Background:Escherichia coli (E. coli) is a leading cause of infections among term and preterm newborn infants. Continued surveillance of neonatal E. coli antibiotic susceptibility patterns is important to optimize empiric antibiotic prescription for infants at risk for infection, in light of evolving reports of multidrug-resistant gram-negative bacteria in all settings. Our objective was to determine E. coli epidemiology and antibiotic susceptibility patterns among a large sample of infants admitted to neonatal intensive care units (NICUs) across the United States from 2009 to 2017. Methods: Retrospective observational study using the Premier Database, including infants born from 2009 to 2017 and admitted to academic or community NICUs contributing microbiology data during the study period. We analyzed antibiotic susceptibilities for E. coli isolated from blood, cerebrospinal fluid, and urine. We focused on clinically relevant and priority susceptibility categories: (1) ampicillin nonsusceptible; (2) aminoglycoside nonsusceptible; (3) carbapenem nonsusceptible; and (4) extended-spectrum β-lactamase (ESBL; phenotypic definition). We determined the proportion of infants with nonsusceptible organisms in each category by year and tested for changes over time. Lastly, we assessed susceptibility patterns by specimen source, birthweight, and timing of infection. Results: Of the 117,484 included infants, 733 (0.6%) had at least 1 E. coli episode, of which 721 (98.4%) had available susceptibility results, from 69 centers. Patient and center characteristics of infants with E. coli are shown in Table 1. Most organisms were tested against ampicillin (99.9%), gentamicin (99.6%), and ceftriaxone (91.5%). Figure 1 shows nonsusceptibility rates for the categories of interest. Overall, ampicillin nonsusceptibility ranged from 63.3% to 68.6% per year (mean, 66.8%±1.5%); aminoglycoside nonsusceptibility ranged from 10.7% to 23.2% (mean, 16.8%±4.5%); carbapenem nonsusceptibility was 0% for all years; and ESBL ranged from 1.2% to 11.3% (mean, 5.1%±3.4%). We detected no statistically significant trends for any of the categories of interest over time (all P > .05), and susceptibility trends were consistent when repeated by specimen source, birthweight, and timing of infection. Conclusions: We found stable, yet concerning, patterns of E. coli antibiotic nonsusceptibility among infants admitted to NICUs across the United States from 2009 to 2017. Rates of ampicillin nonsusceptibility and aminoglycoside nonsusceptibility were higher than previous reports. ESBL E. coli rates were low but present among neonatal patients. No carbapenem nonsusceptible E. coli was identified. These findings can inform empiric antibiotic prescription for infants admitted to NICUs across the United States.

Funding: None

Disclosures: None

Background: Nursing home (NH) residents are at high risk for Clostridioides difficile infection (CDI) due to older age, frequent antibiotic exposure, and previous healthcare exposure. Incidence of CDI attributed to NHs is not well established, but it is hypothesized to be related to the magnitude of transfers. We evaluated the relationship between NH CDI incidence and facility characteristics to explain variability in rates in Atlanta, Georgia. Methods: Incident C. difficile cases from 2016 to 2018 were identified through the Georgia Emerging Infections Program (funded by the Centers for Disease Control and Prevention), which conducts active population-based surveillance in the 8-county metro Atlanta area. An incident case was defined as an NH resident with a toxin-positive stool specimen (without a positive test within 8 weeks). Sampled (1 to 3 on age and gender) incident cases were attributed to a NH if a patient was an NH resident within 4 days of specimen collection. Facility characteristics (beds, resident days, admissions, and average length of stay [ALOS]) were obtained from NH cost reports, and facility-specific connectivity metrics were calculated (indegree and betweenness) from 2016 Medicare claims data. Case counts were aggregated to estimate yearly incidence and correlated with facility characteristics and location within the healthcare network using the Spearman correlation. A negative binomial model was used to assess residual variability in NH CDI incidence. Results: In total, 386 incident CDI cases were attributed to 64 NHs (range, 0–27). Approximately half (54.7%) resided in the NH at the time of specimen collection; however, 33.7% were in inpatient units (≤4 days of admission), and 10.9% were in an emergency room (ER). The frequency of NH CDI cases correlated strongly with admissions (r = .70; P < .01), inversely with ALOS (r = −0.53; P < .01), and moderately with resident days (r = .38; P < .01). After accounting for admissions, incidence (per 1,000 admissions) still varied (Fig. 1) (median 14; range, 0–34). The inverse association with ALOS decreased and incidence no longer correlated with the remaining facility characteristics or location within the healthcare transfer network (P > .05, all comparisons). However, there was residual correlation with connectivity metrics (indegree r = 0.26; P = .04). Conclusions: Our data suggest that attributing CDI to NHs requires the inclusion of hospital and ER-based specimen collection. NH CDI incidence appears highest among facilities with a low ALOS and a high number of admissions; incidence rates calculated per 1,000 admissions may best account for infection risk inherent early in a resident’s stay. Residual variability attributed to connectivity to the healthcare network was of borderline significance and should be further explored in the NH setting.

Funding: None

Disclosures: Scott Fridkin, consulting fee, vaccine industry (spouse)

Background: Certain nursing home (NH) resident care tasks have a higher risk for multidrug-resistant organisms (MDRO) transfer to healthcare personnel (HCP), which can result in transmission to residents if HCPs fail to perform recommended infection prevention practices. However, data on HCP-resident interactions are limited and do not account for intrafacility practice variation. Understanding differences in interactions, by HCP role and unit, is important for informing MDRO prevention strategies in NHs. Methods: In 2019, we conducted serial intercept interviews; each HCP was interviewed 6–7 times for the duration of a unit’s dayshift at 20 NHs in 7 states. The next day, staff on a second unit within the facility were interviewed during the dayshift. HCP on 38 units were interviewed to identify healthcare personnel (HCP)–resident care patterns. All unit staff were eligible for interviews, including certified nursing assistants (CNAs), nurses, physical or occupational therapists, physicians, midlevel practitioners, and respiratory therapists. HCP were asked to list which residents they had cared for (within resident rooms or common areas) since the prior interview. Respondents selected from 14 care tasks. We classified units into 1 of 4 types: long-term, mixed, short stay or rehabilitation, or ventilator or skilled nursing. Interactions were classified based on the risk of HCP contamination after task performance. We compared proportions of interactions associated with each HCP role and performed clustered linear regression to determine the effect of unit type and HCP role on the number of unique task types performed per interaction. Results: Intercept-interviews described 7,050 interactions and 13,843 care tasks. Except in ventilator or skilled nursing units, CNAs have the greatest proportion of care interactions (interfacility range, 50%–60%) (Fig. 1). In ventilator and skilled nursing units, interactions are evenly shared between CNAs and nurses (43% and 47%, respectively). On average, CNAs in ventilator and skilled nursing units perform the most unique task types (2.5 task types per interaction, Fig. 2) compared to other unit types (P < .05). Compared to CNAs, most other HCP types had significantly fewer task types (0.6–1.4 task types per interaction, P < .001). Across all facilities, 45.6% of interactions included tasks that were higher-risk for HCP contamination (eg, transferring, wound and device care, Fig. 3). Conclusions: Focusing infection prevention education efforts on CNAs may be most efficient for preventing MDRO transmission within NH because CNAs have the most HCP–resident interactions and complete more tasks per visit. Studies of HCP-resident interactions are critical to improving understanding of transmission mechanisms as well as target MDRO prevention interventions.

Funding: Centers for Disease Control and Prevention (grant no. U01CK000555-01-00)

Disclosures: Scott Fridkin, consulting fee, vaccine industry (spouse)

Background: Surgical site infections (SSIs) are among the most common healthcare-associated infections (HAIs) in low- and middle-income countries (LMICs). SSI surveillance can be challenging and resource-intensive to implement in LMICs. To support feasible LMIC SSI surveillance, we piloted a multisite SSI surveillance protocol using simplified case definitions and methodology in Sierra Leone. Methods: A standardized evaluation tool was used to assess SSI surveillance knowledge, capacity, and attitudes at 5 proposed facilities. We used simplified case definitions restricted to objective, observable criteria (eg, wound purulence or intentional reopening) without considering the depth of infection. Surveillance was limited to post-cesarean delivery patients to control variability of patient-level infection risk and to decrease data collection requirements. Phone-based patient interviews at 30-days facilitated postdischarge case finding. Surveillance activities utilized existing clinical staff without monetary incentives. The Ministry of Health provided training and support for data management and analysis. Results: Three facilities were selected for initial implementation. At all facilities, administration and surgical staff described most, or all, infections as “preventable” and all considered SSIs an “important problem” at their facility. However, capacity assessments revealed limited staff availability to support surveillance activities, limited experience in systematic data collection, nonstandardized patient records as the basis for data collection, lack of unique and consistent patient identifiers to link patient encounters, and no quality-assured microbiology services. To limit system demands and to maximize usefulness, our surveillance data collection elements were built into a newly developed clinical surgical safety checklist that was designed to support surgeons’ clinical decision making. Following implementation and 2 months of SSI surveillance activities, 77% (392 of 509) of post-cesarean delivery patients had a checklist completed within the surveillance system. Only 145 of 392 patients (37%) under surveillance were contacted for final 30-day phone interview. Combined SSI rate for the initial 2-months of data collection in Sierra Leone was 8% (32 of 392) with 31% (10 of 32) identified through postdischarge case finding. Discussion: The surveillance strategy piloted in Sierra Leone represents a departure from established HAI strategies in the use of simplified case definitions and implementation methods that prioritize current feasibility in a resource-limited setting. However, our pilot implementation results suggest that even these simplified SSI surveillance methods may lack sustainability without additional resources, especially in postdischarge case finding. However, even limited phone-based patient interviews identified a substantial number of infections in this population. Although it was not addressed in this pilot study, feasible laboratory capacity building to support HAI surveillance efforts and promote appropriate treatment should be explored.

Funding: None

Disclosures: None

Background: The current approach to measuring hand hygiene (HH) relies on human auditors who capture <1% of HH opportunities and rapidly become recognized by staff, resulting in inflation in performance. Our goal was to assess the impact of group electronic monitoring coupled with unit-led quality improvement on HH performance and prevention of healthcare-associated transmission and infection. Methods: A stepped-wedge cluster randomized quality improvement study was undertaken across 5 acute-care hospitals in Ontario, Canada. Overall, 746 inpatient beds were electronically monitored across 26 inpatient medical and surgical units. Daily HH performance as measured by group electronic monitoring was reported to inpatient units who discussed results to guide unit-led improvement strategies. The primary outcome was monthly HH adherence (%) between baseline and intervention. Secondary outcomes included transmission of antibiotic resistant organisms such as methicillin resistant Staphylococcus aureus (MRSA) and other healthcare-associated infections. Results: After adjusting for the correlation within inpatient units, there was a significant overall improvement in HH adherence associated with the intervention (IRR, 1.73; 95% CI, 1.47–1.99; P < .0001). Monthly HH adherence relative to the intervention increased from 29% (1,395,450 of 4,544,144) to 37% (598,035 of 1,536,643) within 1 month, followed by consecutive incremental increases up to 53% (804,108 of 1,515,537) by 10 months (P < .0001). We identified a trend toward reduced healthcare-associated transmission of MRSA (0.74; 95% CI, 0.53–1.04; P = .08). Conclusions: The introduction of a system for group electronic monitoring led to rapid, significant, and sustained improvements in HH performance within a 2-year period.

Funding: None

Disclosures: None

Background: Healthcare-associated infection (HAI) surveillance is essential for most infection prevention programs and continuous epidemiological data can be used to inform healthcare personal, allocate resources, and evaluate interventions to prevent HAIs. Many HAI surveillance systems today are based on time-consuming and resource-intensive manual reviews of patient records. The objective of HAI-proactive, a Swedish triple-helix innovation project, is to develop and implement a fully automated HAI surveillance system based on electronic health record data. Furthermore, the project aims to develop machine-learning–based screening algorithms for early prediction of HAI at the individual patient level. Methods: The project is performed with support from Sweden’s Innovation Agency in collaboration among academic, health, and industry partners. Development of rule-based and machine-learning algorithms is performed within a research database, which consists of all electronic health record data from patients admitted to the Karolinska University Hospital. Natural language processing is used for processing free-text medical notes. To validate algorithm performance, manual annotation was performed based on international HAI definitions from the European Center for Disease Prevention and Control, Centers for Disease Control and Prevention, and Sepsis-3 criteria. Currently, the project is building a platform for real-time data access to implement the algorithms within Region Stockholm. Results: The project has developed a rule-based surveillance algorithm for sepsis that continuously monitors patients admitted to the hospital, with a sensitivity of 0.89 (95% CI, 0.85–0.93), a specificity of 0.99 (0.98–0.99), a positive predictive value of 0.88 (0.83–0.93), and a negative predictive value of 0.99 (0.98–0.99). The healthcare-associated urinary tract infection surveillance algorithm, which is based on free-text analysis and negations to define symptoms, had a sensitivity of 0.73 (0.66–0.80) and a positive predictive value of 0.68 (0.61–0.75). The sensitivity and positive predictive value of an algorithm based on significant bacterial growth in urine culture only was 0.99 (0.97–1.00) and 0.39 (0.34–0.44), respectively. The surveillance system detected differences in incidences between hospital wards and over time. Development of surveillance algorithms for pneumonia, catheter-related infections and Clostridioides difficile infections, as well as machine-learning–based models for early prediction, is ongoing. We intend to present results from all algorithms. Conclusions: With access to electronic health record data, we have shown that it is feasible to develop a fully automated HAI surveillance system based on algorithms using both structured data and free text for the main healthcare-associated infections.

Funding: Sweden’s Innovation Agency and Stockholm County Council

Disclosures: None

Background: Carbapenem-resistant Enterobacteriaceae (CRE) are a serious threat to public health due to high associated morbidity and mortality. Healthcare personnel (HCP) gloves and gowns are frequently contaminated with antibiotic-resistant bacteria, including CRE. We aimed to identify patients more likely to transmit CRE to HCP gloves or gowns and HCP types and interactions more likely to lead to glove or gown contamination. Methods:Between January 2016 and August 2018, patients with a clinical or surveillance culture positive for CRE in the preceding 7 days were enrolled at 5 hospitals in California, Maryland, New York, and Pennsylvania. Ten HCP–patient interactions were observed for each patient and were recorded by research staff. Following patient care, but prior to doffing, the gloves and gown of each HCP were sampled for the presence of CRE. Results: We enrolled 313 CRE-colonized patients, and we observed 3,070 HCP interactions. CRE was transmitted to HCP gloves in 242 of 3,070 observations (7.9%) and to gowns in 132 of 3,070 observations (4.3%). Transmission to either gloves or gown occurred in 308 of 3,070 interactions observed (10%). The most frequently identified organism was Klebsiella pneumoniae (n = 171, 53.2%), followed by Enterobacter cloacae (n = 36, 11.2%), and Escherichia coli (n = 33, 10.3%). Patients in the intensive care unit (n = 177, 56.5%) were more likely to transmit CRE to HCP gloves or gown (OR, 1.65; 95% CI, 1.03–2.64) compared to those not in an ICU and adjusted for HCP type. The odds of CRE transmission increased with the number of different items touched near the patient (OR, 1.32; 95% CI, 1.21–1.44) and with the number of different items touched in the environment (OR, 1.13; 95% CI, 1.06–1.21). Respiratory therapists had the highest rates of transmission to gloves and gown (OR, 3.79; 95% CI, 1.61–8.94), followed by physical therapists and occupational therapists (OR, 2.82; 95% CI, 1.01–8.32) when compared to HCP in the “other” category. Manipulating the rectal tube (OR, 3.03; 95% CI, 1.53–6.04), providing wound care (OR, 2.81; 95% CI, 1.73–4.59), and touching the endotracheal tube (OR, 2.79; 95% CI, 1.86–4.19) were the interactions most strongly associated with CRE transmission compared to not touching these items and adjusted for HCP type. Conclusions: Transmission of CRE to HCP gloves and gowns occurs frequently. We identified interactions and HCP types that were particularly high risk for transmission. Infection control programs may wish to target infection prevention resources and education toward these high-risk professions and interactions.

Funding: This work was supported by the CDC Prevention Epicenter Program (U43CK000450-01) and the NIH National Institute of Allergy and Infectious Diseases (R01 AI121146-01).

Disclosures: None

Background: To provide a standardized, risk-adjusted method for summarizing antimicrobial use (AU), the Centers for Disease Control and Prevention developed the standardized antimicrobial administration ratio, an observed-to-predicted use ratio in which predicted use is estimated from a statistical model accounting for patient locations and hospital characteristics. The infection burden, which could drive AU, was not available for assessment. To inform AU risk adjustment, we evaluated the relationship between the burden of drug-resistant gram-positive infections and the use of anti-MRSA agents. Methods: We analyzed data from acute-care hospitals that reported ≥10 months of hospital-wide AU and microbiologic data to the National Healthcare Safety Network (NHSN) from January 2018 through June 2019. Hospital infection burden was estimated using the prevalence of deduplicated positive cultures per 1,000 admissions. Eligible cultures included blood and lower respiratory specimens that yielded oxacillin/cefoxitin–resistant Staphylococcus aureus (SA) and ampicillin-nonsusceptible enterococci, and cerebrospinal fluid that yielded SA. The anti-MRSA use rate is the total antimicrobial days of ceftaroline, dalbavancin, daptomycin, linezolid, oritavancin, quinupristin/dalfopristin, tedizolid, telavancin, and intravenous vancomycin per 1,000 days patients were present. AU rates were modeled using negative binomial regression assessing its association with infection burden and hospital characteristics. Results: Among 182 hospitals, the median (interquartile range, IQR) of anti-MRSA use rate was 86.3 (59.9–105.0), and the median (IQR) prevalence of drug-resistant gram-positive infections was 3.4 (2.1–4.8). Higher prevalence of drug-resistant gram-positive infections was associated with higher use of anti-MRSA agents after adjusting for facility type and percentage of beds in intensive care units (Table 1). Number of hospital beds, average length of stay, and medical school affiliation were nonsignificant. Conclusions: Prevalence of drug-resistant gram-positive infections was independently associated with the use of anti-MRSA agents. Infection burden should be used for risk adjustment in predicting the use of anti-MRSA agents. To make this possible, we recommend that hospitals reporting to NHSN’s AU Option also report microbiologic culture results.

Funding: None

Disclosures: None

Background: State Health Departments (SHDs) have systematically studied the validity of healthcare-associated infection (HAI) surveillance data submitted by healthcare facilities in their jurisdictions to the Centers for Disease Control and Prevention’s (CDC’s) National Healthcare Safety Network (NHSN) for central-line–associated bloodstream infections (CLABSIs), catheter-associated urinary tract infections (CAUTIs), surgical site infections following colon and abdominal hysterectomy procedures (SSI COLO and HYST), methicillin-resistant Staphylococcus aureus and Clostridioides difficile laboratory identified (MRSA and CDI LabID respectively) events. These studies are a key source of information about data quality and completeness serving as an impetus and a guide for improving the caliber of NHSN’s HAI data. Methods: We contacted SHD HAI coordinators in all states for an inventory of HAI validation studies. We used data from these studies to calculate pooled mean sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for HAI case determinations. HAI case reporting “error rates” were computed as the proportion of mismatches (underreport and overreport) among the medical records reviewed by SHDs and reasons for misclassification were categorized. Results: SHD validation studies varied by HAI type (range, 4 studies for MRSA LabID and 23 for CLABSI). Pooled mean sensitivity of HAI reporting ranged from 73.1% (COLO SSI) to 92.7% (CDI LabID). Pooled mean specificity and PPV exceeded 90% for all HAIs. LabID event validations demonstrated the lowest NPV (58.8% for MRSA and 55.1% for CDI). Error rates of HAI reporting to NHSN ranged from 2.5% (HYST SSI) to 13.6% (MRSA LabID). Common errors identified during CLABSI and CAUTI validations were incorrect application of general NHSN and CLABSI- and CAUTI-specific definitions. Incorrect secondary BSI attribution was the most frequently identified reason by CLABSI SHD validations (64.7%). Of all operative procedure-associated misclassifications, inconsistent surveillance practices (66.6%), incorrect NHSN operative procedure category assignment (55.5%), and misapplication of general organ-space and/or site-specific infection criteria (44.4%) were identified as the most common shortcomings. Among MRSA and CDI LabID validations, missed case finding due to failure to review candidate events and gaps in understanding the 14-day reporting rule of LabID protocol were identified as predominant reasons for inaccurate reporting. Conclusions: SHD HAI data validations identified specific targets for additional surveillance training, especially CLABSI determinations and application of the protocol rules for MDRO/CDI LabID case determinations. Further work is also needed to assure that data sources in addition to wound cultures are used for SSI determinations and that postdischarge SSI surveillance is more vigorous and comprehensive.

Funding: None

Disclosures: None

Background: In July 2017, recognizing the threat that antimicrobial resistance poses to the population, the Ethiopian Public Health Institute (EPHI) launched the Ethiopia AMR Surveillance Network at 4 sentinel laboratories. Simultaneously, laboratory capacity building was initiated to ensure the reporting of quality laboratory data to the surveillance system. One initiative, Project ECHO (Extension for Community Healthcare Outcomes) was used to virtually connect subject matter experts with participating laboratories in remote settings to provide ongoing education and telementoring and to foster peer-to-peer learning and problem solving in microbiology. The 10-month project was supported by the Centers for Disease Control and Prevention (CDC) and the American Society for Microbiology (ASM).

Methods: Biweekly 1-hour sessions were held by ASM for 2 sentinel sites, Tikur Anbessa Specialized Hospital and the EPHI Clinical Microbiology and Mycology Laboratory, using a videoconferencing platform. Each virtual session consisted of a didactic session, a case presentation by a participating laboratory, open discussion and feedback. Case presentations focused on technical challenges and problems encountered in the preanalytical, analytical, and postanalytical phases of microbiology testing. Experts from CDC and ASM provided feedback along with a summation of key learning objectives. Sessions were recorded and post session reports were shared with participants. To assess participants’ baseline knowledge, a comprehensive pretest was administered prior to the first session. The same instrument was administered as a posttest 2 weeks after the final session. Unstructured interviews were also conducted to assess participants’ perceptions of the value of ECHO to their work. Results: Mean pretest scores were 69.25% and the posttest scores were 71.04%, a difference of 1.79% (P = NS). Participant interviews revealed perceived benefits of ECHO participation to include enhanced critical thinking and problem resolution in microbiology, increased communication and improved working relationships between participating sites, and improved understanding and application of CLSI standards. As a result of Ethiopia’s participation in Project ECHO, 23 case presentations have been added to ECHO Box, a resource bank and web portal, which allows members of the ECHO community to share and access didactics, documents, and learning materials. Conclusions: Despite minimal difference between pretest and posttest scores, the Project ECHO experience of virtual case-based learning and collaborative problem solving has encouraged critical thinking, peer-to-peer learning, networking among participants, and has provided microbiologists with the resources for improved bacterial isolation, identification, and antibiotic susceptibility testing. The lessons learned could be applied as this project is expanded to additional laboratories in the AMR Surveillance Network.

Funding: None

Disclosures: None

Background: Healthcare-associated infections caused by antibiotic-resistant organisms (AROs) are a major cause of significant morbidity and mortality. To create and optimize infection prevention strategies, it is crucial to delineate the role of the environment and clinical infections. Methods: Over a 14-month period, we collected environmental samples, patient feces, and patient bloodstream infection (BSI) isolates in a newly built bone marrow transplant (BMT) intensive care unit (ICU). Samples were collected from 13 high-touch areas in the patient room and 4 communal areas. Samples were collected from the old BMT ICU, in the new BMT ICU before patients moved in, and for 1 year after patients moved in. Selective microbiologic culture was used to isolate AROs, and whole-genome sequencing (WGS) was used to determine clonality. Antibiotic susceptibility testing was performed using Kirby-Bauer disk diffusion assays. Using linear mixed modeling, we compared ARO recovery across time and sample area. Results: AROs were collected and cultured from environmental samples, patient feces, and BSI isolates (Fig. 1a). AROs were found both before and after a patient entered the ICU (Fig. 1b). Sink drains had significantly more AROs recovered per sample than any other surface area (P < .001) (Fig. 1c). The most common ARO isolates were Pseudomonas aeruginosa and Stenotrophomonas maltophila (Fig. 1d). The new BMT ICU had fewer AROs recovered per sample than the old BMT ICU (P < .001) and no increase in AROs recovered over the first year of opening (P > .05). Furthermore, there was no difference before versus after patients moved into the hospital (P > .05). Antibiotic susceptibility testing reveal that P. aeruginosa isolates recovered from the old ICU were resistant to more antibiotics than isolates recovered from the new ICU (Fig. 2a). ANI and clonal analyses of P. aeruginosa revealed a large cluster of clonal isolates (34 of 76) (Fig. 2b). This clonal group included isolates found before patients moved into the BMT ICU and patient blood isolates. Furthermore, this clonal group was initially found in only 1 room in the BMT ICU, and over 26 weeks, it was found in sink drains in all 6 rooms sampled (Fig. 2b). Conclusions: AROs are present before patients move into a new BMT ICU, and sink drains act as a reservoir for AROs over time. Furthermore, sink-drain P. aeruginosa isolates are clonally related to isolates found in patient BSIs. Overall, these results provide insight into ARO transmission dynamics in the hospital environment.

Funding: Research reported in this publication was supported by the Washington University Institute of Clinical and Translational Sciences grant UL1TR002345 from the National Center for Advancing Translational Sciences (NCATS) of the National Institutes of Health (NIH). The content is solely the responsibility of the authors and does not necessarily represent the official view of the NIH.

Disclosures: None

Background: Despite ongoing efforts over the past 3 decades, hospital-onset Clostridioides difficile infection (HO-CDI) continues to challenge interventions aimed at its prevention and control. We describe the impact of a model environmental services (EVS) program on the incidence of HO-CDI across 8 hospitals that are part of a nationwide integrated health system. Methods: Eight acute-care hospitals with 44–532 beds (mean, 263 beds) in 6 states with stable endemic HO-CDI incidence densities independently implemented identical sporicidal environmental hygiene interventions in 2017. The program combined the use of a hydrogen peroxide/peroxyacetic acid surface disinfectant for all patient-zone hygienic cleaning combined with a structured model EVS cleaning program that included optimized cleaning and disinfection technique, staff training, and auditing with objective performance feedback, which aligned with 2008 HICPAC/CDC categories I and II as well as 2010 CDC Guidance Level II monitoring program recommendations. After a 3-month phase-in, we compared NHSN-reported LabID HO-CDI SIRs for 18 months before and 12 months after implementation of the program. Results were not shared between sites and data were not collated by the authors until a year after the postintervention results were initially available. Multiple possible confounding factors were evaluated and determined not to have identifiably affected the outcome. Results: Mean preintervention HO-CDI SIRs over the 18 months measured ranged from 0.5 to 1.4 (mean, 1.0 for the group). Following the wash-in period, SIRs decreased precipitously in all sites to a mean of 0.42 for the group by the end of 12 months of the intervention. (P < .0001) (Fig. 1). Individual site improvement ranged from 20% to 92% (mean, 57%) (Fig. 2.) Conclusions: Overall, HO-CDI SIRs decreased almost 60% in the study hospitals following daily sporicidal disinfection cleaning of all patient-zone surfaces in association with ongoing programmatic optimization of cleaning practice. As predicted by earlier single-site studies reporting a favorable impact of sporicidal disinfectant cleaning in outbreak settings, this multisite quasi-experimental study has illustrated the substantial potential impact of hospital-wide sporicidal disinfection integrated with objectively sustained optimized thoroughness of cleaning to decrease the incidence of HO-CDI.

Funding: None

Disclosures: None

Background: Delayed or in vitro inactive empiric antibiotic therapy may be detrimental to survival in patients with bloodstream infections (BSIs). Understanding the landscape of delayed or discordant empiric antibiotic therapy (DDEAT) across different patient, pathogen, and hospital types, as well as by their baseline resistance milieu, may enable providers, antimicrobial stewardship programs, and policy makers to optimize empiric prescribing. Methods: Inpatients with clinically suspected serious infection (based on sampling of blood cultures and receiving systemic antibiotic therapy on the same or next day) found to have BSI were identified in the Cerner Healthfacts EHR database. Patients were considered to have received DDEAT when, on culture sampling day, they received either no antibiotic(s) or none that displayed in vitro activity against the pathogenic bloodstream isolate. Antibiotic-resistant phenotypes were defined by in vitro resistance to taxon-specific prototype antibiotics (eg, methicillin/oxacillin resistance in S. aureus) and were used to estimate baseline resistance prevalence encountered by the hospital. The probability of DDEAT was examined by bacterial taxon, by time of BSI onset, and by presence versus absence of antibiotic-resistance phenotypes, sepsis or septic shock, hospital type, and baseline resistance. Results: Of 26,036 assessable patients with a BSI at 131 US hospitals between 2005 and 2014, 14,658 (56%) had sepsis, 3,623 (14%) had septic shock, 5,084 (20%) had antibiotic-resistant phenotypes, and 8,593 (33%) received DDEAT. Also, 4,428 (52%) recipients of DDEAT received no antibiotics on culture sampling day, whereas the remaining 4,165 (48%) received in vitro discordant therapy. DDEAT occurred most often in S. maltophilia (87%) and E. faecium (80%) BSIs; however, 75% of DDEAT cases and 76% of deaths among recipients of DDEAT collectively occurred among patients with S. aureus and Enterobacteriales BSIs. For every 8 bacteremic patients presenting with septic shock, 1 patient did not receive any antibiotics on culture day (Fig. 1A). Patients with BSIs of hospital (vs community) onset were twice as likely to receive no antibiotics on culture day, whereas those with bloodstream pathogens displaying antibiotic-resistant (vs susceptible) phenotypes were 3 times as likely to receive in vitro discordant therapy (Fig. 1B). The median proportion of DDEAT ranged between 25% (14, 37%) in eight <300-bed teaching hospitals in the lowest baseline resistance quartile and 40% (31, 50%) at five ≥300-bed teaching hospitals in the third baseline resistance quartile (Fig. 2). Conclusions: Delayed or in vitro discordant empiric antibiotic therapy is common among patients with BSI in US hospitals regardless of hospital size, teaching status, or local resistance patterns. Prompt empiric antibiotic therapy in septic shock and hospital-onset BSI needs more support. Reliable detection of S. aureus and Enterobacteriales bloodstream pathogens and their resistance patterns earlier with rapid point-of-care diagnostics may mitigate the population-level impact of DDEAT in BSI.

Funding: This study was funded in part by the National Institutes of Health Clinical Center, National Institutes of Allergy and Infectious Diseases, National Cancer Institute (NCI contract no. HHSN261200800001E) and the Agency for Healthcare Research and Quality.

Disclosures: None

Background: Rapidly identifying patients colonized with multidrug-resistant organisms (MDROs) upon ICU admission is critical to control and prevent the spread of these pathogens in healthcare facilities. Electronic health records (EHR) provide a rich source of data to predict the likelihood of MDRO colonization at admission, whereas surveillance methods are resource intensive and results are not immediately available. Our objectives were (1) to predict VRE and CRO colonization at ICU admission and (2) to identify patient subpopulations at higher risk for colonization with these MDROs. Methods: We conducted a retrospective analysis of patients aged ≥16 years admitted to any of 6 medical or surgical intensive care units (ICU) in the Johns Hopkins Hospital from July 1, 2016, through June 30, 2018. Perirectal swabs were collected at ICU unit admission and were tested for VRE and CRO. Patient demographic data, prior hospitalizations, and preadmission clinical data, including prior medication administration, prior diagnoses, and prior procedures, were extracted to develop prediction models. We employed the machine-learning algorithms logistic regression (LR), random forest (RF), and XGBoost (XG). The sum of sensitivity and specificity (ie, Youden’s index) was selected as the performance metric. Results: In total, 5,033 separate ICU visits from 3,385 patients were included, where 555 (11%) and 373 (7%) admissions tested positive for VRE and CRO, respectively. The sensitivity and specificity of our models for VRE were 78% and 80% with LR, 80% and 82% with RF, and 77% and 87% with XG. Predictions for CRO were not as precise, with LR at 73% and 53%, RF at 81% and 48%, and XG at 69% and 61%. The XG algorithm was the best-performing algorithm for both VRE and CRO. Prior VRE colonization, recent (<180 days) long-term care facility stay, and prior hospitalization >60 days were the key predictors for VRE, whereas the primary predictor for CRO colonization was prior carbapenem use. Conclusions: We demonstrated that EHR data can be used to predict >75% of VRE positive cases with a <15% false-positive rate and ~70% of CRO cases with a <40% false-positive rate. Future studies using larger sample sizes may improve the prediction accuracy and inform model generalizability across sites and thus reduce the risk of transmission of MDROs by rapidly identifying MDRO-colonized patients.

Funding: This work was funded by the Centers for Disease Control and Prevention (CDC) Epicenters Program (Grant Number 1U54CK000447) and the CDC MInD-Healthcare Program (Grant Number 1U01CK000536).

Disclosures: Aaron Milstone, BD (consulting)

Background: With an aging population, increasingly complex care, and frequent re-admissions, prevention of healthcare-associated infections (HAIs) in nursing homes (NHs) is a federal priority. However, few contemporary sources of HAI data exist to inform surveillance, prevention, and policy. Prevalence surveys (PSs) are an efficient approach to generating data to measure the burden and describe the types of HAI. In 2017, the Centers for Disease Control and Prevention (CDC) performed its first large-scale HAI PS through the Emerging Infections Program (EIP) to measure the prevalence and describe the epidemiology of HAI in NH residents. Methods: NHs from several states (CA, CO, CT, GA, MD, MN, NM, NY, OR, & TN) were randomly selected and asked to participate in a 1-day HAI PS between April and October 2017; participation was voluntary. EIP staff reviewed available medical records for NH residents present on the survey date to collect demographic and basic clinical information and infection signs and symptoms. HAIs with onset on or after NH day 3 were identified using revised McGeer infection definitions applied to data collected by EIP staff and were reported to the CDC through a web-based system. Data were reviewed by CDC staff for potential errors and to validate HAI classifications prior to analysis. HAI prevalence, number of residents with >1 HAI per number of surveyed residents ×100, and 95% CIs were calculated overall (pooled mean) and for selected resident characteristics. Data were analyzed using SAS v9.4 software. Results: Among 15,296 residents in 161 NHs, 358 residents with 375 HAIs were identified. The most common HAI sites were skin (32%), respiratory tract (29%), and urinary tract (20%). Cellulitis, soft-tissue or wound infection, symptomatic UTI, and cold or pharyngitis were the most common individual HAIs (Fig. 1). Overall HAI prevalence was 2.3 per 100 residents (95% CI, 2.1–2.6); at the NH level, the median HAI prevalence was 1.8 and ranged from 0 to 14.3 (interquartile range, 0–3.1). At the resident level (Fig. 2), HAI prevalence was significantly higher in persons admitted for postacute care with diabetes, with a pressure ulcer, receiving wound care, or with a device. Conclusions: In this large-scale survey, 1 in 43 NH residents had an HAI on a given day. Three HAI types comprised >80% of infections. In addition to identifying characteristics that place residents at higher risk for HAIs, these findings provide important data on HAI epidemiology in NHs that can be used to expand HAI surveillance and inform prevention policies and practices.

Funding: None

Disclosures: None

Background: Antimicrobial prophylaxis is one of the strongest surgical site infection (SSI) prevention measures. Current guidelines recommend the use of cefazolin as antimicrobial prophylaxis for abdominal hysterectomy procedures. However, there is growing evidence that anaerobes play a role in abdominal hysterectomy SSIs. We assessed the impact of adding anaerobic coverage on abdominal hysterectomy SSI rates in our institution. Methods: The University of Iowa Hospitals & Clinics is an 811-bed academic medical center that serves as a referral center for Iowa and neighboring states. Each year, ~33,000 major surgical operations are performed here, and on average, 600 are abdominal hysterectomies. Historically, patients have received cefazolin only, but beginning November 2017, patients undergoing abdominal hysterectomy received cefazolin + metronidazole for antimicrobial prophylaxis. Order sets within the electronic medical record were modified, and education was provided to surgeons, anesthesiologists, and other ordering providers. Procedures and subsequent SSIs were monitored and reported using National Healthcare Safety Network (NHSN) definitions. Infection rates are calculated using all depths (superficial, deep and organ space) and by deep and organ space only, as this is how they are publicly reported. We used numerator (SSIs) and denominator (number of abdominal hysterectomy procedures) data from the NHSN from January 2015 through September 2019. We performed an interrupted time-series analysis to determine how the addition of metronidazole was associated with abdominal hysterectomy SSIs (all depths, and deep and organ space). Results: From January 2015 through October 2017, the hysterectomy SSI rates were 3.2% (all depths) and 1.5% (deep and organ space). After the adjustment was made to antimicrobial prophylaxis in November 2017, the rates decreased to 1.6% (all depths) and 0.6% (deep and organ space). Of the SSIs with pathogens identified, the proportion of anaerobes decreased from 59% to 25% among all depths and from 82% to 50% among deep and organ-space SSIs. The rate of SSI decline after the intervention was statistically significant (P = .01) for deep and organ-space infections but not for all depths (P = .73). Conclusions: The addition of anaerobic coverage with metronidazole was associated with a decrease in deep and organ-space abdominal hysterectomy SSI rates at our institution. Hospitals should assess the microbiology of abdominal hysterectomy SSIs and should consider adding metronidazole to their antimicrobial prophylaxis.

Funding: None

Disclosures: None

Background: US hospitals are required to report C. difficile infections (CDIs) to the NHSN as a performance measure tied to payment penalties for poor scores. Currently, only the charted CDI test results performed last in reflex testing scenarios are reported to the NHSN (CDI events). We describe the reduction in NHSN CDI events from the addition of a reflex toxin enzyme immunoassay (EIA) after a positive nucleic acid amplification test (NAAT) in teaching and nonteaching hospitals, and we estimate the impact on standardized infection ratios (SIR). Methods: Reporting of all CDI test results, by test method, occurred during April 2018–July 2019 to the Georgia Emerging Infections program (funded by the Centers for Disease Control and Prevention), which conducts active population-based surveillance in an 8-county Atlanta area (population, 4 million). Among facilities starting reflex EIA testing, results were aggregated by test method during months of reflex testing to calculate facility-specific reduction in NHSN CDI events (% reduction; 1-[no. EIA+/no. NAAT+]). Differences in percent reduction between facilities by characteristic were compared using the Kruskal-Wallis test. We simulated expected changes in the SIR for a range of reductions, assuming equal effect on both community-onset (CO) and hospital-onset (HO) tests. Each facility’s historical NHSN CDI events prior to reflex testing were used to estimate changes to facility-specific SIRs by reducing values by the corresponding facility’s percent reduction. Results: Overall, 13 acute-care hospitals (bed size, 52–633; ICU bed size, 6–105) started reflex testing during the study period (mean, 7 months, 15,800 admissions, 66,400 patient days), resulting in 550 +NAAT tests reflexing to 180 +EIA tests (pooled mean 58% reduction). Percent reduction varied (mean, 67%; range, 42%–81%) but did not differ between larger (≥217 beds) and smaller hospitals (61 vs 50% reduction; P > .05) or by outsourced versus inhouse testing (65% vs 54% reduction; P > .05). Simulations identified a threshold reduction at which point effect on HO counteract the effects on CO events enough to reduce the SIR; thresholds for nonteaching and teaching were 26% and 32% reduction, respectively (Fig. 1). The estimated reductions in facility-specific SIRs using measured percent reductions on historic NHSN CDI events closely paralleled the simulation, and the mean estimated change in SIR was −46% (range, −12% to −71%) (Fig. 1). Conclusions: Although the magnitude of the effect varied, all 13 facilities experienced dramatic reductions in CDI events reportable to NHSN due to reflex testing; applying these reductions to historical NHSN data illustrates anticipated reductions in their facility-specific SIRs due to this testing change.

Funding: None

Disclosures: Scott Fridkin, consulting fee, vaccine industry (various) (spouse)

Background: Carbapenem-resistant Enterobacteriaceae (CRE) are endemic in the Chicago region. We assessed the regional impact of a CRE control intervention targeting high-prevalence facilities; that is, long-term acute-care hospitals (LTACHs) and ventilator-capable skilled nursing facilities (vSNFs). Methods: In July 2017, an academic–public health partnership launched a regional CRE prevention bundle: (1) identifying patient CRE status by querying Illinois’ XDRO registry and periodic point-prevalence surveys reported to public health, (2) cohorting or private rooms with contact precautions for CRE patients, (3) combining hand hygiene adherence, monitoring with general infection control education, and guidance by project coordinators and public health, and (4) daily chlorhexidine gluconate (CHG) bathing. Informed by epidemiology and modeling, we targeted LTACHs and vSNFs in a 13-mile radius from the coordinating center. Illinois mandates CRE reporting to the XDRO registry, which can also be manually queried or generate automated alerts to facilitate interfacility communication. The regional intervention promoted increased automation of alerts to hospitals. The prespecified primary outcome was incident clinical CRE culture reported to the XDRO registry in Cook County by month, analyzed by segmented regression modeling. A secondary outcome was colonization prevalence measured by serial point-prevalence surveys for carbapenemase-producing organism colonization in LTACHs and vSNFs. Results: All eligible LTACHs (n = 6) and vSNFs (n = 9) participated in the intervention. One vSNF declined CHG bathing. vSNFs that implemented CHG bathing typically bathed residents 2–3 times per week instead of daily. Overall, there were significant gaps in infection control practices, especially in vSNFs. Also, 75 Illinois hospitals adopted automated alerts (56 during the intervention period). Mean CRE incidence in Cook County decreased from 59.0 cases per month during baseline to 40.6 cases per month during intervention (P < .001). In a segmented regression model, there was an average reduction of 10.56 cases per month during the 24-month intervention period (P = .02) (Fig. 1), and an estimated 253 incident CRE cases were averted. Mean CRE incidence also decreased among the stratum of vSNF/LTACH intervention facilities (P = .03). However, evidence of ongoing CRE transmission, particularly in vSNFs, persisted, and CRE colonization prevalence remained high at intervention facilities (Table 1). Conclusions: A resource-intensive public health regional CRE intervention was implemented that included enhanced interfacility communication and targeted infection prevention. There was a significant decline in incident CRE clinical cases in Cook County, despite high persistent CRE colonization prevalence in intervention facilities. vSNFs, where understaffing or underresourcing were common and lengths of stay range from months to years, had a major prevalence challenge, underscoring the need for aggressive infection control improvements in these facilities.

Funding: The Centers for Disease Control and Prevention (SHEPheRD Contract No. 200-2011-42037)

Disclosures: M.Y.L. has received research support in the form of contributed product from OpGen and Sage Products (now part of Stryker Corporation), and has received an investigator-initiated grant from CareFusion Foundation (now part of BD).

Background: Detection of unusual carbapenemase-producing organisms (CPOs) in a healthcare facility may signify broader regional spread. During investigation of a VIM-producing Pseudomonas aeruginosa (VIM-CRPA) outbreak in a long-term acute-care hospital in central Florida, enhanced surveillance identified VIM-CRPA from multiple facilities, denoting potential regional emergence. We evaluated infection control and performed screening for CPOs in skilled nursing facilities (SNFs) across the region to identify potential CPO reservoirs and improve practices. Methods: All SNFs in 2 central Florida counties were offered a facility-wide point-prevalence survey (PPS) for CPOs and a nonregulatory infection control consultation. PPSs were conducted using a PCR-based screening method; specimens with a carbapenemase gene detected were cultured to identify the organisms. Infection control assessments focused on direct observations of hand hygiene (HH), environmental cleaning, and the sink splash zone. Thoroughness of environmental cleaning was evaluated using fluorescent markers applied to 6 standardized high-touch surfaces in at least 2 rooms per facility. Results: Overall, 21 (48%) SNFs in the 2-county region participated; 18 conducted PPS. Bed size ranged from 40 to 391, 5 (24%) facilities were ventilator-capable SNFs (vSNFs), and 12 had short-stay inpatient rehabilitation units. Of 1,338 residents approached, 649 agreed to rectal screening, and 14 (2.2%) carried CPOs. CPO-colonized residents were from the ventilator-capable units of 3 vSNFs (KPC-CRE=7; KPC-CRPA=1) and from short-stay units of 2 additional facilities (VIM-CRPA, n = 5; KPC-CRE, n = 1). Among the 5 facilities where CPO colonization was identified, the prevalence ranged from 1.1% in a short-stay unit to 16.1% in a ventilator unit. All facilities had access to soap and water in resident bathrooms; 14 (67%) had alcohol-based hand rubs accessible. Overall, mean facility HH adherence was 52% (range, 37%–66%; mean observations per facility = 106) (Fig. 1). We observed the use of non–EPA-registered disinfectants and cross contamination from dirty to clean areas during environmental cleaning; the overall surface cleaning rate was 46% (n = 178 rooms); only 1 room had all 6 markers removed. Resident supplies were frequently stored in the sink splash zone. Conclusions: A regional assessment conducted in response to emergence of VIM-CRPA identified a relatively low CPO prevalence at participating SNFs; CPOs were primarily identified in vSNFs and among short-stay residents. Across facilities, we observed low adherence to core infection control practices that could facilitate spread of CPOs and other resistant organisms. In this region, targeting ventilator and short-stay units of SNFs for surveillance and infection control efforts may have the greatest prevention impact.

Funding: None

Disclosures: None

Background: Contamination of the near-patient hospital environment including work surfaces and equipment, contributes to skin colonization and subsequent invasive bacterial infections in hospitalized neonates. In resource-limited settings, cleaning of the neonatal ward environment and equipment is seldom standardized and infrequently audited. Methods: A baseline multimodal assessment of surface and equipment cleaning was performed in a 30-bed high-care neonatal ward in Cape Town, South Africa, October 7–9, 2019. Adequacy of routine cleaning was evaluated using ATP bioluminescence assays, fluorescent ultraviolet (UV) markers, and quantitative bacterial surface cultures. For flat surfaces (eg, tables, incubators, trolleys), a 10×10-cm template was used to standardize the swab inoculum; for small equipment and devices with complex surfaces (eg, humidifiers, suction apparatus, stethoscopes), a standard swabbing protocol was developed for each item. Swabs in liquid transport medium were processed in the laboratory by vortexing for 30 seconds, plating onto blood and MacConkey agars, and incubating at 37°C for 48 hours. Manual counting of bacterial colony forming units was performed, followed by conventional biochemical testing and/or VITEK automated identification. Results: Of 100 swabs (58 from surfaces and 42 from equipment), 11 yielded growth of known neonatal pathogens (Enterobacteriaceae, A. baumannii, P. aeruginosa, S. aureus, S. agalactiae, and enterococci), 36 isolated potential neonatal pathogens (mostly coagulase-negative staphylococci). In addition, 4 grew environmental organisms and 49 showed no growth. The highest aerobic colony counts (ACCs) were obtained from swabs of suction tubing, milk kitchen surfaces, humidifiers, and sinks; the median ACC from swabs with any bacterial growth (n = 51) was 3 (IQR, 1–22). Only 40% of the 100 surface and equipment swabs had ATP values <200 relative light units (RLU) threshold for cleanliness. Median ATP values were 301 (IQ range, 179–732) RLUs for surface swabs versus 230 (IQ range, 78–699) RLUs for equipment swabs (P = .233). Of the 100 fluorescent UV markers placed on near-patient surfaces and high-touch equipment, only 23% had been removed after 2 staff shift changes (24 hours later). Surfaces had a higher proportion of UV marker removal than equipment (19 of 58 [32.8%] vs 4 of 42 [9.5%]; P = .008). Conclusions: Environmental cleaning of this neonatal ward was suboptimal, especially for equipment. Improvement of environmental cleaning practices is an important intervention for neonatal infection prevention in resource-limited settings. Future studies should evaluate the impact of staff training, environmental cleaning tools and repeated audit with feedback, on the adequacy of cleaning in neonatal wards.

Funding: Funding: for the laboratory work was provided by The Society for Healthcare Epidemiology of America (SHEA) International Ambassador Alumni Research Award and a South African Medical Research Council Self-initiated Research (SIR) Grant to Angela Dramowski, who is supported by a NIH Fogarty Emerging Global Leader Award K43 TW010682.

Disclosures: None