To determine the proportion of hospitals that implemented 6 leading practices in their antimicrobial stewardship programs (ASPs). Design: Cross-sectional observational survey.

Acute-care hospitals.

ASP leaders.

Advance letters and electronic questionnaires were initiated February 2020. Primary outcomes were percentage of hospitals that (1) implemented facility-specific treatment guidelines (FSTG); (2) performed interactive prospective audit and feedback (PAF) either face-to-face or by telephone; (3) optimized diagnostic testing; (4) measured antibiotic utilization; (5) measured C. difficile infection (CDI); and (6) measured adherence to FSTGs.

Of 948 hospitals invited, 288 (30.4%) completed the questionnaire. Among them, 82 (28.5%) had <99 beds, 162 (56.3%) had 100–399 beds, and 44 (15.2%) had ≥400+ beds. Also, 230 (79.9%) were healthcare system members. Moreover, 161 hospitals (54.8%) reported implementing FSTGs; 214 (72.4%) performed interactive PAF; 105 (34.9%) implemented procedures to optimize diagnostic testing; 235 (79.8%) measured antibiotic utilization; 258 (88.2%) measured CDI; and 110 (37.1%) measured FSTG adherence. Small hospitals performed less interactive PAF (61.0%; P = .0018). Small and nonsystem hospitals were less likely to optimize diagnostic testing: 25.2% (P = .030) and 21.0% (P = .0077), respectively. Small hospitals were less likely to measure antibiotic utilization (67.8%; P = .0010) and CDI (80.3%; P = .0038). Nonsystem hospitals were less likely to implement FSTGs (34.3%; P < .001).

Significant variation exists in the adoption of ASP leading practices. A minority of hospitals have taken action to optimize diagnostic testing and measure adherence to FSTGs. Additional efforts are needed to expand adoption of leading practices across all acute-care hospitals with the greatest need in smaller hospitals.

We compared the activity of 8 novel β-lactam and tetracycline-derivative antibiotics against a cohort of clinical carbapenem-resistant Enterobacterales (CRE) isolates and investigated the incremental susceptibility benefit of the addition of an aminoglycoside, fluoroquinolone, or polymyxin to the β-lactam agents to assist with empiric antibiotic decision making.

A collection of consecutive CRE clinical isolates from unique patients at 3 US hospitals (2016–2021) was assembled. Broth microdilution was performed to obtain antimicrobial susceptibility testing results. Mechanisms of carbapenem resistance were investigated through short-read and long-read whole-genome sequencing.

Of the 603 CRE isolates, 276 (46%) were carbapenemase producing and 327 (54%) were non–carbapenemase producing, respectively. The organisms most frequently identified were Klebsiella pneumoniae (38%), Enterobacter cloacae complex (26%), and Escherichia coli (16%). We obtained the following percent susceptibility to novel β-lactam agents: ceftazidime-avibactam (95%), meropenem-vaborbactam (92%), imipenem-relebactam (84%), and cefiderocol (92%). Aminoglycosides and the polymyxins provided greater incremental coverage as second agents, compared to fluoroquinolones. Amikacin and plazomicin exhibited the greatest additive value. Ceftazidime-avibactam, meropenem-vaborbactam, and cefiderocol were active against 94% of the 220 KPC-producing isolates. Cefiderocol was active against 83% of the 29 NDM-producing isolates. Ceftazidime-avibactam had 100% activity against the 9 OXA-48-like–producing isolates. Tigecycline had the highest activity compared to other tetracyclines against KPC, NDM, or OXA-48-like–producing isolates.

Selection among novel agents requires a nuanced understanding of the molecular epidemiology of CRE. This work provides insights into the comparative activity of novel agents and the additive value of a second antibiotic for empiric antibiotic decision making.

To develop a pediatric research agenda focused on pediatric healthcare-associated infections and antimicrobial stewardship topics that will yield the highest impact on child health.

The study included 26 geographically diverse adult and pediatric infectious diseases clinicians with expertise in healthcare-associated infection prevention and/or antimicrobial stewardship (topic identification and ranking of priorities), as well as members of the Division of Healthcare Quality and Promotion at the Centers for Disease Control and Prevention (topic identification).

Using a modified Delphi approach, expert recommendations were generated through an iterative process for identifying pediatric research priorities in healthcare associated infection prevention and antimicrobial stewardship. The multistep, 7-month process included a literature review, interactive teleconferences, web-based surveys, and 2 in-person meetings.

A final list of 12 high-priority research topics were generated in the 2 domains. High-priority healthcare-associated infection topics included judicious testing for Clostridioides difficile infection, chlorhexidine (CHG) bathing, measuring and preventing hospital-onset bloodstream infection rates, surgical site infection prevention, surveillance and prevention of multidrug resistant gram-negative rod infections. Antimicrobial stewardship topics included β-lactam allergy de-labeling, judicious use of perioperative antibiotics, intravenous to oral conversion of antimicrobial therapy, developing a patient-level “harm index” for antibiotic exposure, and benchmarking and or peer comparison of antibiotic use for common inpatient conditions.

We identified 6 healthcare-associated infection topics and 6 antimicrobial stewardship topics as potentially high-impact targets for pediatric research.

Targeted screening for carbapenem-resistant organisms (CROs), including carbapenem-resistant Enterobacteriaceae (CRE) and carbapenemase-producing organisms (CPOs), remains limited; recent data suggest that existing policies miss many carriers.

Our objective was to measure the prevalence of CRO and CPO perirectal colonization at hospital unit admission and to use machine learning methods to predict probability of CRO and/or CPO carriage.

We performed an observational cohort study of all patients admitted to the medical intensive care unit (MICU) or solid organ transplant (SOT) unit at The Johns Hopkins Hospital between July 1, 2016 and July 1, 2017. Admission perirectal swabs were screened for CROs and CPOs. More than 125 variables capturing preadmission clinical and demographic characteristics were collected from the electronic medical record (EMR) system. We developed models to predict colonization probabilities using decision tree learning.

Evaluating 2,878 admission swabs from 2,165 patients, we found that 7.5% and 1.3% of swabs were CRO and CPO positive, respectively. Organism and carbapenemase diversity among CPO isolates was high. Despite including many characteristics commonly associated with CRO/CPO carriage or infection, overall, decision tree models poorly predicted CRO and CPO colonization (C statistics, 0.57 and 0.58, respectively). In subgroup analyses, however, models did accurately identify patients with recent CRO-positive cultures who use proton-pump inhibitors as having a high likelihood of CRO colonization.

In this inpatient population, CRO carriage was infrequent but was higher than previously published estimates. Despite including many variables associated with CRO/CPO carriage, models poorly predicted colonization status, likely due to significant host and organism heterogeneity.

Timely identification of multidrug-resistant gram-negative infections remains an epidemiological challenge. Statistical models for predicting drug resistance can offer utility where rapid diagnostics are unavailable or resource-impractical. Logistic regression–derived risk scores are common in the healthcare epidemiology literature. Machine learning–derived decision trees are an alternative approach for developing decision support tools. Our group previously reported on a decision tree for predicting ESBL bloodstream infections. Our objective in the current study was to develop a risk score from the same ESBL dataset to compare these 2 methods and to offer general guiding principles for using each approach.

Using a dataset of 1,288 patients with Escherichia coli or Klebsiella spp bacteremia, we generated a risk score to predict the likelihood that a bacteremic patient was infected with an ESBL-producer. We evaluated discrimination (original and cross-validated models) using receiver operating characteristic curves and C statistics. We compared risk score and decision tree performance, and we reviewed their practical and methodological attributes.

In total, 194 patients (15%) were infected with ESBL-producing bacteremia. The clinical risk score included 14 variables, compared to the 5 decision-tree variables. The positive and negative predictive values of the risk score and decision tree were similar (>90%), but the C statistic of the risk score (0.87) was 10% higher.

A decision tree and risk score performed similarly for predicting ESBL infection. The decision tree was more user-friendly, with fewer variables for the end user, whereas the risk score offered higher discrimination and greater flexibility for adjusting sensitivity and specificity.