 predictors and windowed time-varying features into non-overlapping windows. We used logistic regression, random forest and XGBoost to predict pathogen, and then predict AMR to treatments relevant to the most likely pathogen. We used dynamic thresholding to convert probabilities into AMR phenotypes and then chose the antibiotic of the narrowest spectrum among those susceptible. Outcomes were the percentage of decisions that were inappropriately narrow versus broad spectrum. We compared model performance to that of clinicians. Figure 1:Analytic pipeline Schematic of machine learning architecture, thresholding algorithm and calculation of outcomes. Results The highest performing models for pathogen prediction were XGBoost, with area under the receiver operator curve (AUROC) statistics ranging from 0.60 for Staphylococcus spp. to 0.68 for Proteus spp. Random forests performed best for AMR prediction, with AUROCs ranging from 0.59 for ampicillin-sulbactam to 0.74 for penicillin. The model utilized broad-spectrum therapy in 92.4% (92.2% - 92.7%) of patients compared to 89.3% (88.9% - 89.6%) for clinicians, a relative increase of 3.5%, but chose inappropriately narrow treatments in 7.3% (7.1% - 7.6%) of patients, compared to 9.4% (9.1% - 9.6%) for providers, a 29% reduction. Figure 2:Model performance for pathogen prediction AUROCs for prediction of pathogen groupings for patients with community-onset sepsis. All models are XGBoost. Figure 3:Model performance for AMR prediction AUROCs for prediction of antimicrobial resistance based on most likely predicted pathogens for patients with community-onset sepsis. All models are random forests. Figure 4:Post-hoc outcome analysis Comparison of rates of inappropriately narrow antibiotic treatment (IAT) and use of broad spectrum therapy for the machine learning decision algorithm versus clinicians for patients with community onset sepsis. Conclusion An ML decision support tool for antibiotic prescription in community-onset sepsis did not result in decreased broad spectrum use but was able to reduce inappropriately narrow treatment. Work is underway to refine feature sets, improve the thresholding algorithm and better account for time-varying confounders. Disclosures Sanjat Kanjilal, MD, MPH, GlaxoSmithKline: Advisor/Consultant|Roche Diagnostics: Honoraria David Sontag, PhD, Adobe: Grant/Research Support|ASAPP: Advisor/Consultant|Cureai Health: Stocks/Bonds|Facebook: Grant/Research Support|Google: Grant/Research Support|IBM: Grant/Research Support|SAP: Grant/Research Support|Takeda: Grant/Research Support.

Antimicrobial Resistance and Mortality in Hospitalized Patients with Bacteremia in the Greater Paris Area from 2016 to 2019.

Antibiotic-resistant bacteremia is a leading global cause of infectious disease morbidity and mortality. Clinical data warehouses (CDWs) allow for the secure, real-time coupling of diverse data sources from real-world clinical settings, including care-based medical-administrative data and laboratory-based microbiological data. The main purpose of this study was to assess the contribution of CDWs in the epidemiological study of antibiotic resistance by constructing a database of bacteremia patients, BactHub, and describing their main clinico-microbiological features and outcomes. Adult patients with bacteremia hospitalized between January 1, 2016 and December 31, 2019 in 14 acute care university hospitals from the Greater Paris area were identified; their first bacteremia episode was included. Data describing patients, episodes of bacteremia, bacterial isolates, and antimicrobial resistance were structured. Among 29,228 patients with bacteremia, 41% of episodes were community-onset (CO) and 59% were hospital-acquired (HA). Thirty-day and ninety-day mortality rates were 15% and 20% in CO episodes, and 18% and 36% in HA episodes. Overall resistance rates were high, including third-generation cephalosporin resistance among Klebsiella pneumoniae (CO 21%, HA 37%) and Escherichia coli (CO 13%, HA 17%), and methicillin resistance among Staphylococcus aureus (CO 11%, HA 14%). Annual incidence rates increased significantly from 2017 to 2019, from 20.0 to 20.9 to 22.1 stays with bacteremia per 1000 stays (p < 0.0001). The Bacthub database provides accurate clinico-microbiological data describing bacteremia across France's largest hospital group. Data from Bacthub may inform surveillance and the clinical decision-making process for bacteremia patients, including choice of antimicrobial therapy. The database also offers opportunities for research, including analysis of hospital care pathways and significant patient outcomes such as mortality and recurrence of infection.

Community-onset Clostridioides difficile infection in south Serbia

BackgroundData from the past decade indicates that Clostridioides difficile infection (CDI) is not only a nosocomial infection but is also increasingly recognized as a disease in the community. ObjectiveWe aimed to study community-onset (CO) CDI in the various age groups in south Serbia with its clinical characteristics, risk factors and microbiological characterization. MethodsThe study group included 93 patients with CO-CDI (median age 62). The control group consisted of 186 patients with community-onset diarrhea and stool samples negative tested for CDI. ResultsOf all CDI cases diagnosed with a community onset, 74.19% had a previous contact with a healthcare facility in the previous 12 weeks, but 34.40% have no record on hospitalization in the previous 12 months. Using a multivariate statistical regression model, the following risk factors for CO-CDI development were found; antacid usage (OR = 0.267, 95%C.I.:0.10–0.291, p < 0.01), chronic kidney disease (OR = 0.234, 95%C.I.:0.10–0.51, p < 0.01) and antibiotic use during the prior 2 months (OR = 0.061, 95%C.I.:0.02–0.17, p < 0.01), especially tetracycline's (OR = 0.146, 95% C.I.:0.07–0.22, p < 0.01) and cephalosporin's (OR = 0.110, 95%C.I.:0.14–0.42, p < 0.01). The most common ribotypes (RTs) detected in patients with CO-CDI were RT001 (32.3%) and RT027 (24.7%). All tested toxin producing C. difficile isolates were sensitive to metronidazole, vancomycin and tigecycline. A high rate of resistance to moxifloxacin (73.11%) and rifampicin (23.65%) was found. ConclusionPatients with CO-CDI had frequently contact with healthcare facility in the previous 12 weeks. Restriction of antacid usage and of high-risk antibiotics in the community may help reduce the incidence of CO-CDI.

Effect of temperature on Escherichia coli bloodstream infection in a nationwide population-based study of incidence and resistance

BackgroundThe incidence of Escherichia coli bloodstream infections (BSI) is high and increasing. We aimed to describe the effect of season and temperature on the incidence of E. coli BSI and antibiotic-resistant E. coli BSI and to determine differences by place of BSI onset.MethodsAll E. coli BSI in adult Israeli residents between January 1, 2018 and December 19, 2019 were included. We used the national database of mandatory BSI reports and outdoor temperature data. Monthly incidence and resistance were studied using multivariable negative binomial regressions with season (July–October vs. other) and temperature as covariates.ResultsWe included 10,583 events, 9012 (85%) community onset (CO) and 1571 (15%) hospital onset (HO). For CO events, for each average monthly temperature increase of 5.5 °C, the monthly number of events increased by 6.2% (95% CI 1.6–11.1%, p = 0.008) and the monthly number of multidrug-resistant events increased by 4.9% (95% CI 0.3–9.7%, p = 0.04). The effect of season was not significant. For HO events, incidence of BSI and resistant BSI were not associated with temperature or season.ConclusionTemperature increases the incidence of CO E. coli BSI and CO antibiotic-resistant E. coli BSI. Global warming threatens to increase the incidence of E. coli BSI.

Prevalence and Impact of Co-infection in Critically Ill COVID-19 Patients

Objective: To determine the epidemiology and impact of co-infection on COVID-19 pneumonia in critical care setting. Methods: This was a single center- retrospective study of COVID-19 patients, who were admitted to intensive care unit between March 2020-April 2021. Only patients with microbiological evaluation of blood, respiratory, or urine culture data were included. Community onset bacterial co-infection (COI) was defined as infection diagnosed within 48 hours and hospital acquired bacterial infection (HAI) after 48 hours of hospital admission. Both COI and HAI were further categorized into respiratory, blood or urinary sources. Demographics, laboratory and hospitalization outcomes were compared between patients with and without COI and HAI. Results: Among the 141 patients, COI was found in 25/132 patients (19%) and HAI in 41/109 patients (38%). COI was associated with younger age, lower body mass index (BMI) and being from nursing or long- term care facility. HAI was associated with longer length of hospital stay (29 vs 15 days). Staphylococcus aureus pneumonia (31.3%) and Pseudomonas aeruginosa pneumonia (40.0%) were the most common cause of COI and HAI respectively. Overall, gram negative bacteria predominated in both community and hospital acquired pneumonia. Candida was the single most common cause of blood stream infection in HAI (23%). The hospital survival was not significantly different with or without COI. Conclusion: In the critical care setting, a significant proportion of patients with COVID-19 had COI and HAI, especially patients from nursing or long-term care facility. Antibiotic coverage for COI and HAI pneumonia should account for the increase in prevalence of Staphylococcus aureus and Pseudomonas aeruginosa respectively.

Microbial Interdomain Interactions Delineate the Disruptive Intestinal Homeostasis in Clostridioides difficile Infection.

ABSTRACTClostridioides difficile infection (CDI) creates an imbalance in the intestinal microbiota due to the interaction of the components making up this ecosystem, but little is known about the impact of this disease on other microbial members. This work has thus been aimed at evaluating the taxonomic composition, potential gene-associated functions, virulence factors, and antimicrobial resistance profiles of gut microbiomes. A total of 48 DNA samples obtained from patients with health care facility-acquired (HCFO) and community-onset (CO) diarrhea were distributed in the following four groups according to CDI status: HCFO/+ (n = 13), HCFO/– (n = 8), CO/+ (n = 13), and CO/– (n = 14). These samples were subjected to shotgun metagenomics sequencing. Although the CDI groups’ microbiota had microbiome alterations, the greatest imbalance was observed in the in the HCFO+/– groups, with an increase in common pathogens and phage populations, as well as a decrease in beneficial microorganisms that leads to a negative impact on some intestinal homeostasis-related metabolic processes. A reduction in the relative abundance of butyrate metabolism-associated genes was also detected in the HCFO groups (P < 0.01), with an increase in some virulence factors and antibiotic-resistance markers. A set of 51 differentially abundant species in the groups with potential association to CDI enabled its characterization, leading to their spatial separation by onset. Strong correlations between phages and some archaeal and bacterial phyla were identified. This highlighted the need to study the microbiota’s various components since their imbalance is multifactorial, with some pathogens contributing to a greater or lesser extent because of their interaction with the ecosystem they inhabit.IMPORTANCE Clostridioides difficile infection represents a serious public health problem in different countries due to its high morbi-mortality and the high costs it represents for health care systems. Studies have shown the impact of this infection on intestinal microbiome homeostasis, mainly on bacterial populations. Our research provides evidence of the impact of CDI at both the compositional (bacteria, archaea, and viruses), and functional levels, allowing us to understand that the alterations of the microbiota occur systemically and are caused by multiple perturbations generated by different members of the microbiota as well as by some pathogens that take advantage of the imbalance to proliferate. Likewise, the 51 differentially abundant species in the study groups with potential association to CDI found in this study could help us envisage future treatments against this and other inflammatory diseases, improving future therapeutic options for patients.

Hospital-onset bacteremia and fungemia: An evaluation of predictors and feasibility of benchmarking comparing two risk-adjusted models among 267 hospitals.

To evaluate the prevalence of hospital-onset bacteremia and fungemia (HOB), identify hospital-level predictors, and to evaluate the feasibility of an HOB metric. We analyzed 9,202,650 admissions from 267 hospitals during 2015-2020. An HOB event was defined as the first positive blood-culture pathogen on day 3 of admission or later. We used the generalized linear model method via negative binomial regression to identify variables and risk markers for HOB. Standardized infection ratios (SIRs) were calculated based on 2 risk-adjusted models: a simple model using descriptive variables and a complex model using descriptive variables plus additional measures of blood-culture testing practices. Performance of each model was compared against the unadjusted rate of HOB. Overall median rate of HOB per 100 admissions was 0.124 (interquartile range, 0.00-0.22). Facility-level predictors included bed size, sex, ICU admissions, community-onset (CO) blood culture testing intensity, and hospital-onset (HO) testing intensity, and prevalence (all P < .001). In the complex model, CO bacteremia prevalence, HO testing intensity, and HO testing prevalence were the predictors most associated with HOB. The complex model demonstrated better model performance; 55% of hospitals that ranked in the highest quartile based on their raw rate shifted to a lower quartile when the SIR from the complex model was applied. Hospital descriptors, aggregate patient characteristics, community bacteremia and/or fungemia burden, and clinical blood-culture testing practices influence rates of HOB. Benchmarking an HOB metric is feasible and should endeavor to include both facility and clinical variables.

Investigation and Mitigation of a Respiratory Syncytial Virus (RSV) Outbreak on an Adult Inpatient Leukemia Unit

<h3>Background</h3> In February 2020, an uptick in RSV infections was detected on an adult, inpatient leukemia unit at a tertiary oncology hospital. Upon outbreak identification, an epidemiological investigation was launched, and outbreak mitigation responses implemented. <h3>Methods</h3> All new RSV results were reviewed for connections to the uptick detected on the inpatient adult leukemia unit which houses patients on high-risk services. Upon identification of an outbreak, the Infection Control Department met with interdisciplinary stakeholders to implement a stepwise approach to stop the outbreak. The infection control team provided education to staff, implemented enhanced twice daily unit cleaning, performed daily unit rounds, monitored sick staff calls and performed daily patient chart reviews for symptoms. As additional cases were identified in roommates of index patients, further contact tracing and surveillance testing was performed. In response to continued detection of cases, all patients were placed on droplet precautions, the unit closed to new admissions, patients and healthcare staff were cohorted, employee surveillance testing was performed, and a physical containment barrier created. <h3>Results</h3> The Infection Control Department monitored outbreak activity and implemented mitigation responses between February 1, 2020, to March 24, 2020. There were 23 patient cases associated with this outbreak, 18 of which were hospital-acquired and 5 of which were community onset. 22 patients associated with the outbreak were on a high-risk service. There were 7 cases detected in healthcare workers. The outbreak was declared over when there were no cases within 2 incubation periods, 14 days. The unit subsequently returned to routine practices. <h3>Conclusions</h3> This outbreak investigation indicates that high-risk inpatient units require a multitude of interventions. Surveillance testing and quarantine are essential in this population, and mitigation efforts should be enacted as quickly as possible to stop transmission.

Clostridioides difficile infection in a rural New Zealand secondary care centre: an incidence case-control study.

Clostridioides difficile infection (CDI) is a form of antibiotic-associated infectious diarrhoea resulting in significant morbidity and mortality. Community-acquired disease in low-risk individuals is increasingly recognised. There are limited New Zealand data published. To determine the incidence and location of onset of CDI cases in the Manawatu region, and further describe the demographics, risk factors and prevalent C. difficile ribotypes of the population. We performed an incidence case-control study of CDI in the Manawatu region between September 2018 and September 2019. Cases were matched to controls with a negative test for C. difficile. Demographic and comorbidity data, location of onset, drug exposure, disease recurrence and 30-day mortality were collected. Ribotype analysis was performed on C. difficile isolates. Thirty-two specimens tested toxin positive over 12 months, yielding an incidence of 18.3 cases per 100 000 person-years. Twenty-five percent of cases had community onset disease. Cases were more likely to have had amoxicillin/clavulanate or ceftriaxone prescribed. Elevated blood white cell count and lower HbA1c were significantly associated with CDI. The dominant ribotype was 014/020. Two cases were RT 023. Our data are similar to previous national data. RT 023 has not been previously reported in New Zealand and has been associated with severe colitis. We demonstrated a significant proportion of community-acquired cases and the true incidence might be higher. Vigilance for community onset disease is required. These data may allow observation of temporal changes in incidence and infection patterns of CDI in New Zealand.

Evaluating the performance characteristics of five lateral flow assays for the detection of the SARS-CoV-2 nucleocapsid antigen

In response to the COVID-19 pandemic, lateral flow assays (LFAs) for the detection of SARS-CoV-2 antigen have been proposed as a complementary option to the more costly and time consuming reverse-transcriptase polymerase chain reaction (RT-PCR). We assessed five commercially available SARS-CoV-2 antigen detecting LFAs (ASSUT EUROPE (Rome, Italy), Besthree (Taizhou, China), Encode (Zhuhai, China), Fortress (Antrim UK), and Hughes Medical (Buckinghamshire, UK), using samples collected from hospitalised individuals with COVID-19 and compared these results against established RT-PCR assays with the aim of estimating test performance characteristics. We performed a diagnostic accuracy study of the five LFAs on 110 inpatients with confirmed COVID-19 and 75 COVID-19 negative control participants. Assay evaluation was performed using a modified version of each manufacturer’s protocol allowing for parallel testing of a single sample on multiple assays. Additional variables were studied including infection acquisition, oxygenation requirements at time of swabbing, and patient outcomes. The 110 patients were 48% (53) female, with mean age 67 years (range 26–100 years), and 77% (85) cases were community onset SARS-CoV-2. Across the five assays, sensitivity ranged from 64 (95% CI 53–73) to 76% (95% CI 65–85); Fortress performed best with sensitivity of 76% (95% CI 65–85). Specificity was high across all assays with 4/5 LFAs achieving 100%. LFA sensitivity was not dependant on RT-PCR cycle thresholds. SARS-CoV-2 antigen detecting LFAs may complement RT-PCR testing to facilitate early diagnosis and provide community testing strategies for identification of patients with COVID-19, however we find suboptimal test performance characteristics across a range of commercially available manufacturers, below WHO and MHRA pre-set sensitivity performance thresholds. With such variation in sensitivity between LFAs and PCR testing and between assay brands, we advise caution in the deployment of LFAs outside of environments with clinical oversight.

Ceftriaxone resistance and adequacy of initial antibiotic therapy in community onset bacterial pneumonia.

Much remains unknown about the impact of initial antibiotic adequacy on mortality in community onset bacterial pneumonia (COBP). Therefore, we performed a study to determine how the adequacy of initial antibiotic therapy affects in-hospital mortality for patients with COBP.We carried out a retrospective cohort study among the 11 BJC Healthcare community and academic hospitals in Missouri and Illinois. The electronic medical records for BJC Healthcare were queried to obtain a set of patient admissions with culture positive (respiratory or blood) COBP admitted from January 1, 2016 through December 31, 2019. Patients with COBP required an International Classification of Diseases (ICD)-10 diagnostic code for pneumonia, admission to the hospital through an emergency department, a chest radiograph with an infiltrate, an abnormal white blood cell count or temperature, an order for 1 or more new antibiotics, and a positive respiratory or blood culture. Antibiotic selection was deemed adequate if the patient had organisms susceptible to at least one of the antibiotics received according to in vitro testing using standard laboratory breakpoints.Among 36,645 screened pneumonia admissions, 1843 met criteria for culture positive COBP. Eight hundred nineteen (44.4%) had ceftriaxone-resistant (CTX-R) organisms and 1024 had ceftriaxone-sensitive (CTX-S) organisms. The most common CTX-R pathogens were methicillin resistant Staphylococcus aureus (46.9%), Pseudomonas species (38.4%), and Escherichia coli (4.5%). On the day of admission 71% of all patients were given adequate antibiotic treatment (62.2% of CTX-R and 77.9% of CTX-S). Unnecessarily broad initial treatment was administered to 57.1% of CTX-S patients. In a logistic regression model accounting for comorbidities and severity of illness, inadequate therapy on the day of admission was associated with higher in-hospital mortality (P = .005). Among CTX-S patients who were adequately treated, initial use of unnecessarily broad antibiotics was associated with increased in-hospital mortality (P = .003).Ceftriaxone resistance was common in this cohort of culture positive COBP patients. Inappropriate coverage on day of admission was associated with greater likelihood of in-hospital mortality.

Evaluation of the genomic epidemiology and transmission of Clostridioides difficile infection across a community

Background:Clostridioides difficile infection (CDI) is a major cause of morbidity and healthcare costs in the United States. The epidemiology of CDI has recently shifted, with healthcare-associated (HCA) CDI trending downward and community-associated (CA)-CDI becoming more prominent. The cause of this shift is not well understood but may be related to changing genomic epidemiology. We assessed C. difficile strains across a CDC Emerging Infections Program (EIP) site in Western New York, including strains from both HCA-CDI and CA-CDI cases to characterize predominating strains and putative transmission across epidemiological classifications and between index and recurrent cases. Methods: In total, 535 isolates of C. difficile were collected over a 6-month period in 2018 from the Monroe Country, New York, EIP site and were analyzed using whole-genome sequencing (WGS). Standard epidemiological definitions were used to classify cases as hospital onset (HO-CDI); community associated (CA-CDI); community onset, healthcare associated (CO-HCFA-CDI); or long-term care onset (LTCO-CDI). Recurrent cases were defined as those diagnosed within 8 weeks of an initial positive test. Multilocus sequence types (MLSTs) were assigned according to PUBMLST and single-nucleotide polymorphisms (SNPs) were determined using a modified CFSAN analytical pipeline. Cases resulting from putative transmission were defined as those separated by 0–1 core SNPs. Results: Of 535 isolates, 454 were from index and 81 were from recurrent cases. The index cases were comprised of CA-CDI (47.4%), CO-HCFA-CDI (24%), LTCO-CDI (8.1%), and HO-CDI (19.3%). Cases with recurrent disease mirrored the epidemiological distribution of the larger set. Common MLSTs included ST2 (12.3%), ST8 (10.5%), ST42 (7.9%), ST58 (4.9%), ST43 (4.5%), and ST11 (4.3%). The previously widespread epidemic strain, NAP1/ST1/RT027 accounted for Conclusions: The genomic epidemiology of C. difficile across this large community cohort demonstrated a diverse group of strain types that was similarly distributed across epidemiological classifications and between index and recurrent cases. SNP analysis indicated that direct transmission between cases was uncommon.Funding: NoneDisclosures: None

Rapid identification and isolation of patients with COVID-19 reduces the odds of transmission to hospital roommates

Background: The Cleveland Clinic Main Campus is a multispecialty academic medical center with 1,200 adult patient beds, 58% of which are double occupancy. Our facility relies on double-occupancy rooms to provide needed care during the COVID-19 pandemic. Inherently, double occupancy poses a greater risk of exposure to SARS-CoV-2 despite mitigation efforts. We investigated the incidence of postexposure SARS-CoV-2 infection in double-occupancy rooms and evaluated risk factors for viral transmission. Methods: Early in the observation period patients were tested for SARS-CoV-2 based on clinical suspicion. By June 2020, all admitted patients were tested. Symptomatic patients were admitted with pending tests under transmission-based precautions. Asymptomatic patients were managed with standard precautions including patients admitted to double-occupancy rooms. A double-occupancy exposure event was defined as an uninfected patient sharing a room with a patient positive for SARS-CoV-2. All patient exposures were tracked and evaluated by the infection prevention (IP) team. The IP prospective review of source patients included determination of lowest cycle threshold (Ct) value of first COVID-19 test, and whether their infection was hospital or community onset. Review of exposed patients included sex, age, and exposure time (in hours) to the source patient. Postexposure infection was defined as a positive test for SARS-CoV-2 in the exposed population within 14 days of the defined exposure event. We fit a multivariable logistic regression model to estimate the effect of exposure time on the odds of postexposure infection in susceptible roommates. Results: From March 15 to December 15, 2020, 172 susceptible patients were exposed to a roommate with COVID-19. Also, 28 exposed patients met our definition for postexposure infection (attack rate, 16%). The frequency of postexposure infection was higher in patients for whom the source was hospital-onset versus community-onset disease (25% vs 10%; P = .01) and when the source patient’s Ct value was below the median value of 21.1 (26% vs 11% p Conclusions: We identified a postexposure infection attack rate of 16% for double-occupancy patients in the first 9 months of the pandemic. Time exposed to source patient was significantly associated with infection. Our experience demonstrates the potential benefit of asymptomatic admission testing with expedited turnaround time to mitigate viral transmission between patients in double-occupancy rooms.Funding: NoneDisclosures: None

Developing national benchmarks for antimicrobial resistance–NHSN, 2019

Background: The emergence and spread of drug-resistant pathogens continues to significantly impact patient safety and healthcare systems. Although antimicrobial susceptibility test (AST) results of clinical specimens are used by individual facilities for antimicrobial resistance surveillance, accurate tracking and benchmark comparison of a facility’s antimicrobial resistance using national data requires risk-adjusted methods to be more meaningful. The CDC NHSN Antimicrobial Resistance (AR) Option collects patient-level, deduplicated, isolate information, including AST results, for &gt;20 organisms from cerebrospinal fluid, lower respiratory tract (LRT), blood, and urinary specimens. To provide risk-adjusted national benchmarks, we developed prediction models for incidence of hospital-onset isolates with antimicrobial resistance. Methods: We analyzed AST results of isolates reported through the NHSN AR Option for January through December 2019. Isolates from facilities that had &gt;10% missing AST results for the organism-drug combinations or from hospitals that used outdated breakpoints were excluded. We assessed associations between facility-level factors and incidence rates of hospital-onset (specimen collected 3 days or more after hospital admission) isolates of specific drug-resistant phenotypes from blood, LRT, and urinary specimens. Factors included number of beds, length of stay, and prevalence of community onset isolates of the same phenotype. Drug-resistant phenotypes assessed included methicillin-resistant Staphylococcus aureus (MRSA), multidrug-resistant (MDR) Pseudomonas aeruginosa, carbapenem-resistant Enterobacterales (CRE), fluoroquinolone-resistant Pseudomonas aeruginosa, fluoroquinolone-resistant Enterobacterales, and extended-spectrum cephalosporin-resistant Enterobacterales. Isolates of different phenotypes and from different specimen sources were modeled separately. Negative binomial regression was used to evaluate the factors associated with antimicrobial resistance incidence. Variable entry into the models is based on significance level P Among the models, 1 for each drug-resistant phenotype-specimen type combination, the number of isolates with AST results ranged from 718 (Pseudomonas aeruginosa–fluoroquinolones, blood) to 16,412 (Enterobacterales–fluoroquinolones, urine). The pooled incidence rate was highest for fluoroquinolone-resistant Enterobacterales in urinary specimens (0.2179 isolates per 1,000 patient days) among all phenotype-specimen combinations evaluated (Table 1). The incidence of drug-resistant isolates was consistently associated with community-onset prevalence across models evaluated. Other associated factors varied across phenotype-specimen combinations (Table 2). Conclusions: We developed statistical models to predict facility-level incidence rates of hospital-onset antimicrobial resistant isolates based on community-onset drug-resistant prevalence and facility characteristics. These models will enable facilities to compare antimicrobial resistance rates to the national benchmarks and therefore to inform their antimicrobial stewardship and infection prevention efforts.Funding: NoneDisclosures: None