Hospital-onset Cases Research Articles

Association between hospital-onset SARS-CoV-2 and ending universal admission testing and masking at five US hospitals

Background: Many US hospitals have stopped universal masking and testing all patients on admission for SARS-CoV-2. We assessed the association of ending universal masking and admission testing with the incidence of hospital-onset SARS-CoV-2 infections in five Massachusetts hospitals. Method: We conducted a retrospective study of all patients admitted between March 6, 2020 and December 14, 2023 and identified hospital-onset SARS-CoV-2 infections (newly positive SARS-CoV-2 PCR tests >4d after arrival) and community-onset infections (newly positive ≤4d after arrival). We excluded cases if local infection control teams discontinued precautions within 4d (suggesting a false positive or remote/resolved infection). We calculated weekly ratios between hospital-onset and community-onset SARS-CoV-2 cases to account for changes in community SARS-CoV-2 incidence over time. We then performed interrupted time series analysis, looking for changes in the ratio of hospital-onset to community-onset cases across three periods: pre-Omicron period with universal testing and masking in place (March 6, 2020–Dec 16, 2021); Omicron period with universal testing and masking in place (Dec 17, 2021–May 11, 2023); and Omicron period without universal testing and masking (May 12, 2023–Dec 14, 2023). We performed medical record reviews on 100 randomly selected hospital-onset cases after May 12, 2023 to examine if community-onset cases were being misclassified as hospital-onset cases. Result: During the study period, there were 626,908 patient admissions, including 24,980 with community-onset and 1,510 with hospital-onset SARS-CoV-2 infections. The mean weekly ratio of new hospital-onset to community-onset SARS-CoV-2 infections rose from 2.6% before Omicron, to 8.5% (95% CI, 7.0–9.9%) during Omicron, to 17% (95% CI, 15–19%) after universal admission testing and masking ended (Figure 1). There was a significant immediate level change after the pre-Omicron-to-Omicron transition (140% relative increase; 95% CI, 40–240%) and after universal admission testing and masking ended (110% relative increase; 95% CI, 73–150%). On medical record review of 100 randomly selected hospital-onset SARS-CoV-2 cases after universal admission testing had ended, 89% had new symptoms at the time of testing, 80% had PCR cycle thresholds ≤30, 27% had a known COVID-19 exposure, and 97% met at least one of these criteria. In-hospital mortality occurred in 8% of the 100 reviewed cases. Conclusion: Stopping universal masking and admission testing of all hospitalized patients at five Massachusetts hospitals was associated with a significant increase in hospital-onset COVID-19. Nosocomial COVID-19 remains a common complication of hospital care. Preventing nosocomial infections in this vulnerable population remains an important safety goal.Disclosure: Theodore Pak: Founder/CEO - The East Harlem Software Company, Inc.

Population and hospital-level COVID-19 measures are associated with increased risk of hospital-onset COVID-19.

A review of hospital-onset COVID-19 cases revealed 8 definite, 106 probable, and 46 possible cases. Correlations between hospital-onset cases and both HCW and inpatient cases were noted in 2021. Rises in community measures were associated with rises in hospital-onset cases. Measures of community COVID-19 activity might predict hospital-onset cases.

14-Year Epidemiologic study of Pseudomonas aeruginosa bloodstream infection incidence and resistance in the Veterans Health Administration system, 2009-2022.

Multidrug resistant Pseudomonas aeruginosa (PA) represents a serious threat to hospitalized patients. Characterizing the incidence of PA infection and degree of resistance can inform empiric treatment and preventative measures. We sought to describe trends in incidence and resistance characteristics of PA bloodstream infections (BSI) observed within the Veterans Health Administration (VHA) system and identify factors contributing to higher observed mortality within this population. We characterized demographic and clinical features of unique patients among the VHA population presenting with their first episode of PA-BSI between 2009 and 2022 and summarized trends related to mortality and resistance phenotype based on year and geographical location. We additionally used logistic regression analysis to identify predictors of 30-day mortality among this cohort. We identified 8039 PA-BSIs during the study period, 32.7% of which were hospital onset. Annual PA-BSI cases decreased by 35.8%, and resistance among all antimicrobial classes decreased during the study period, while the proportion of patients receiving early active treatment based on susceptibility testing results increased. Average 30-day mortality rate was 23.3%. Higher Charlson Comorbidity Index, higher mAPACHE score, VHA facility complexity 1b and hospital-onset cases were associated with higher mortality, and early active treatment was associated with lower mortality. PA-BSI resistance decreased across the VHA system during the study period. Further investigation of antimicrobial stewardship measures possibly contributing to the observed decreased resistance in this cohort and identification of measures to improve on the high mortality associated with PA-BSI in the VHA population is warranted.

Severity of influenza-associated hospitalisations by influenza virus type and subtype in the USA, 2010–19: a repeated cross-sectional study

Influenza burden varies across seasons, partly due to differences in circulating influenza virus types or subtypes. Using data from the US population-based surveillance system, Influenza Hospitalization Surveillance Network (FluSurv-NET), we aimed to assess the severity of influenza-associated outcomes in individuals hospitalised with laboratory-confirmed influenza virus infections during the 2010-11 to 2018-19 influenza seasons. To evaluate the association between influenza virus type or subtype causing the infection (influenza A H3N2, A H1N1pdm09, and B viruses) and in-hospital severity outcomes (intensive care unit [ICU] admission, use of mechanical ventilation or extracorporeal membrane oxygenation [ECMO], and death), we used FluSurv-NET to capture data for laboratory-confirmed influenza-associated hospitalisations from the 2010-11 to 2018-19 influenza seasons for individuals of all ages living in select counties in 13 US states. All individuals had to have an influenza virus test within 14 days before or during their hospital stay and an admission date between Oct 1 and April 30 of an influenza season. Exclusion criteria were individuals who did not have a complete chart review; cases from sites that contributed data for three or fewer seasons; hospital-onset cases; cases with unidentified influenza type; cases of multiple influenza virus type or subtype co-infection; or individuals younger than 6 months and ineligible for the influenza vaccine. Logistic regression models adjusted for influenza season, influenza vaccination status, age, and FluSurv-NET site compared odds of in-hospital severity by virus type or subtype. When missing, influenza A subtypes were imputed using chained equations of known subtypes by season. Data for 122 941 individuals hospitalised with influenza were captured in FluSurv-NET from the 2010-11 to 2018-19 seasons; after exclusions were applied, 107 941 individuals remained and underwent influenza A virus imputation when missing A subtype (43·4%). After imputation, data for 104 969 remained and were included in the final analytic sample. Averaging across imputed datasets, 57·7% (weighted percentage) had influenza A H3N2, 24·6% had influenza A H1N1pdm09, and 17·7% had influenza B virus infections; 16·7% required ICU admission, 6·5% received mechanical ventilation or ECMO, and 3·0% died (95% CIs had a range of less than 0·1% and are not displayed). Individuals with A H1N1pdm09 had higher odds of in-hospital severe outcomes than those with A H3N2: adjusted odds ratios (ORs) for A H1N1pdm09 versus A H3N2 were 1·42 (95% CI 1·32-1·52) for ICU admission; 1·79 (1·60-2·00) for mechanical ventilation or ECMO use; and 1·25 (1·07-1·46) for death. The adjusted ORs for individuals infected with influenza B versus influenza A H3N2 were 1·06 (95% CI 1·01-1·12) for ICU admission, 1·14 (1·05-1·24) for mechanical ventilation or ECMO use, and 1·18 (1·07-1·31) for death. Despite a higher burden of hospitalisations with influenza A H3N2, we found an increased likelihood of in-hospital severe outcomes in individuals hospitalised with influenza A H1N1pdm09 or influenza B virus. Thus, it is important for individuals to receive an annual influenza vaccine and for health-care providers to provide early antiviral treatment for patients with suspected influenza who are at increased risk of severe outcomes, not only when there is high influenza A H3N2 virus circulation but also when influenza A H1N1pdm09 and influenza B viruses are circulating. The US Centers for Disease Control and Prevention.

Association between stopping universal SARS-CoV-2 admission testing and hospital-onset SARS-CoV-2 in England and Scotland

Background: Many hospitals test all patients for SARS-CoV-2 upon admission to prevent silent transmission to other patients and healthcare workers. The utility of universal admission testing has been questioned, however, due to resource constraints, care delays, and sparse data on its impact on nosocomial infections. England and Scotland stopped requiring universal admission testing on August 31, 2022, and September 28, 2022, respectively. We assessed associations between these changes and hospital-onset SARS-CoV-2 infection rates. Methods: We used public data from National Health Service England and Public Health Scotland on hospital-onset SARS-CoV-2 infections, defined as cases diagnosed >7 days after admission, between July 1, 2021, and December 16, 2022. Because hospital-onset infections are driven by SARS-CoV-2 community incidence rates, we calculated the weekly ratio between hospital-onset versus community-onset SARS-CoV-2 admissions (diagnosed ≤7 days from admission) and assessed for temporal changes associated with stopping universal admission testing using interrupted time-series analysis. The study was divided into 3 periods: sARS-CoV-2 delta-variant dominance with admission testing, SARS-CoV-2 omicron-variant dominance with admission testing (starting December 14, 2021), and SARS-CoV-2 omicron-variant dominance without admission testing. Results: During the study period, there were 518,379 COVID-19 admissions in England, including 398,264 community-onset and 120,115 hospital-onset cases, and 46,517 COVID-19 admissions in Scotland, including 34,183 community-onset and 12,334 hospital-onset cases. The mean weekly ratio of new hospital-onset SARS-CoV-2 infections versus community-onset admissions in England rose from 0.12 during the SARS-CoV-2 delta-variant surge to 0.33 during the SARS-CoV-2 omicron-variant surge to 0.48 after universal admission testing ended (Fig.). There was a significant immediate level change both after the SARS-CoV-2 delta-to-omicron variant transition (92% relative increase; 95% CI, 58%–127%) and after admission testing ended (32% relative increase; 95% CI, 14%–50%). Likewise, the mean weekly ratios rose from 0.11 to 0.43 to 0.89 during their analogous periods in Scotland, with significant level changes both after SARS-CoV-2 delta-to-omicron variant transition (113% relative increase; 95% CI, 54%–172%) and after admission testing ended (72% relative increase; 95% CI, 43%–100%). No significant trend changes were observed. Conclusions: Stopping asymptomatic screening of hospitalized patients in 2 national health systems was associated with significant increases in hospital-onset SARS-CoV-2 infections. Nosocomial SARS-CoV-2 remains a common and potentially morbid complication, with reported mortality rates for nosocomial infections by SARS-CoV-2 omicron variant ranging from 5% to 13%. Preventing infections in vulnerable populations remains an important safety goal. Hospitals should exercise caution when considering reductions in SARS-CoV-2 admission screening.Disclosures: None

Hospital-onset COVID-19: Associations with population- and hospital-level measures to guide infection prevention efforts

Objective: To characterize hospital-onset COVID-19 cases and to investigate the associations between these rates and population and hospital-level rates including trends in healthcare worker infections (HCW), community cases, and COVID-19 wastewater data. Design: Retrospective cohort study from January 1, 2021, to November 23, 2022. Setting: This study was conducted at a 589-bed urban Midwestern tertiary-care hospital system. Participants and interventions: The infection prevention team reviewed the electronic medical records (EMR) of patients who were admitted for >48 hours and subsequently tested positive for SARS-CoV-2 to determine whether COVID-19 was likely to be hospital-onset illness. Each case was further categorized as definite, probable, or possible based on viral sequencing, caregiver tracing analysis, symptoms, and cycle threshold values. Patients were excluded if there was a known exposure prior to admission. Clinical data including vaccination status were collected from the EMR. HCW case data were collected via our institution’s employee health services. Community cases and wastewater data were collected via the Wisconsin Department of Health Services database. Additionally, we evaluated the timing of changes in infection prevention guidance such as visitor restrictions. Results: In total, 156 patients met criteria for hospital-onset COVID-19. Overall, 6% of cases were categorized as definite, 24% were probable, and 70% were possible hospital-onset illness. Most patients were tested prior to a procedure (31%), for new symptoms (30%), and for discharge planning (30%). Also, 53% were symptomatic and 41% received treatment for their COVID-19. Overall, 38% of patients were immunocompromised and 27% were unvaccinated. Overall, 12% of patients died within 1 month of their positive SARS-CoV-2 test, and 11% required ICU admission during their hospital stay. Hospital-onset COVID-19 increased in fall of 2022. Specifically, October 2022 had 16 cases, whereas fall of 2021 (September–November) only had 3 cases total. Finally, similar peaks were observed in total cases by week between healthcare workers, county cases, and COVID-19 wastewater levels. These peaks correspond with the SARS-CoV-2 delta and omicron variant surges, respectively. Conclusions: Hospital-onset cases followed similar trends as population and hospital-level data throughout the study period. However, hospital-onset rate did not correlate as strongly in the second half of 2022 when cases were disproportionately high. Given that hospital-onset cases can result in significant morbidity, continued enhanced infection prevention efforts and low threshold for testing are warranted in the inpatient environment.Disclosure: None

Trends in and Risk Factors for Recurrent Clostridioides difficile Infection, New Haven County, Connecticut, USA, 2015-2020.

Recurrent Clostridioides difficile infection (RCDI) causes an increased burden on the healthcare system. We calculated RCDI incidence and identified factors associated with RCDI cases in New Haven County, Connecticut, USA, during 2015-2020 by using data from population-based laboratory surveillance. A subset of C. difficile cases had complete chart reviews conducted for RCDI and potentially associated variables. RCDI was defined as a positive C. difficile specimen occurring 2-8 weeks after incident C. difficile infection. We compared cases with and without RCDI by using multiple regression. RCDI occurred in 12.0% of 4,301 chart-reviewed C. difficile cases, showing a U-shaped time trend with a sharp increase in 2020, mostly because of an increase in hospital-onset cases. Malignancy (odds ratio 1.51 [95% CI 1.11-2.07]) and antecedent nitrofurantoin use (odds ratio 2.37 [95% CI 1.23-4.58]) were medical risk factors for RCDI. The 2020 increase may reflect the impact of the COVID-19 pandemic.

Vonoprazan poses no additional risk of developing Clostridioides difficile infection compared to proton pump inhibitors.

The use of proton pump inhibitors (PPIs) has been repeatedly reported as a trigger of Clostridioides difficile infection (CDI), a leading cause of nosocomial diarrhea. However, only a few studies have reported on the association between vonoprazan, a novel potassium-competitive acid blocker providing potent acid suppression, and CDI, with no studies having been conducted in a clinical setting. We therefore evaluated the association between various classes of acid suppressants and CDI with special attention paid to differences in the magnitudes of association between PPIs and vonoprazan. A retrospective hospital-based cohort from a secondary-care hospital in Japan (n=25821) was collected, wherein eligible CDI cases were defined as hospital-onset cases (n=91). A multivariable adjusted logistic regression analysis for the entire cohort and propensity analyses for subgroups consisting of PPI and/or vonoprazan users at various doses (n=10306) were performed. The overall CDI incidence rate was 1.42/10000 patient-days, which was comparable with previous reports. A multivariable analysis showed that both PPIs and vonoprazan were positively associated with CDI (odds ratios [95% confidence intervals]: 3.15 [1.67-5.96] and 2.63 [1.01-6.88], respectively). In addition, matched subgroup analyses showed that PPIs and vonoprazan had equivalent magnitudes of association with CDI. We found that both PPIs and vonoprazan were associated with CDI, and the magnitude of the association was comparable. Because vonoprazan is widely available in Asian countries, further studies on the association of its usage with CDI are warranted.

Estimation of the impact of hospital-onset SARS-CoV-2 infections on length of stay in English hospitals using causal inference.

From March 2020 through August 2021, 97,762 hospital-onset SARS-CoV-2 infections were detected in English hospitals. Resulting excess length of stay (LoS) created a potentially substantial health and economic burden for patients and the NHS, but we are currently unaware of any published studies estimating this excess. We implemented appropriate causal inference methods to determine the extent to which observed additional hospital stay is attributable to the infection rather than the characteristics of the patients. Hospital admissions records were linked to SARS-CoV-2 test data to establish the study population (7.5 million) of all non-COVID-19 admissions to English hospitals from 1st March 2020 to 31st August 2021 with a stay of at least two days. The excess LoS due to hospital-onset SARS-CoV-2 infection was estimated as the difference between the mean LoS observed and in the counterfactual where infections do not occur. We used inverse probability weighted Kaplan-Meier curves to estimate the mean survival time if all hospital-onset SARS-CoV-2 infections were to be prevented, the weights being based on the daily probability of acquiring an infection. The analysis was carried out for four time periods, reflecting phases of the pandemic differing with respect to overall case numbers, testing policies, vaccine rollout and prevalence of variants. The observed mean LoS of hospital-onset cases was higher than for non-COVID-19 hospital patients by 16, 20, 13 and 19days over the four phases, respectively. However, when the causal inference approach was used to appropriately adjust for time to infection and confounding, the estimated mean excess LoS caused by hospital-onset SARS-CoV-2 was: 2.0 [95% confidence interval 1.8-2.2] days (Mar-Jun 2020), 1.4 [1.2-1.6] days (Sep-Dec 2020); 0.9 [0.7-1.1] days (Jan-Apr 2021); 1.5 [1.1-1.9] days (May-Aug 2021). Hospital-onset SARS-CoV-2 is associated with a small but notable excess LoS, equivalent to 130,000 bed days. The comparatively high LoS observed for hospital-onset COVID-19 patients is mostly explained by the timing of their infections relative to admission. Failing to account for confounding and time to infection leads to overestimates of additional length of stay and therefore overestimates costs of infections, leading to inaccurate evaluations of control strategies.

Nosocomial SARS-CoV-2 transmission in multi-bedded hospital cubicles over successive pandemic waves: Lower mortality but wider spread with Omicron despite enhanced infection-prevention measures.

BackgroundIncreased transmissibility of severe-acute-respiratory-syndrome-coronavirus-2(SARS-CoV-2) variants, such as the Omicron-variant, presents an infection-control challenge. We contrasted nosocomial transmission amongst hospitalized inpatients across successive pandemic waves attributed to the Delta- and Omicron variants, over a 9-month period in which enhanced-infection-prevention-measures were constantly maintained.MethodsEnhanced-infection-prevention-measures in-place at a large tertiary hospital included universal N95-usage, routine-rostered-testing (RRT) for all inpatient/healthcare-workers (HCWs), rapid-antigen-testing (RAT) for visitors, and outbreak-investigation coupled with enhanced-surveillance (daily-testing) of exposed patients. The study-period lasted from 21st June 2021-21st March 2022. Chi-square test and multivariate-logistic-regression was utilized to identify factors associated with onward transmission and 28d-mortality amongst inpatient cases of hospital-onset COVID-19.ResultsDuring the Delta-wave, hospital-onset cases formed 2.7% (47/1727) of all COVID-19 cases requiring hospitalisation; in contrast, hospital onset-cases formed a greater proportion (17.7%, 265/1483; odds-ratio, OR=7.78, 95%CI=5.65-10.70) during the Omicron-wave, despite universal N95-usage and other enhanced infection-prevention measures that remained unchanged. The odds of 28d-mortality were higher during the Delta-wave compared to the Omicron-wave (27.7%, 13/47, vs. 10.6%, 28/265, adjusted-odds-ratio, aOR=2.78, 95%CI=1.02-7.69). Onward-transmission occurred in 21.2% (66/312) of hospital-onset cases; being on enhanced-surveillance (daily-testing) was independently associated with lower odds of onward-transmission (aOR=0.18, 95%CI=0.09-0.38)ConclusionA surge of hospital-onset COVID-19 cases was encountered during the Omicron-wave, despite continuation of enhanced infection-prevention measures; mortality amongst hospital-onset cases was reduced. The Omicron variant poses an infection-control challenge in contrast to Delta; surveillance is important especially in settings where infrastructural limitations make room-sharing unavoidable, despite the high risk of transmission.

Impact of a multipronged approach to reduce the incidence of Clostridioides difficile infections in hospitalized patients

Effective approaches to reduce Clostridioides difficile infections (CDI) in hospitalized patients are needed. We report data from 3 years preceding and 3 years following interventions that proved successful, with detailed analysis of all cases the first year after implementation. Interventions included a nursing protocol to identify cases present on admission by asking if the patient had 1 or more liquid stools in the last 24 hours, and a 2-step testing algorithm with samples positive by polymerase chain reaction (PCR) for the C. difficile toxin gene reflexing to an enzyme immunoassay (EIA) for the toxin antigen. Healthcare-associated infections due to CDI fell from ∼160 in each of the preceding 3 years to <65 in each of the subsequent 3 years (P < .001), while the ratio of observed-to-expected hospital-onset cases diminished to ∼0.50 (P < .02). In the first year, 395 samples were PCR(+), but only 118 (29.9%) of these were EIA(+). 55 (46.6%) of the PCR(+)/EIA(+) samples were from hospital day 1 or 2 and classified as present on admission. The mean time from stool collection to report of PCR results was ∼7.5 hours, and the EIA took on average only 68 additional minutes to be reported. The number of incident CDI cases can be dramatically decreased by implementing an admission screening question and a 2-step testing algorithm.

Protocol for the COG-UK hospital-onset COVID-19 infection (HOCI) multicentre interventional clinical study: evaluating the efficacy of rapid genome sequencing of SARS-CoV-2 in limiting the spread of COVID-19 in UK NHS hospitals

ObjectivesNosocomial transmission of SARS-CoV-2 has been a significant cause of mortality in National Health Service (NHS) hospitals during the COVID-19 pandemic. The COG-UK Consortium Hospital-Onset COVID-19 Infections (COG-UK HOCI) study...

Exploring temporal trends and risk factors for resistance in Escherichia coli-causing bacteraemia in England between 2013 and 2018: an ecological study.

Escherichia coli are Gram-negative bacteria associated with an increasing burden of antimicrobial resistance (AMR) in England. To create a comprehensive epidemiological picture of E. coli bacteraemia resistance trends and risk factors in England by linking national microbiology data sources and performing a longitudinal analysis of rates. A retrospective observational study was conducted on all national records for antimicrobial susceptibility testing on E. coli bacteraemia in England from 1 January 2013 to 31 December 2018 from the UK Health Security Agency (UKHSA) and the BSAC Resistance Surveillance Programme (BSAC-RSP). Trends in AMR and MDR were estimated using iterative sequential regression. Logistic regression analyses were performed on UKHSA data to estimate the relationship between risk factors and AMR or MDR in E. coli bacteraemia isolates. An increase in resistance rates was observed in community- and hospital-onset bacteraemia for third-generation cephalosporins, co-amoxiclav, gentamicin and ciprofloxacin. Among community-acquired cases, and after adjustment for other factors, patients aged >65 years were more likely to be infected by E. coli isolates resistant to at least one of 11 antibiotics than those aged 18-64 years (OR: 1.21, 95% CI: 1.18-1.25; P < 0.05). In hospital-onset cases, E. coli isolates from those aged 1-17 years were more likely to be resistant than those aged 18-64 years (OR: 1.33, 95% CI: 1.02-1.73; P < 0.05). Antibiotic resistance rates in E. coli-causing bacteraemia increased between 2013 and 2018 in England for key antimicrobial agents. Findings of this study have implications for guiding future policies on a prescribing of antimicrobial agents, for specific patient populations in particular.

Molecular Epidemiology of Large COVID-19 Clusters at an Academic Medical Center, March–October 2020

Background: COVID-19 in hospitalized patients may be the result of community acquisition or in-hospital transmission. Molecular epidemiology can help confirm hospital COVID-19 transmission and outbreaks. We describe large COVID-19 clusters identified in our hospital and apply molecular epidemiology to confirm outbreaks. Methods: The University of Iowa Hospitals and Clinics is an 811-bed academic medical center. We identified large clusters involving patients with hospital onset COVID-19 detected during March–October 2020. Large clusters included ≥10 individuals (patients, visitors, or HCWs) with a laboratory confirmed COVID-19 diagnosis (RT-PCR) and an epidemiologic link. Epidemiologic links were defined as hospitalization, work, or visiting in the same unit during the incubation or infectious period for the index case. Hospital onset was defined as a COVID-19 diagnosis ≥14 days from admission date. Admission screening has been conducted since May 2020 and serial testing (every 5 days) since July 2020. Nasopharyngeal swab specimens were retrieved for viral whole-genome sequencing (WGS). Cluster patients with a pairwise difference in ≤5 mutations were considered part of an outbreak. WGS was performed using Oxford Nanopore Technology and protocols from the ARTIC network. Results: We identified 2 large clusters involving patients with hospital-onset COVID-19. Cluster 1: 2 hospital-onset cases were identified in a medical-surgical unit in June 2020. Source and contact tracing revealed 4 additional patients, 1 visitor, and 13 employees with COVID-19. Median age for patients was 62 (range, 38–79), and all were male. In total, 17 samples (6 patients, 1 visitor, and 10 HCWs) were available for WGS. Cluster 2: A hospital-onset case was identified via serial testing in a non–COVID-19 intensive care unit in September 2020. Source investigation, contact tracing, and serial testing revealed 3 additional patients, and 8 HCWs. One HCW also had a community exposure. Patient median age was 60 years (range, 48–68) and all were male. In total, 11 samples (4 patients and 7 HCWs) were sequenced. Using WGS, cluster 1 was confirmed to be an outbreak: WGS showed 0–5 mutations in between samples. Cluster 2 was also an outbreak: WGS showed less diversity (0–3 mutations) and ruled out the HCW with a community exposure (20 mutations of difference). Conclusion: Whole-genome sequencing confirmed the outbreaks identified using classic epidemiologic methods. Serial testing allowed for early outbreak detection. Early outbreak detection and implementation of control measures may decrease outbreak size and genetic diversity.Funding: NoDisclosures: None

Coronavirus Disease 2019 Serial Testing Among Hospitalized Patients in a Midwest Tertiary Medical Center, July-September 2020.

We implemented serial COVID-19 testing for inpatients with a negative test on admission. The conversion rate (negative to positive) on repeat testing was one percent. We identified patients during their incubation period and hospital-onset cases, rapidly isolated them, and potentially reduced exposures. Serial testing and infectiousness determination were resource intensive.

The Incidence, Characteristics, and Outcomes of Community and Hospital-Associated S. aureus Disease in Fulton County, Georgia

Background: Due to reliance on hospital discharge data for case identification, the burden of noninvasive and community-acquired S. aureus disease is often underestimated. To determine the full burden of S. aureus infections, we utilized population-based surveillance in a large urban county. Methods: The Georgia Emerging Infections Program (GA EIP) conducted CDC-funded, population-based surveillance by finding cases of S. aureus infections in 8 counties around Atlanta in 2017. Cases were residents with S. aureus isolated from either a normally sterile site in a 30-day period (invasive cases) or another site in a 14-day period (noninvasive cases). Medical records (all invasive and 1:4 sample of noninvasive cases) among Fulton County residents were abstracted for clinical, treatment, and outcome data. Cases treated were mapped to standard therapeutic site codes. Noninvasive specimens were reviewed and attributed to an invasive case if both occurred within 2 weeks. Incidence rates were calculated using 2017 census population and using a weight-adjusted cohort to account for sampling. Results: In total, 1,186 noninvasive (1:4 sample) and 529 invasive cases of S. aureus in Fulton county were reviewed. Only 35 of 1,186 (2.9%) noninvasive cases were temporally linked to invasive cases, resulting in 5,133 cases after extrapolation (529 invasive, 4,604 noninvasive). All invasive cases and 3,776 of 4,604 noninvasive cases (82%) were treated (4,305 total). Treatment was highest in skin (90%) and abscess (97%), lowest in urine (62%) and sputum (60%), and consisted of antibacterial agents alone (65%) or in addition to drainage procedures (35%). Overall, 41% of all cases were hospitalized, 12% required ICU admission, and 2.7% died, almost exclusively with bloodstream and pulmonary infections. Attribution of noninvasive infection was most often outside healthcare settings (87%); only 341 (7.9%) were hospital-onset cases; however, 34% of cases had had healthcare exposure in the preceding year, most often inpatient hospitalization (75%) or recent surgery (35%). Estimated countywide incidence was 414 per 100,000 (130 for MRSA and 284 for MSSA), invasive infection was 50 per 100,000. Among treated cases, 57% were SSTI, and the proportion of cases caused by MRSA was ~33% but varied slightly by therapeutic site (Fig. 1). Conclusions: The incidence of treated S. aureus infection in our large urban county is estimated to be 414 per 100,000 persons, which exceeds previously estimated rates based on hospital discharge data. Only 12% of treated infections were invasive, and &lt;1 in 10 were hospital onset. Also, two-thirds of treated disease cases were MSSA; most were SSTIs.Funding: Proprietary Organization: Pfizer.Disclosures: Scott Fridkin, consulting fee - vaccine industry (spouse).

Mnemonic Flash Card Education Campaign Decreases Hospital-Onset Clostridioides difficile Infections

<h3>Background</h3> Clostridioides difficile (C. difficile) is now the most common healthcare-acquired pathogen. The pathogen causes life-threatening diarrhea and colitis. The hospital-onset C. difficile rate of a southern California hospital was significantly higher than that of local community hospitals in 2018. A review of the hospital-onset cases revealed that the majority were due to missed sample collection through patient day 3. The objective of this study is to create an education campaign that promotes smarter C.difficile testing in order to reduce hospital-onset C.difficile infections. <h3>Methods</h3> The C. difficile education campaign was centered around a mnemonic flash card (5 by 2.5 inches) titled "THE RUNS". The first word (THE) of the acronym had phrases focused on the importance of collecting specimens through patient admission day 3. The first word's phrases also emphasized prompt patient isolation and hand hygiene with soap and water. The second word (RUNS) had phrases that promoted C.difficile testing stewardship after patient day 3. Nursing conversations with unit leadership and physicians regarding the appropriateness of C.difficile testing were encouraged. The flash card was fastened to the majority of nursing unit computer monitors. The card's education was presented at nursing leadership meetings and unit huddles. <h3>Results</h3> The rate of hospital-onset C.difficile decreased from 4.4 cases per 10,000 patient days (January through October 2018) to 2.6 cases per 10,000 patient days (January through October 2019), representing a 41% reduction in rate. <h3>Conclusions</h3> The education campaign centered around the mnemonic flash card "THE RUNS" was successful in reducing the hospital onset C.difficile rate. Frontline healthcare providers became more knowledgeable about the importance of preventing hospital-onset C.difficile infections through smarter C.difficile testing.

2362. Back to the Future: The Impact of Multi-Step Algorithm C. difficile Testing at a Large Tertiary Medical Center

BackgroundAntibiotic stewardship and infection control programs rely on C. difficile infection (CDI) test results to measure CDI incidence in the hospital setting. C. difficile carriage is common and distinguishing infection from colonization is difficult with the highly sensitive nucleic acid amplification testing (NAAT) commonly used. Current guidelines recommend a multi-step algorithm for testing. The impact on patient outcomes and CDI metrics are largely unknown.MethodsThis was a pre-post study at the University of Maryland Medical Center, evaluating the impact of a CDI testing strategy (introduced October 2018) that simultaneously reported NAAT and confirmatory enzyme immunoassay (EIA) when used with existing best practice alerts for appropriate testing. Pre-intervention (November 2017–September 2018) and post intervention (October 2018–March 2019) periods were compared for mean CDI incidence (CDI per 10,000 admissions) defined by: (1) positive NAAT, (2) reported CDI (last positive test), and (3) treated CDI (receiving oral vancomycin). Both community and hospital-onset cases were included. The NAAT CDI incidence was used as the pre-intervention comparison for all 3 measures. In addition, oral vancomycin days of therapy (DOT) per 1,000 patient-days (PD) was compared. Pre–post comparisons of mean CDI incidence and mean DOT rates were done using Student t-test.ResultsThere were 3,237 samples tested (2,269 pre and 968 post-intervention) with 376 NAAT positive (262 pre and 114 post-intervention). Of the 99 tests with reflex EIA, there were 74 discordant tests (NAAT +/EIA -) with 35 (47%) treated for CDI. Mean NAAT CDI incidence pre-intervention was 54 per 10,000 admissions. Post-intervention mean CDI incidence decreased as follows: 45 NAAT CDI per 10,000 admissions (P = 0.13), 15 reported CDI per 1000 admissions (P < 0.0001), and 28 treated CDI per 10,000 admissions (P = 0.0007). Oral vancomycin DOT per 1,000 PD decreased from 16 to 9 (P = 0.0002).Conclusion C. difficile NAAT testing with confirmatory EIA, in combination with best practice alert, decreased reported and treated cases of CDI, which may distinguish infection vs. colonization and avoid unnecessary treatment, beyond that achieved with alerts that improve appropriate patient selection for testing. Disclosures All authors: No reported disclosures.

Web Exclusive. Annals for Hospitalists Inpatient Notes - How I Treat-Catheter-Related Deep Venous Thrombosis.

Web Exclusives21 May 2019Annals for Hospitalists Inpatient Notes - How I Treat—Catheter-Related Deep Venous ThrombosisVineet Chopra, MD, MScVineet Chopra, MD, MScUniversity of Michigan Health System and Veterans Affairs Ann Arbor Health System, Ann Arbor, Michigan (V.C.)Search for more papers by this authorAuthor, Article, and Disclosure Informationhttps://doi.org/10.7326/M19-1172 SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail With growing use of peripherally inserted central catheters (PICCs), upper-extremity deep venous thrombosis (DVT) is becoming more common. In a study of hospitalized medical patients, upper-extremity DVT accounted for half of all hospital-onset cases and most of these events were associated with PICCs (1). In a systematic review and meta-analysis, these catheters were found to be associated with a 2.5-fold risk for thrombosis compared with other central venous catheters. Of note, risk for DVT with PICCs was greater in patients with cancer and those with critical illness (2). Because hospitalists often care for and treat patients with PICCs and because ...

Factors that impact on the burden of Escherichia coli bacteraemia: multivariable regression analysis of 2011–2015 data from West London

The incidence of Escherichia coli bacteraemia in England is increasing amid concern regarding the roles of antimicrobial resistance and nosocomial acquisition on burden of disease. To determine the relative contributions of hospital-onset E. coli bloodstream infection and specific E. coli antimicrobial resistance patterns to the burden and severity of E. coli bacteraemia in West London. Patient and antimicrobial susceptibility data were collected for all cases of E. coli bacteraemia between 2011 and 2015. Multivariable logistic regression was used to determine the association between the category of infection (hospital or community-onset) and length of stay, intensive care unit admission, and 30-day all-cause mortality. E. coli bacteraemia incidence increased by 76% during the study period, predominantly due to community-onset cases. Resistance to quinolones, third-generation cephalosporins, and aminoglycosides also increased over the study period, occurring in both community- and hospital-onset cases. Hospital-onset and non-susceptibility to either quinolones or third-generation cephalosporins were significant risk factors for prolonged length of stay, as was older age. Rates of mortality were 7% and 12% at 7 and 30 days, respectively. Older age, a higher comorbidity score, and bacteraemia caused by strains resistant to three antibiotic classes were all significant risk factors for mortality at 30 days. Multidrug resistance, increased age, and comorbidities were the main drivers of adverse outcome. The rise in E. coli bacteraemia was predominantly driven by community-onset infections, and initiatives to prevent community-onset cases should be a major focus to reduce the quantitative burden of E. coli infection.