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Initial assessments of coronavirus disease 2019 (COVID-19) preparedness revealed resource shortages and variations in infection prevention policies across US hospitals. Our follow-up survey revealed improvement in resource availability, increase in testing capacity, and uniformity in infection prevention policies. Most importantly, the survey highlighted an increase in staffing shortages and use of travel nursing.

Background Increases in central line-associated bloodstream infection (CLABSI) rates have been reported in association with the COVID-19 pandemic, particularly among Candida species and coagulase-negative Staphylococcal species (CoNS). We sought to further validate the impact of the COVID-19 pandemic on CLABSI trends and perform a microbiologic analysis. Methods This is an IRB-approved retrospective analysis of CLABSIs across a network of 38 community hospitals in southeastern United States. CLABSI rates were compared between pre-pandemic (1/1/2018-3/30/2020) and pandemic periods (4/1/2020-12/31/2021). Regression models were developed to evaluate CLABSI incidence over time. Likelihood ratio tests were used to compare models that were exclusively time-dependent to segmented regression models that also accounted for the COVID-19 pandemic. Results A total of 1,167 CLABSIs over 1,345,062 central line days were analyzed (Table 1). The mean monthly CLABSI rate per hospital increased from 0.63 to 1.01 per 1,000 central line days (p< 0.001) in the pandemic period (Table 1). CLABSIs secondary to Candida (0.16 to 0.33, p< 0.001), CoNS (0.09 to 0.22, p< 0.001), and Enterococcal species (0.06 to 0.18, p=0.001) increased, while Escherichia coli CLABSIs decreased (0.04 to 0.01, p< 0.001). Upon regression modeling, the COVID-19 pandemic was associated with increases in monthly CLABSI rates by Candida and Enterococcus species (Figure 1). In contrast, the changes in CoNS and Escherichia coli CLABSI rates were better explained by exclusively time-dependent models (Figure 1; Table 2). Non-sustained changes in Staphylococcus aureus and Klebsiella pneumoniae CLABSI rates were also noted (Table 2). Table 1:Count data, central line days, and mean monthly CLABSI incidence by organism per hospital. Rates are provided as CLABSIs per 1,000 central line days. Figure 1:Regression analysis of monthly CLABSI rates by pathogen. Gray areas denote COVID-19 pandemic period. Statistically significant level changes in CLABSI rates were observed among Candida and Enterococcus spp. (RR=1.92, CI 1.16-3.20 and 2.42, CI 1.09-5.38). Staphylococcus aureus CLABSI rates had a non-sustained but significant increase at the onset of COVID-19 (RR 2.20, CI 1.16-4.20). CoNS and E. coli rate changes occurred independent of COVID-19 (see Table 2). Table 2:Coefficient table of regression analyses for CLABSI rates by pathogen. Exclusively time-dependent models were compared to segmented regression models for each organism and, if no significant difference was noted between models, only the time-dependent model was applied. Conclusion The COVID-19 pandemic was associated with substantial increases in CLABSIs, driven in part by Candida and Enterococcus species across this network of hospitals. However, the observed increase in CoNS CLABSIs and decrease in Escherichia coli CLABSIs appear to have occurred independently of COVID-19, which only became apparent upon regression analysis. Interpretation of pre-post statistics without assessment of pre-existing trends should be done cautiously. Additional analyses may help elucidate other factors influencing these CLABSI trends by organism. Disclosures Sonali D. Advani, MBBS, MPH, FIDSA, Locus Biosciences: Advisor/Consultant|Locus Biosciences: Honoraria|Sysmex America: Advisor/Consultant Melissa D. Johnson, PharmD, MHS, AAHIVP, Charles River Laboratories: Grant/Research Support|Entasis: Honoraria|Merck: Grant/Research Support|Pfizer: Honoraria|Scynexis: Grant/Research Support|Theratechnologies: Honoraria|UpToDate: Honoraria.

Background COVID-19 shifted antibiotic stewardship program resources and changed antibiotic use (AU). Shifts in patient populations with COVID surges, including pauses to surgical procedures, and dynamic practice changes makes temporal associations difficult to interpret. Our analysis aimed to address the impact of COVID on AU after adjusting for other practice shifts. Methods We performed a longitudinal analysis of AU data from 30 Southeast US hospitals. Three pandemic phases (1: 3/20–6/20; 2: 7/20–10/20; 3: 11/20–2/21) were compared to baseline (1/2018–1/2020). AU (days of therapy (DOT)/1000 patient days (PD)) was collected for all antimicrobial agents and specific subgroups: broad spectrum (NHSN group for hospital-onset infections), CAP (ceftriaxone, azithromycin, levofloxacin, moxifloxacin, and doxycycline), and antifungal. Monthly COVID burden was defined as all PD attributed to a COVID admission. We fit negative binomial GEE models to AU including phase and interaction terms between COVID burden and phase to test the hypothesis that AU changes during the phases were related to COVID burden. Models included adjustment for Charlson comorbidity, surgical volume, time since 12/2017 and seasonality. Results Observed AU rates by subgroup varied over time; peaks were observed for different subgroups during distinct pandemic phases (Figure). Compared to baseline, we observed a significant increase in overall, broad spectrum, and CAP groups during phase 1 (Table). In phase 2, overall and CAP AU was significantly higher than baseline, but in phase 3, AU was similar to baseline. These phase changes were separate from effects of COVID burden, except in phase 1 where we observed significant effects on antifungal (increased) and CAP (decreased) AU (Table). Conclusion Changes in hospital AU observed during early phases of the COVID pandemic appeared unrelated to COVID burden and may have been due to indirect pandemic effects (e.g., case mix, healthcare resource shifts). By pandemic phase 3, these disruptive effects were not as apparent, potentially related to shifts in non-COVID patient populations or ASP resources, availability of COVID treatments, or increased learning, diagnostic certainty, and provider comfort with avoiding antibacterials in patients with suspected COVID over time. Disclosures Melissa D. Johnson, PharmD, Biomeme: Licensed Transcriptional Signature for Candidemia|Charles River Laboratories: Grant/Research Support|Entasis Therapeutics: Advisor/Consultant|Merck & Co. Inc: Advisor/Consultant|Merck & Co. Inc: Grant/Research Support|Pfizer, Inc.: Advisor/Consultant|Scynexis Inc.: Grant/Research Support|Theratechnologies: Advisor/Consultant Angelina Davis, PharmD, M.S., Merck & Co.: Honoraria Sonali D. Advani, MBBS, MPH, FIDSA, Locus Biosciences: Advisor/Consultant|Locus Biosciences: Honoraria|Sysmex America: Advisor/Consultant Ibukun Kalu, MD, Pfizer, Inc.: Institutional support for clinical trial Rebekah W. Moehring, MD, MPH, FIDSA, FSHEA, UpToDate, Inc.: Author Royalties.

We performed a cross-sectional survey of infection preventionists in 60 US community hospitals between April 22 and May 8, 2020. Several differences in hospital preparedness for SARS-CoV-2 emerged with respect to personal protective equipment conservation strategies, protocols related to testing, universal masking, and restarting elective procedures.

The COVID-19 pandemic had a considerable impact on US healthcare systems, straining hospital resources, staff, and operations. However, a comprehensive assessment of the impact on healthcare associated infections (HAIs) across different hospitals with varying level of infectious disease (ID) physician expertise, resources, and infrastructure is lacking.This retrospective longitudinal multi-center cohort study included central-line-associated bloodstream infections (CLABSIs), catheter-associated urinary tract infections (CAUTIs), C. difficile infections (CDIs), and ventilator-associated events (VAEs) from 53 hospitals (academic and community) in Southeastern United States from January 1, 2018 to March 31, 2021. Segmented negative binomial regression generalized estimating equations models estimated changes in monthly incidence rates in the baseline (01/2018 - 02/2020) compared to the pandemic period (03/2020 - 03/2021, further divided into three pandemic phases).CLABSIs and VAEs increased by 24% and 34% respectively during the pandemic period. VAEs increased in all phases of the pandemic, while CLABSIs increased in later phases of the pandemic. CDI trend increased by 4.2% per month in the pandemic period. On stratifying the analysis by hospital characteristics, the impact of the pandemic on healthcare-associated infections was more significant in smaller sized and community hospitals. CAUTIs did not change significantly during the pandemic across all hospital types.CLABSIs, VAEs, and CDIs increased significantly during the pandemic, especially in smaller community hospitals, most of which lack ID physician expertise. Future efforts should focus on better understanding challenges faced by community hospitals, strengthening infection prevention infrastructure, and expanding the ID workforce, particularly to community hospitals.

Background The COVID-19 pandemic had a considerable impact on US healthcare systems, straining hospital resources, staff, and operations. Our objective was to evaluate the impact of COVID-19 pandemic on incidence and trends of healthcare-associated infections (HAIs) in a network of hospitals. Methods This was a retrospective review of central-line-associated bloodstream infections (CLABSIs), catheter-associated urinary tract infections (CAUTIs), C. difficile infections (CDI), and ventilator-associated events (VAE) in 51 hospitals from 2018 to 2021. Descriptive statistics were reported as mean hospital-level monthly incidence rates (IR) and compared using Poisson regression GEE models with period as the only covariate. Segmented regression (SR) analysis was performed to estimate changes in monthly IR of CAUTIs, CLABSIs and CDI in the baseline period (01/2018 – 02/2020) and the Pandemic period (03/2020 – 03/2021). SR model was not appropriate for VAE based on the plot. All models were constructed using SAS v.9.4 (SAS Institute, Cary NC). Results Compared to the baseline period, CLABSIs increased significantly by 50% from 0.6 to 0.9/ 1000 catheter days (P< 0. 001). In contrast, no significant changes were identified for CAUTI (P=0.87). Similar trends were seen in SR models for CLABSI and CAUTI (Figures 1, 2 and Table 1). While overall CDIs decreased significantly from 3.5 to 2.5/10,000 patient days in the pandemic period (P< 0.001), SR model showed increasing pandemic trend change (Figure 3). VAEs increased > 700% from 6.9 to 59.7/1000 ventilator days (P=0.15), but displayed considerable variation during the pandemic period (Figure 4). Compared to baseline period, there was a significant increase in central line days (647 vs 677, P=0.02), ventilator days (156 vs 215, P< 0.001), but no change in urinary catheter days (675 vs 686, P=0.32) during the pandemic period. Figure 1: Segmented Regression model showing baseline and pandemic period trends of CLABSI Figure 2: Segmented Regression model showing baseline and pandemic period trends of CAUTI Figure 3: Segmented Regression model showing baseline and pandemic period trends of C. difficile (HO-CDI) infections Conclusion The COVID-19 pandemic was associated with substantial increases in CLABSIs and VAEs, no change in CAUTIs, and an increasing trend in CDI incidence. These variations in trends of different HAIs are likely due, in part, to unique characteristics of the underlying infection, resource shortages, staffing concerns, increased device use, changes in testing practices, and the limitations of surveillance definitions. Figure 4: Trend of Ventilator-Associated Events (VAE) in the baseline and pandemic period (Segmented Regression model not appropriate) Disclosures Sonali D. Advani, MBBS, MPH, Nothing to disclose David J. Weber, MD, MPH, Merck (Individual(s) Involved: Self): Consultant; PDI (Individual(s) Involved: Self): Consultant; Pfizer (Individual(s) Involved: Self): Consultant; Sanofi (Individual(s) Involved: Self): Consultant; UVinnovators (Individual(s) Involved: Self): Consultant

Background Traditional approaches for SSI surveillance have deficiencies that can delay detection of SSI outbreaks and other clinically important increases in SSI rates. Optimized SPC methods for SSI surveillance have not been prospectively evaluated. Methods We conducted a prospective multicenter stepped wedge cluster RCT to evaluate the performance of SSI surveillance and feedback performed with optimized SPC plus traditional surveillance methods compared to traditional surveillance alone. We divided 13 common surgical procedures into 6 clusters (Table 1). A cluster of procedures at a single hospital was the unit of randomization and analysis, and 105 total clusters across 29 community hospitals were randomized to 12 groups of 8-10 clusters (Figure 1). After a 12-month baseline observation period (3/2016-2/2017), the SPC surveillance intervention was serially implemented according to stepped wedge assignment over a 36-month intervention period (3/2017-2/2020) until all 12 groups of clusters had received the intervention. The primary outcome was the overall SSI prevalence rate (PR=SSIs/100 procedures), evaluated with a GEE model with Poisson distribution. Table 1 Figure 1 Schematic for stepped wedge design. The 12-month baseline observation period was followed by the 36-month intervention period, comprised of 12 3-month steps. Results Our trial involved prospective surveillance of 237,704 procedures that resulted in 1,952 SSIs (PR=0.82). The overall SSI PR did not differ significantly between clusters of procedures assigned to SPC surveillance (781 SSIs/89,339 procedures; PR=0.87) and those assigned to traditional surveillance (1,171 SSIs/148,365 procedures; PR=0.79; PR ratio=1.10 [95% CI, 0.94–1.30]; P=.25) (Table 2). SPC surveillance identified 104 SSI rate increases that required formal investigations, compared to only 25 investigations generated by traditional surveillance. Among 10 best practices for SSI prevention, 453 of 502 (90%) SSIs analyzed due to SPC detection of SSI rate increases had at least 2 deficiencies (Table 3). Table 2 Poisson regression models comparing surgical site infection (SSI) prevalence rates for procedure clusters receiving statistical process control surveillance to SSI rates for clusters receiving traditional control surveillance. Table 3 Compliance with 10 best practices for surgical site infection (SSI) prevention among 502 SSIs analyzed during SSI investigations generated by statistical process control surveillance. Conclusion SPC methods more frequently detected important SSI rate increases associated with deficiencies in SSI prevention best practices than traditional surveillance; however, feedback of this information did not lead to SSI rate reductions. Further study is indicated to determine the best application of SPC methods to improve adherence to SSI quality measures and prevent SSIs. Disclosures Arthur W. Baker, MD, MPH, Medincell (Advisor or Review Panel member) Susan S. Huang, MD, MPH, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals and nursing homes received contributed antiseptic and cleaning products)Molnlycke (Other Financial or Material Support, Conducted studies in which participating hospitals and nursing homes received contributed antiseptic and cleaning products)Stryker (Sage) (Other Financial or Material Support, Conducted studies in which participating hospitals and nursing homes received contributed antiseptic and cleaning products)Xttrium (Other Financial or Material Support, Conducted studies in which participating hospitals and nursing homes received contributed antiseptic and cleaning products)

Background The COVID-19 pandemic placed a strain on inpatient clinical and hospital programs due to increased patient volume and rapidly evolving data on best COVID-19 management strategies. However, the impact of the pandemic on ASPs has not been well described. Methods We performed a cross-sectional electronic survey of stewardship pharmacy and physician leaders in 37 hospitals within the Duke Antimicrobial Stewardship Outreach Network (DASON) (community) and Duke/UNC Health systems (academic) in April-May 2021. The survey included 60 questions related to staffing changes, use of COVID-targeted therapies, related restrictions, and medication shortages. Results Twenty-seven facilities responded (response rate of 73%). Pharmacy personnel was reduced in 17 (63%) facilities by an average of 16%. Impacted pharmacy personnel included the stewardship lead in 15/17 (88.2%) hospitals. Converting to remote work was rare and only reported in academic institutions (n=2, 7.4%). ASP personnel were reassigned to non-stewardship duties in 12 (44%) hospitals with only half returning to routine ASP work as of May 2021. Respondents estimated that 62% of routine ASP activities were diverted during the time of the pandemic. Non-traditional, pandemic-related ASP activities included managing multiple drug shortages, of which ventilator support medications (91%) were most common affecting patient care at 52% of facilities. Steroid and hydroxychloroquine shortages were less frequent (44% and 22%, respectively). Despite staff reductions, pharmacists often served as primary contact for remdesivir approvals either using a criteria-based checklist at dispensing or as part of a dedicated phone approval team (Figure). Most (77%) hospitals used a criteria-based pharmacist review strategy after remdesivir FDA approval. Restriction processes for other COVID-19 therapies such as tocilizumab, hydroxychloroquine, and ivermectin were reported in 64% of hospitals. Remdesivir Allocation Strategy Proportion of facilities implementing specific remdesivir allocation strategies from the time of the first US Food and Drug Administration (FDA) Emergency Use Authorization (EUA) through FDA approval Conclusion Pandemic response diverted routine ASP work and has not yet returned to baseline. Despite the reduction in pharmacy personnel due to the pandemic, the ASP pharmacy lead took on a novel and critical stewardship role throughout the pandemic exemplified by their involvement in novel treatment allocation for COVID patients. Disclosures Melissa D. Johnson, PharmD, MHS, Astellas (Consultant, Grant/Research Support)Charles River Laboratories (Grant/Research Support)Cidara (Consultant)Merck & Co (Consultant, Research Grant or Support)Paratek (Consultant)Pfizer (Consultant)Scynexis (Scientific Research Study Investigator)Theratechnologies (Consultant)UpToDate (Other Financial or Material Support, Author Royalties) Sonali D. Advani, MBBS, MPH, Nothing to disclose Rebekah W. Moehring, MD, MPH, UpToDate, Inc. (Other Financial or Material Support, Author Royalties)

Background The SARS-CoV-2 pandemic has placed a tremendous strain on the U.S. healthcare system leading to personal protective equipment (PPE) and resource shortages. Hospitals have developed contingency and crisis capacity strategies to optimize the use of resources, but, to date, community hospital preparedness has not been described. Methods We performed a cross-sectional survey of infection preventionists in 60 community hospitals within the Duke Infection Control Outreach Network between April 22 and May 7, 2020 using Qualtrics. The survey included 13 questions related to resource availability, crisis capacity strategies and approaches to testing. Results We received 50 responses during the study period with a response rate of 83%. Community hospitals reported varying degrees of PPE shortages (Table 1); 80% of community hospitals were implementing strategies to extend and reuse N95 respirators, Powered Air-Purifying Respirators, face shields and face masks. Over 70% of facilities reported reprocessing N95 respirators (Figure 1). Almost all facilities reported universal masking at time of this survey with 90% performing daily employee screening at point of entry. Additionally, 8% of facilities restarted elective procedures at the time of this survey, but only 54% of facilities reported that they were performing preoperative testing for SARS-CoV-2. Thirty-seven percent of facilities performed one SARS-CoV-2 test before discharging an asymptomatic patient to skilled nursing facility, while 43% of facilities performed 2 tests. Table 1- Supply of Personal Protective Equipment and other resources in 50 community hospitals in southeastern United States Figure 1: Different methods of reprocessing N95 respirators by 50 community hospitals in southeastern United States Conclusion Our findings reveal differences in resource availability, crisis capacity strategies and testing approaches used by community hospitals in preparation for the SARS-COV-2 pandemic. Lack of harmonization in approaches may be in part due to differences in state guidelines and decentralized federal approach to SARS-CoV-2 preparedness. Disclosures All Authors: No reported disclosures

Background The NHSN recently updated risk adjustment models and “rebaselined” Standardized Infection Ratios (SIRs) for healthcare-associated infections. The CDC expected that hospital SIRs would generally increase. However, the impact of rebaselining on individual hospitals’ SIRs was unknown. Accordingly, we assessed the impact of rebaselining on SIRs in a network of community hospitals. Methods We analyzed 2016 SIR data for CAUTI, MRSA LabID events, CDI LabID events, colon SSIs (COLO), and abdominal hysterectomy SSIs (HYST) from 38 hospitals in the Duke Infection Control Outreach Network (DICON). SIRs calculated using the old and new baselines were compared. Wilcoxon signed rank test was performed to determine whether hospitals’ SIRs changed significantly following rebaselining. Hospitals were ranked by SIR for each metric, and change in rank following rebaselining was determined. Meaningful change in rank was defined as increase or decrease by ≥4 places (greater than a decile). Hospitals that did not have an SIR calculated for a given metric were excluded from that metric’s analysis. Results Median hospital SIRs for CAUTI and CDI increased significantly after rebaselining (0.587 vs 0.307, P Conclusion SIRs increased following rebaselining for CAUTI and CDI but did not change significantly for MRSA, COLO, or HYST. The majority of hospitals’ SIR rank did not change meaningfully following rebaselining. Disclosures D. Sexton, Centers for Disease Control and Prevention: Grant Investigator, Grant recipient; Centers for Disease Control and Prevention Foundation: Grant Investigator, Grant recipient; UpToDate: Collaborator, Royalty Recipient

Extended-spectrum-β-lactamase (ESBL)-producing organisms are increasingly prevalent. We determined the characteristics of 66 consecutive ESBL-producing isolates from six community hospitals in North Carolina and Virginia from 2010 to 2012. Fifty-three (80%) ESBL-producing isolates contained CTX-M enzymes; CTX-M-15 was found in 68% of Escherichia coli and 73% of Klebsiella isolates. Sequence type 131 (ST131) was the commonest type of E. coli , accounting for 48% of CTX-M-15-producing and 66% of CTX-M-14-producing isolates. In conclusion, the CTX-M genotype and ST131 E. coli were common among ESBL isolates from U.S. community hospitals.