Sara E. Cosgrove

Objectives: This study aimed to characterize susceptibility profiles of both currently approved and investigational β-lactam agents against difficult-to-treat resistant Pseudomonas aeruginosa (DTR P. aeruginosa) clinical isolates, including carbapenemase-producing strains. Patients and methods: A total of 502 consecutive DTR P. aeruginosa clinical isolates were collected from unique hospitalized patients between 2022 and 2024 across three tertiary care centres. Isolates originated from bloodstream, respiratory, urinary and intra-abdominal infections. Minimum inhibitory concentrations (MICs) for seven β-lactam agents were determined using triplicate reference broth microdilution. Susceptibility interpretations were based on CLSI criteria or, where applicable, investigational breakpoints. Whole-genome sequencing was performed to detect β-lactamase genes. Results: Among the 502 isolates, cefepime-zidebactam exhibited the highest in vitro activity (100% susceptibility), followed by cefiderocol-xeruborbactam (98%), cefiderocol (95%), ceftolozane-tazobactam (90%), cefepime-taniborbactam (86%), ceftazidime-avibactam (85%), and imipenem-relebactam (41%). Notably, only 35% of isolates were susceptible to all seven agents. Carbapenemase genes were identified in 4% of isolates, including blaGES (n = 13), blaVIM (n = 7), blaKPC (n = 1) and blaIMP (n = 1). Among carbapenemase-producing isolates, cefepime-zidebactam retained activity against all isolates (100%), exceeding that of other approved β-lactam/β-lactamase inhibitor combinations (0%–24% susceptible), cefiderocol (76%), cefiderocol-xeruborbactam (86%) and cefepime-taniborbactam (81%). Conclusions: While currently approved β-lactam agents retained activity against many DTR P. aeruginosa isolates, their performance was markedly compromised in the presence of carbapenemases, with the notable exceptions of cefepime-zidebactam and cefiderocol. Investigational β-lactam/β-lactamase inhibitor combinations exhibited consistently robust activity across resistance phenotypes, including carbapenemase-producing isolates, representing promising options for DTR P. aeruginosa infections.

Background: Desirability of outcome ranking (DOOR), a paradigm for the design, analysis, and interpretation of clinical trials based on patient-centered benefit-risk evaluation, could help inform drug development for acute bacterial skin and skin structure infections (ABSSSIs). Method: A multidisciplinary committee created an infectious diseases DOOR endpoint, incorporating clinical response, infectious complications, serious adverse events, and mortality, and adapted this for ABSSSI. We retrospectively applied DOOR to 2 previous ABSSSI randomized controlled registrational trials (OASIS-1 and OASIS-2), both comparing omadacycline with linezolid. We evaluated the DOOR distribution and estimated the probability of having a more desirable outcome with omadacycline compared with linezolid. For OASIS-2, we used the 36-Item Short Form Health Survey Version 2 (SF-36v2) scores as a “tiebreaker” for participants who would otherwise have the same DOOR outcome. We performed DOOR component, partial credit, and subgroup analyses. Results: The ABSSSI DOOR demonstrated similar overall clinical outcomes between treatment arms for OASIS-1 and OASIS-2. The probability of a more desirable outcome for a participant treated with omadacycline, compared with linezolid, was 50.6% (95% confidence interval [CI], 47.4%–53.7%) and 52.1% (95% CI, 49.2%–55.0%) in OASIS-1 and OASIS-2, respectively. In OASIS-2, applying a health-related quality-of-life score as a tiebreaker did not significantly change the DOOR probability. Partial credit and subgroup analyses demonstrated similar overall results. Conclusions: DOOR may provide additional perspective on benefit versus risk in individual patients in ABSSSI trials. Future trials using DOOR prospectively will continue inform how to incorporate patient-centered outcomes in ABSSSI trials.

Antimicrobial stewardship programs (ASPs) optimize antimicrobial use to ensure the best patient outcomes from infections. Since the initial publication of this guidance in 2014, accreditation standards have bolstered antimicrobial stewardship requirements in various healthcare settings, and antibiotic use and resistance data reporting requirements have further increased the scope and resource needs of ASPs. The purpose of this guidance is to outline and update the knowledge and skills necessary to lead ASPs, organized by basic, intermediate, and advanced skills recommended for stewards practicing in acute care, outpatient care, and long-term care settings. This update was developed by a multidisciplinary group of antimicrobial stewardship experts from key organizations and with diverse backgrounds engaged in advancing the practice of antimicrobial stewardship. It does not specifically address healthcare resources, staffing models, reporting structures, or percentage of full-time equivalents needed to conduct this work, which will be addressed by future multi-society publications.

Objective: To identify system-level barriers to effective environmental cleaning and disinfection in long-term care (LTC) facilities. Design: Transdisciplinary, mixed-methods approach using human factors engineering and environmental sampling to assess environmental cleaning practices and associated multi-drug resistant organism (MDRO) burden in LTC patient rooms and common areas. Setting: Two high-acuity skilled nursing facilities in Maryland. Methods: Environmental sampling of high-touch surfaces (HTSs) was performed in resident rooms, communal areas, and nursing stations. Samples were analyzed for MDROs and overall bacterial burden. System-level barriers were assessed through observations of cleaning and focus groups with environmental services (EVS) staff and supervisors, guided by the Systems Engineering Initiative for Patient Safety (SEIPS) model. Results: Of 123 composite samples of environmental surfaces, 76% of patient room samples were culture positive for 1+ MDRO, with MRSA the most prevalent organism. Quantitative bacterial burden was higher from Composite 1 (the area in the room closest to the resident) than any other composite in the resident room (median of 27.17 CFU/mL/cm ² vs 4.55 (near door) and 1.22 (bathroom)). Observations indicated significant deviations from cleaning protocols, with only 43% of HTSs cleaned per facility policy. Focus group discussions highlighted systemic challenges, including inconsistent training, frequent interruptions, and poor communication among staff. Conclusions: The findings underscore the burden of MDRO contamination in LTC settings and critical barriers to effective environmental cleaning. Addressing these issues through standardized training with feedback mechanisms, enhanced communication, and leadership engagement is essential for improving infection prevention efforts and resident safety in LTC facilities.

Objectives: To evaluate the implementation and effectiveness of a novel home infusion central line-associated bloodstream infection (CLABSI) and home infusion-onset bloodstream infection (HiOB) dashboard and prevention toolkit. Design: Mixed methods study. Setting and Participants: Five home infusion agencies participating in the first CLABSI prevention collaborative. Methods: Agencies uploaded CLABSI and HiOB data to a comparative dashboard. The dashboard started in December 2022 and accepted retrospective data from June 2021. A CLABSI prevention toolkit was made available in June 2024. Using an interrupted time series, we present CLABSI and HiOB rates before and after dashboard and toolkit implementation. We surveyed and interviewed participants about the tools and toolkit, using directed content analysis to analyze the interviews. Results: After dashboard implementation, there was a decrease in CLABSI (−0.23/10,000 home-CVC days, 95% CI −0.28 to −0.18) and HiOB (−0.25/10,000 home-CVC days, 95% CI: −0.31 to −0.18) over time. With toolkit implementation, there was a further decrease in CLABSI (−0.17/10,000 home-CVC days, 95% CI: −0.30 to −0.044) and HiOB (−0.23/10,000 home-CVC days, 95% CI: −0.37 to −0.089) over time. Themes were associated with use of the tools (accessible, adaptable, patient-centered tools; user-friendly education to enhance understanding; barriers identified; tool mismatches; and strategies for tool delivery) and toolkit implementation (structural barriers, user-centered design, collaborative engagement and communication, toolkit used to enhance workforce competency, and concerns related to consistency). Conclusions: Implementation of a dashboard and a CLABSI prevention toolkit were each associated with both CLABSI and HiOB reduction in a collaborative of home infusion agencies.

Background: External comparisons of hospital antimicrobial use (AU), risk-adjusted using encounter characteristics, may better inform antimicrobial stewardship program strategy. Barriers to encounter-level modeling include feasibility of data collection and defining optimal methods for selecting input variables for risk-adjustment purposes. Methods: We measured achievements in sharing validated, encounter-level AU data among a multisystem hospital collaborative. Then, we performed retrospective analyses to compare variable selection strategies for AU risk-adjustment models. Electronic health record data from 50 US hospitals from 2020 to 2021 were split for model training and testing. Four input variable strategies were compared: (1) diagnosis-related group categories, (2) adjudicated Elixhauser comorbidity categories, (3) agnostic strategy including all diagnosis and procedure categories from AHRQ's Clinical Classification Software Refined (CCSR), and (4) adjudicated strategy where CCSR categories not appropriate for risk-adjustment were excluded by expert consensus. Gradient-boosted machine tree-based models estimated antibacterial days of therapy (DOT). Accuracy was measured for each strategy using mean absolute error (MAE); correlation plots compared model estimates and observed DOT among testing encounters. The top 20 most influential variables were defined using model variable importance. Results: Fifty of 76 hospitals successfully shared validated datasets using local resources. MAE was lowest for modeling strategies with larger numbers of CCSR inputs. Agnostic and adjudicated strategies had highly correlated estimates and similar influential variables. Conclusions: Expert adjudication required personnel effort and potentially introduced biases, yet did not produce results different from an agnostic approach. Risk-adjustment incorporating large encounter-level data and machine learning may prove feasible and meaningful in future hospital AU assessments.

This manuscript presents key advances of the Antibacterial Resistance Leadership Group (ARLG) Innovations Task Force, a collaboration focused on improving endpoints for registrational trials of antibacterial drugs, including health-related quality-of-life measures, for common acute infections. This group includes members from the United States Food and Drug Administration, National Institutes of Health, academia, industry, and patient representatives to amplify patient voices and ensure that the endpoints are meaningful to how patients feel, function, and survive.

Pneumonia is a frequent cause of intensive care unit (ICU) admission and is the most common infection in ICU patients across all geographic regions. It takes 48-72h for most patients to respond to appropriate antibiotic therapy. Non-response is typically defined as the persistence/worsening of clinical signs—such as fever, respiratory distress, impaired oxygenation and/or radiographic abnormalities—with rates ranging 20–30%. Several factors can contribute to non-response. Host factors, including immunosuppression, chronic lung disease, or ongoing aspiration, may impair resolution. Additionally, incorrect antibiotic dosing, atypical or resistant pathogens (such as multidrug-resistant bacteria, Mycobacterium tuberculosis, or fungal infections) may be responsible, requiring alternative antimicrobial strategies. A septic complication related to pneumonia (e.g., empyema) or not (e.g., acalculous cholecystitis) may need to be excluded. Finally, non-infectious conditions (e.g., pulmonary embolism, malignancy, secondary ARDS or vasculitis) that can mimic or potentiate pneumonia must be considered. Although non-responding pneumonia is frequent, its management lacks strong evidence, and its approach is based mostly on the art of medicine and clinical judgement. Clinicians should continuously reassess the medical history and physical exam, review microbiological data, and consider imaging such as chest CT. Bronchoscopy or repeat sputum sampling may aid in identifying alternative pathogens or non-infectious causes. The management of a non-responding pneumonia depends on the findings of a structured reassessment. Herein, we provide guidance on how to identify and manage non-responding pneumonia. Ultimately, addressing pneumonia that does not respond to antibiotics is crucial for preventing complications, optimizing antimicrobial stewardship, and improving patient outcomes.