The dissemination and implementation (D&I) of evidence at the community level is critical to improve health and advance health equity. Social networks are considered essential to D&I efforts, but there lacks clarity regarding how best to study and leverage networks. We examined networks in community-level D&I frameworks to characterize the range of network actors, activities, and change approaches. We conducted a narrative review of 66 frameworks. Among frameworks that explicitly addressed networks – that is, elaborated on network characteristics, structure, and/or activities – we extracted and synthesized network concepts using descriptive statistics and narrative summaries. A total of 24 (36%) frameworks explicitly addressed networks. Commonly included actors were implementers, adopters/decision-makers, innovation developers, implementation support professionals, and innovation recipients. Network activities included the exchange of resources, knowledge, trust, and norms. Most network-explicit frameworks characterized ties within and across organizations and considered element(s) of network structure – for example, size, centrality, and density. The most common network change strategy was identifying individuals to champion D&I efforts. We discuss opportunities to expand network inquiry in D&I science, including understanding networks as implementation determinants, leveraging network change approaches as implementation strategies, and exploring network change as an implementation outcome.

Our interviews of inpatient clinicians (physicians, physician assistants) modeled after the Capability, Opportunity, and Motivation Model of Behavior model revealed opportunity and motivation as important drivers for overdiagnosis and overprescribing for asymptomatic bacteriuria in older adults. Understanding these barriers is an important step toward implementing age-friendly stewardship interventions.

Background: Blood cultures are commonly ordered for patients with low risk of bacteremia. Liberal blood-culture ordering increases the risk of false-positive results, which can lead to increased length of stay, excess antibiotics, and unnecessary diagnostic procedures. We implemented a blood-culture indication algorithm with data feedback and assessed the impact on ordering volume and percent positivity. Methods: We performed a prospective cohort study from February 2022 to November 2022 using historical controls from February 2020 to January 2022. We introduced the blood-culture algorithm (Fig. 1) in 2 adult surgical intensive care units (ICUs). Clinicians reviewed charts of eligible patients with blood cultures weekly to determine whether the blood-culture algorithm was followed. They provided feedback to the unit medical directors weekly. We defined a blood-culture event as ≥1 blood culture within 24 hours. We excluded patients aged <18 years, absolute neutrophil count <500, and heart and lung transplant recipients at the time of blood-culture review. Results: In total, 7,315 blood-culture events in the preintervention group and 2,506 blood-culture events in the postintervention group met eligibility criteria. The average monthly blood-culture rate decreased from 190 blood cultures per 1,000 patient days to 142 blood cultures per 1,000 patient days (P < .01) after the algorithm was implemented. (Fig. 2) The average monthly blood-culture positivity increased from 11.7% to 14.2% (P = .13). Average monthly days of antibiotic therapy (DOT) was lower in the postintervention period than in the preintervention period (2,200 vs 1,940; P < .01). (Fig. 3) The ICU length of stay did not change before the intervention compared to after the intervention: 10 days (IQR, 5–18) versus 10 days (IQR, 5–17; P = .63). The in-hospital mortality rate was lower during the postintervention period, but the difference was not statistically significant: 9.24% versus 8.34% (P = .17). The all-cause 30-day mortality was significantly lower during the intervention period: 11.9% versus 9.7% (P < .01). The unplanned 30-day readmission percentage was significantly lower during the intervention period (10.6% vs 7.6%; P < .01). Over the 9-month intervention, we reviewed 916 blood-culture events in 452 unique patients. Overall, 74.6% of blood cultures followed the algorithm. The most common reasons overall for ordering blood cultures were severe sepsis or septic shock (37%), isolated fever and/or leukocytosis (19%), and documenting clearance of bacteremia (15%) (Table 1). The most common indications for inappropriate blood cultures were isolated fever and/or leukocytosis (53%). Conclusions: We introduced a blood-culture algorithm with data feedback in 2 surgical ICUs and observed decreases in blood-culture volume without a negative impact on ICU LOS or mortality rate.

Disclosure: None

Background: Antimicrobial stewardship strategies must be tailored to effectively engage prescribers with distinct training, experiences, and career paths. Advanced practice providers (APPs) have taken on increasing roles as primary team members in acute-care hospitals, but the impact of this practice shift on antimicrobial prescribing is unknown. We describe longitudinal trends in antimicrobial days of therapy (DOT) by attributed provider type in 3 hospitals. Methods: We performed a retrospective time-series analysis of antimicrobial use for the 7-year period of July 2015–June 2022 to investigate the changes by provider type at 3 hospitals: a major university hospital and 2 community hospitals. DOT, antibacterial, and antifungal agent groups were defined using National Healthcare Safety Network methods. We included anti-influenza and antiherpesvirus agents in the antiviral group. We defined protected agents as those targeted by hospital antimicrobial stewardship program policy (eg, requiring preauthorization). Provider type was defined by electronic health record user profiles in 3 categories: physician, trainees (residents, fellows and medical students), and APPs (nurse practitioners, physician assistants, and nurse anesthetists). We evaluated DOT per 1,000 days present over time by agent group to assess quarterly rate trends. Then, we calculated the percentage of total DOT by provider group. We used multinomial logistic regression to measure changes in percentage DOT across the clinician groups over time using physicians as the referent. Results: Across hospitals and provider groups, we observed an overall decrease in use rates for antibacterial and protected agents (17% each) and increased use rates for antiviral agents (38%) and antifungal agents (4%) (Table 1). Baseline distribution of DOT by provider group and change in distribution over time varied by hospital and agent group (Fig. and Table 2). The largest increases in percentage DOT attributed to APPs compared with physicians occurred in the university hospital with the following average increases per quarter: 1.5% for antibacterials, 3.9% for antivirals, 3.3% for antifungals, and 3.8% for protected agents (Table 3). Community hospitals had higher initial percentage DOT attributed to physicians, but both hospitals experienced increased percentage DOT attributed to APPs. Percentage DOT attributed to trainees varied significantly across agent groups and hospitals. Conclusions: Hospitals had differing baseline patterns of DOT attributed to provider groups, but all experienced increases in DOT attributed to APPs. APPs have increasing involvement in antimicrobial use decisions and should be engaged in future antimicrobial stewardship initiatives.

Disclosures: None

We performed surveillance for hospital-acquired COVID-19 (HA-COVID-19) and compared time-based, electronic definitions to real-time adjudication of the most likely source of acquisition. Without real-time adjudication, nearly 50% of HA-COVID-19 cases identified using electronic definitions were misclassified. Both electronic and traditional contact tracing methods likely underestimated the incidence of HA-COVID-19.

We reviewed the sustainability of a multifaceted intervention on catheter-associated urinary tract infection (CAUTI) in 3 intensive care units. During the 4-year postintervention period, we observed reductions in urine culture rates (from 80.9 to 47.5 per 1,000 patient days; P < .01), catheter utilization (from 0.68 to 0.58; P < .01), and CAUTI incidence rates (from 1.7 to 0.8 per 1,000 patient days; P = .16).

Background: Due to limited therapeutic options and potential for spread, carbapenem-resistant Enterobacteriaceae (CRE)-producing New Delhi metallo-β-lactamases (NDMs) are a public health priority. We investigated the epidemiology of NDM-producing CRE reported to the CDC to clarify its distribution and relative prevalence. Methods: The CDC’s Antibiotic Resistance Laboratory Network supports molecular testing of CRE for 5 carbapenemases nationally. Although KPC is the most common carbapenemase in the United States, non-KPC carbapenemases are a growing concern. We analyzed CRE with any of 4 non-KPC plasmid-mediated carbapenemases (NDM, VIM, IMP, or OXA-48 type) isolated from specimens collected from January 1, 2017, through June 30, 2019; only a patient’s first isolate per organism–carbapenemase combination was included. We excluded isolates from specimen sources associated with colonization screening (eg, perirectal). We compared the proportion of NDM-producing CRE to all non-KPC–producing CP-CRE between period A (January to June 2018) and period B (January to June 2019). Health departments and the CDC collected additional exposure and molecular information in selected states to better describe current NDM-producing CRE epidemiology. Results: Overall, 47 states reported 1,013 non–KPC-producing CP-CRE (range/state, 1–109 isolates; median, 11 isolates); 46 states reported 631 NDM-producing CRE (range/state, 1–84; median, 6). NDM-producing CRE increased quarterly from the third quarter of 2018 through the second quarter of 2019; CP-CRE isolates with other non-KPC carbapenemases remained stable (Fig. 1). In period A, 124 of 216 emerging CP-CRE had NDM (57.1%), compared with 255 of 359 emerging CP-CRE (71.0%) during period B (P = .1179). Among NDM-producing CRE, the proportion of Enterobacter spp increased from 10.5% in 2018 to 18.4% in 2019 (P = .0467) (Fig. 2). In total, 18 states reported more NDM-producing CRE in the first 6 months of 2019 than in all of 2018. Connecticut, Ohio, and Oregon were among states that conducted detailed investigations; these 3 states identified 24 NDM-producing CRE isolates from 23 patients in period B. Overall, 5 (21.7%) of 22 patients with history available traveled internationally ≤12 months prior to culture; 17 (73.9%) acquired NDM-producing CRE domestically. Among 15 isolates sequenced, 8 (53.3%) carried NDM-5 (6 E. coli, 1 Enterobacter spp and 1 Klebsiella spp) and 7 (46.7%) carried NDM-1 (6 Enterobacter spp and 1 Klebsiella spp). Species were diverse; no single strain type was shared by >2 isolates. Conclusions: Detection of NDM-producing CRE has increased across the AR Lab Network. Among states with detailed information available, domestic acquisition was common, and no single variant or strain predominated. Aggressive public health response and further understanding of current US NDM-CRE epidemiology are needed to prevent further spread.

Disclosures: None

Funding: None

Background: Antibiotic prophylaxis choice for transrectal prostate biopsy (TRPB) has been affected by the emergence of fluoroquinolone-resistant Escherichia coli (FQRE). Prebiopsy FQRE screening and targeted antibiotic prophylaxis may reduce post-TRPB bloodstream infection (BSI). We assessed the impact of a FQRE screening program on post-TRPB BSIs at an academic medical center. Methods: We implemented a FQRE screening program and targeted TRPB antimicrobial prophylaxis guidelines on May 1, 2017 (Fig. 1). We performed a retrospective cohort study of all TRPB and compared the incidence of post-TRPB BSI (within 7 calendar days) per 100 procedures before the intervention (January, 1, 2016, to April 30, 2017) and to the incidence after the intervention (May 1, 2017, to August 31, 2019). We used a subanalysis to compare BSI incidence between patients with positive (+) and negative () FQRE screens and appropriate prophylaxis use, defined as administration of guideline-recommended antibiotics. The Fisher exact test of independence was used to analyze nominal data. Results: The analysis included 2,157 TRPB procedures: 647 in the preintervention period and 1,510 in the postintervention period. FQRE screening compliance was 61% (n = 914) in the postintervention group (Fig. 2); 168 FQRE screens (18%) were positive. The median time from FQRE screen to procedure was 40 days (IQR, 13–69). Postprocedure BSI rates were higher in than those in the preimplementation group; however, this difference was not statistically significant (0.86 vs 0.46; OR, 2.01; P = .42). Among FQRE-screened patients, BSI rates differed significantly between FQRE+ and FQRE patients (2.98 vs 0.54; OR, 5.67; 95% CI, 1.21–28.94; P = .01). Screened patients receiving appropriate prophylaxis had lower BSI rates than those receiving inappropriate prophylaxis; however, this was not statistically significant (1.10 vs 2.02; OR, 0.54; P = .35). The most common BSI pathogen was E. coli (2 (67%) before implementation and 10 (77%) after implementation). Also, 5 E. coli BSIs (50%) were fluoroquinolone resistant in the postimplementation group compared to 1 (33%) in the preimplementation group. Of 13 postimplementation BSIs, 6 occurred in patients who received aminoglycosides perioperatively; however, all 6 BSI pathogens were aminoglycoside sensitive. Conclusions: Compliance with our FQRE screening program and antimicrobial prophylaxis protocol was moderate. Although pre- and postimplementation differences in BSI rates were not statistically significant, the high failure rate among patients receiving aminoglycosides was concerning and led to a change in TRPB prophylaxis guidelines. Reasons for increased BSI risk among FQRE+ patients may include prophylaxis agent, dose, timing, or other confounding factors associated with drug-resistant pathogens. Facilities implementing FQRE screening protocols should evaluate the efficacy of their program and periodically review screening compliance, prophylaxis dosing and timing adherence, and impact on patient-level outcomes.

Funding: None

Disclosures: None

My subject is two fragments of rhetorical commentary that appear both in an anonymous manuscript collection of quotations ‘From Longinus’ and (without attribution) in Photius' Bibliotheca. My purpose is to clarify some observations that have been made on them by modern scholars and thus offer a correction or two. The collection of separate quotations labelled κ τν Λογγνου in Laurentianus 24, Plut. 58, was given its title by a later hand different from that of the writer of the original. The grounds for that mistaken heading were that the name ‘Longinus’ appears in the second of the fragments. Aristotle and Theophrastus, however, are cited in other quotations, and most are anonymous, with as yet no particular resemblance found to wording in other rhetorical literature. Each begins with the word ὅτι. All are general remarks on subjects such as rhetorical tropes, style, and different types of speeches. They are, in effect, scholia without a text.