Background: Identified risk factors for Candida auris infection are generally markers of disease severity and/or medical vulnerability. A regional C. auris outbreak in 2022 led to extensive admission and point-prevalence testing in tertiary care hospital. We retrospectively reviewed charts to identify novel risk factors for progression from colonization to infection in a universally medically complex population. Methods: A case-control study was performed to compare patients developing invasive infection (cases) from those remaining colonization. Inclusion criteria were patients newly identified to be colonized with C. auris during a hospital admission from 1/2023 through 5/2025. Patients diagnosed with clinical infection as their first evidence of colonization were excluded. Risk factor variables included ICU status, other drug resistant organisms (MDROs), length of stay (LOS), time to colonization (days from admission to identification of C. auris), antimicrobial coverage with broadly acting agents, operative and other procedures, type/number of devices. Factors were compared between cases and controls using Fisher’s Exact Tests for categorical variables and Mann-Whitney U Tests for continuous variables using SAS 9.4. Results: During the study, 74 patients became colonized and 17 went on to develop invasive infection. The following were associated with infection: colonization with other MDROs, tracheostomy, liver failure, dialysis (though not renal failure), transplant w/in last year (driven by liver, N=7), number of drains at colonization (table 1). Several factors associated with severity of illness (ICU status, antibiotic pressure) were not associated with progression to infection in this cohort. Discussion: In a medically complex population, several risk factors associated with illness severity were not associated with progression from C. auris colonization to infection, whereas liver disease and transplantation, along with other MDROs, tracheostomy, and number of drains were. Targeted interventions attempting to decolonize tracheostomy or drain sites, and/or liver disease patients in general may help prioritize infection prevention where it is most needed.

Background: Healthcare facility-level wastewater surveillance (WWS) is a relatively novel tool for pathogen surveillance. The most sensitive methods for detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in wastewater remain unknown. In this study at skilled nursing facilities (SNFs), two wastewater concentration methods were evaluated for SARS-CoV-2 detection by droplet digital polymerase chain reaction (ddPCR): Electronegative membrane filtration (enMF) and Nanotrap® Magnetic Virus Particles (NP). SARS-CoV-2 case surveillance data from the SNFs was incorporated into this evaluation. Methods: Wastewater samples were collected weekly from manholes at three SNFs over 25 weeks in 2022. Samples were split and concentrated by both enMF and NP, then virus concentration was determined by ddPCR. We utilized a weekly questionnaire and each facility’s National Healthcare Safety Network COVID-19 Report to identify the number of resident and healthcare personnel cases in each facility. Differences in the median SARS-CoV-2 concentration were compared across the enMF and NP methods using the Wilcoxon signed-rank test (?=0.05). Spearman’s correlation (?) was used to evaluate the relationship between the SARS-CoV-2 concentration in wastewater and SARS-CoV-2 case counts. Results: For each facility separately, as well as overall, median virus concentration in wastewater from enMF was higher than that from NP (SNF A: 2.84 vs. 1.49 genome copies [gc]/100 mL; SNF B: 8.50 vs. 6.08 gc/100 mL; SNF C: 5.3 vs. 3.86 gc/100 mL; overall: 5.3 vs. 3.86 gc/100 mL). The Wilcoxon signed-rank test confirmed that the difference in median virus concentrations was statistically significant for SNF B (p=0.001) and overall (p=0.001). Spearman’s correlation results showed overall higher correlations with enMF than with NP and a greater number of statistically significant results among subpopulations. For example: using enMF, the correlation among residents and healthcare personnel combined at SNF A was ?=0.86 (p=<0.0001), at SNF B was ?=0.50 (p=<0.01), and at SNF C was ?=0.59 (p=<0.01), whereas using NP, the correlation at SNF A was ?=0.54 (p=<0.01), SNF B was ?=0.46 (p=0.02), and SNF C was ?=0.23 (p=0.26). Conclusions: enMF yielded higher SARS-CoV-2 concentrations than NP and produced stronger correlations between wastewater signal and case counts, though both methods yielded acceptable results. enMF should be considered as an effective virus concentration method for healthcare facility-level WWS to strengthen and complement clinical methods of case detection.

Background: Candida auris, an emerging multidrug-resistant yeast, spreads rapidly in healthcare settings and is associated with difficult-to-control outbreaks. Prevalence has risen significantly in the Metro Detroit area, driven by interfacility transfers and inconsistent communication of colonization status. Patients can remain colonized for prolonged periods and shed the organism into the environment, where it persists for months. Active surveillance testing (AST) can support earlier detection and containment. We describe the implementation of a C. auris AST program across a multi-hospital health system. Methods: Screening for C. auris colonization can be either prevention?based or response?based. Response?based strategies, such as point prevalence surveys (PPS), are recommended by the Centers for Disease Control and Prevention when a new case is identified. Prevention?based AST is also recommended to detect cases early among high?risk groups. In April 2025, we launched a C. auris AST program across five hospitals in Southeast Michigan. Composite axilla–groin swabs were tested by real?time polymerase-chain reaction (PCR). High?risk patients were defined as those presenting from long?term acute care hospitals, skilled nursing facilities, subacute rehabilitation facilities, or inpatient rehabilitation centers who required ED admission or transfer from another healthcare facility (including internal transfers) and had one or more of the following: indwelling devices (e.g., endotracheal or tracheostomy tubes), chronic or non?healing wounds, or colonization/infection with multidrug?resistant or carbapenem?resistant organisms. Upon order signing in the electronic health record, an alert prompted providers to order C. auris PCR and initiate contact precautions. Patients were retested upon readmission. PPS were also performed per state health department recommendations following identification of new cases. Results: During the 6?month surveillance period, 2,710 unique patients underwent AST, with 3,044 total tests completed. Of these tests, 969 (31%) were ordered in the emergency department at the time of admission. Overall, 37 patients (1.2%) screened positive for C. auris colonization (Table). During the same period, 1,354 tests were performed through 77 unit?based PPS, yielding 22 positive results (1.6%). Conclusion: Although AST was implemented as a prevention?based strategy, PPS remained necessary for identifying additional cases. Positivity rates were low across both approaches, yet each required substantial coordination, staff time, and laboratory resources. These findings highlight the need to balance the benefits of early detection with operational demands. Alternative approaches beyond widespread screening should be explored to optimize resource utilization while maintaining effective C. auris prevention and control.

Background: Blood culture contamination (BCC) is a persistent problem in the hospital, contributing to unnecessary antibiotic exposure, excessive diagnostic testing, increased length of stay, and higher costs. For patients with suspected or confirmed sepsis, BCC has particularly serious implications, as early identification of the causative pathogen and prompt initiation of appropriate antimicrobial therapy are critical. National benchmarks define <3% contamination as acceptable, yet emerging evidence supports a more ambitious goal of ?1%, particularly in high-risk populations such as patients with sepsis. Methods Using a sequential practice change across three time periods: (1) no blood culture diversion device (2019–2020), (2) implementation of manual blood diversion device (BDD) #1 (2021–2022), and (3) automatic BDD#2 (2023–present), we have achieved a 74% reduction in BCC (Figure 1). In this project, we further evaluated the impact of this overall reduction on patients admitted with suspected or confirmed sepsis. Results Data were extracted for adult patients admitted to the hospital who had a final discharge diagnosis of sepsis, regardless of their initial admission diagnosis. A total of 154 blood cultures were analyzed across the three time periods. During period 1 (2019 & 2020), 6/62 cultures were contaminated (9.7%). After implementation of manual BDD#1 (11/17/2021-2022), BCC decreased to 2/30 cultures (6.7%). After transition to automatic BDD#2 (1/20/2023-present), BCC further declined to 3/62 cultures (4.8%). One-way analysis of variance (ANOVA) demonstrated a statistically significant difference in BCC rates across the time periods, with post hoc comparisons showing a significantly lower contamination rate when using BDD#2 as compared with both no BDD and manual BDD#1. Additionally, manual BDD#1 was used in 48% (30/62) of the blood culture collections versus to 78% for automatic BDD#2, with staff feedback indicating greater ease of use with the automatic BDD#2 system. Conclusion Adoption of BDD was associated with a 50% (9.7-4.8%) reduction in BCC, with the lowest rates following implementation of automatic BDD#2. Compliance with BDD use was also highest with automatic BDD#2, and staff reported greater ease of use. These findings underscore the importance of optimizing BCC practices as a key component of sepsis management, antimicrobial stewardship, and patient safety efforts. Additional research is needed to identify strategies to further reduce BCC in sepsis patients, with particular attention to BDD usability, workflow integration, and process adherence during real-world use.

Background: We had a large-scale measles exposure event affecting 626 patients. Management of exposed patients was urgent, involving quarantine, post-exposure prophylaxis (PEP), and symptom monitoring on a case-by-case basis. Cases were initially tracked and managed using a combination of multiple spreadsheets amongst stakeholders as well as lists within the electronic health record (EHR). Using shared spreadsheets and lists created significant challenges with maintaining and editing multiple copies and updating workbooks simultaneously. Methods: Infection Prevention and Control (IPAC) worked with primary care personnel and EHR developers to create a shared registry that would prioritize communication and follow-up for exposed patients based on age and immunity. Patients are initially added to the shared registry within the EHR by reviewing non-registry reports and tagging patients, identified using exposure timeframes that overlapped with the index measles cases, in a manner that transferred them to the shared registry. From this registry, measles, mumps, and rubella vaccine status can be confirmed, immune status can be identified, and criteria for PEP assigned. High-risk patients are then contacted with recommendations from IPAC and primary care. Communication and follow-up are tracked directly within the shared registry to ensure appropriate patient management. The exposure management registry is further leveraged throughout the measles outbreak timeframe by allowing real-time monitoring of patients that require additional precautions or guidance related to their exposure. Result: The shared registry enhanced documentation accuracy and transparency between stakeholders. It led to availability of real-time status of exposed patients for the duration of the measles outbreak timeframe. Overall, the registry enabled time-sensitive patient communication to all exposed patients and rapid identification of those who needed PEP. Communication could happen rapidly and seamlessly between care teams within the organization, and the necessary information could be shared quickly with public health authorities as required. Conclusion: Large scale exposures are a challenge to manage and ensure appropriate action and communication occurs. Errors can be prevented by having a single shared exposure management registry. A registry enables care teams to quickly identify and prioritize patients for measles follow-up, communication, PEP, and ongoing management across outbreak timeframes. This process reduces both time and operational burdens of exposure follow-up tracking outside of the EHR.

Background: Central line-associated bloodstream infection (CLABSI) represents the most common and costly device-associated hospital-acquired infection in children. While prevention bundles are effective when reliably implemented, pediatric-specific risks often require strategies beyond standard bundles. Existing CLABSI risk models are insufficient for pediatric patients, remaining static during their hospital stay despite changing clinical conditions. Moreover, previously reported dashboards and predictive models generally do not incorporate data science principles to provide a daily prediction score, and they often exclude demographic factors beyond age and sex. The purpose of developing the CLABSI risk model was to leverage technology-driven strategies for identifying, intervening, and escalating care for high-risk pediatric patients in real time, thereby decreasing infection risk. Methods: Quality Improvement, Data Intelligence, and the interdisciplinary clinical team partnered to develop an evidence-based predictive model tailored to institutional risk factors. Using evidence and clinical expertise, an affinity diagram was created. Data from 61,068 inpatient central line days (January 2022- July 2024), sourced from electronic health records (EHR) and clinical surveillance software, formed the dataset. CLABSI was identified using NHSN surveillance definition. Patient risk factors from the infection date plus two days prior were evaluated. Exploratory data analysis was completed using RStudio integrated development environment to inform model development. Separate models were created for nontunneled lines and tunneled lines. Prediction models were developed using Python coding language and Databricks data intelligence platform. Under-sampling was used to account for uneven distribution between CLABSI and non-CLABSI cases. Logistic regression, random forest, and XGBoost models were created. Candidate models were compared using area under the curve (AUC), sensitivity, and specificity. Result: Using the XGBoost model for nontunneled line, the test AUC was 0.831, demonstrating strong discriminative power. This model achieved a sensitivity of 0.828, indicating its ability to correctly identify a high proportion of actual CLABSI cases, and a specificity of 0.751, reflecting its capacity to correctly identify non-CLABSI cases. Similarly, the tunneled line XGBoost model yielded an AUC of 0.845, showcasing excellent predictive performance. Its sensitivity was 0.833 and specificity was 0.668. In both models, the number of modifiable risks was the most significant factor (Tables 1-3). Conclusion: The CLABSI prediction model enables real-time identification and intervention to support proactive prevention, leveraging these performance characteristics to improve patient care. While predictive modeling has limitations due to EHR documentation, clinician expertise and frontline team engagement remain essential for deploying resources to highest risk patients.

Background: Effective infection prevention and control is integral to the delivery of safe patient care, and with ever increasing demands on their attention, systems to help navigate policy-based guidance can support infection preventionists (IPs) and their healthcare colleagues. Here we present the validation process and results for an internal infection control chatbot. Methods: Eleven frequently asked questions were selected to evaluate the performance of ChatUCM, an AI-powered chatbot. Each question was posed five times and response quality was scored by intent recognition, relevance, accuracy, completeness, and consistency. A category pass threshold was set at 75%. IP feedback was incorporated in a ChatUCM update and response quality was scored again. Reference document edits and real-time prompt modifications were made based on post-update scores. A final round of scoring was performed using similar validation metrics. Two IPs scored a subset of five out of the eleven frequently asked questions to assess interrater reliability. Six nursing managers were invited to ask ChatUCM any questions and score its responses by intent recognition, their ability to trust response accuracy, whether their question was fully answered, the achievement of intended goals, and ease of use. 29 questions were submitted. An IP reviewer also scored these ChatUCM responses as optimal, suboptimal/ no harm, and suboptimal/ possible harm. Results: The impact of the update on scores was variable. ChatUCM performed well on the same 7 out of 11 questions before and after validation round 1 (Fig. 1). After the final round of retraining, ChatUCM passed all categories with high interrater reliability for our frequently asked questions (Fig. 2). Response quality was less predictable outside of these eleven questions (Fig 3, Fig 4). To ensure its safe use, multiple safety checks were created, including disclaimer language, citation links, education, and a query report to facilitate audits. We encountered multiple surprises while validating ChatUCM. The first update resulted in significant improvements, such as the ability to digest data in table and flow diagram formats. It also saw category scores decrease for six questions, three of which had overall scores drop. ChatUCM struggled with accuracy for questions that required accounting for multiple clinical factors (ex: shingles guidance). Conclusions: These findings suggest that ChatUCM could substantially reduce IP workload by accurately answering questions contained in internal SOPs and policies. Additional development will be needed to improve reliability before the tool can function independently without human oversight, particularly when multiple clinical factors must be taken into account.

Background: Less than 25% of adults in the United States are up-to-date on all age-appropriate vaccinations. Primary care providers (PCPs) may defer routine vaccine discussions due to insufficient time to access and review vaccination records. The Virtual Vaccine (ViVa) clinic was implemented to reduce time and cognitive burden on PCPs by offloading the tasks of vaccine history review and patient-specific recommendations to other clinicians, with the goal to improve adult vaccination rates. Methods: From January to October 2025, ViVa clinicians reviewed the charts of Veterans aged ?vs. vs. without (ViVa-) a ViVa note. Patients seen in non-intervention primary care clinics at the same facility served as controls. Results: During the study period, 654 and 2488 Veterans aged ?Table 1). The proportion of up-to-date patients increased across all studied vaccines in all groups. Among ViVa+ patients, 36% received one or more recommended vaccines compared to 27% in the ViVa- and 23% in the control patients. The largest gains were for RSV and pneumococcal vaccines among ViVa+ patients (Figure 1). The median number of vaccines for which ViVa+ patients were up-to-date increased from 3 (IQR 2-4) at baseline to 4 (IQR 2-5) at follow-up. Median values did not change from baseline vs. follow-up for patients in the ViVa- (3, IQR 2-4 vs. vs. Conclusions: A succinct note entered into the electronic health record successfully augmented adult vaccination rates for patients seen in primary care clinics. The ViVa clinic was more effective for vaccines with recent schedule changes compared to those with more established schedules, including seasonal vaccines. Comparing outcomes among ViVa- and control patients suggests a positive spillover effect for PCPs in the intervention clinics.

Background: Measles cases increased significantly in the Americas in 2025; the region lost its measles-free status due to the return of endemic transmission in Canada, Mexico, and the U.S., primarily driven by low vaccination rates among unvaccinated or under-vaccinated populations. In August 2025, Mexico City experienced a surge in measles cases, prompting an urgent call to boost immunization coverage among targeted groups through mass vaccination of children, adolescents, healthcare workers (HCWs), and adults under 50. We report post-vaccination mumps cases among HCWs from six hospitals. Methods: A retrospective study of individuals vaccinated with the Serum Institute of India measles, mumps, and rubella (MMR) vaccine, which contains the Leningrad-Zagreb (L-Z) strain, was conducted at six national referral tertiary care hospitals affiliated with the Mexican Ministry of Health. We included all individuals under 50 years who, 10-30 days after vaccination, developed salivary gland swelling with or without malaise, headache, lymphadenopathy, or fever. We performed a descriptive analysis and calculated the incidence rate of vaccine-related mumps per 1000 doses of the MMR vaccine. Results: Between 08/21/2025 and 11/30/2025, 10,590 HCWs received the Serum Institute of India MMR vaccine; 27 (2.5 X MMR 1000 vaccines) developed parotid swelling (7 bilateral, 20 unilateral), with concomitant fever in 7 (25.9%); lymphadenopathy, 10 (37%); pharyngitis, 11 (40.7%); arthralgias, 8 (29.6%); and headache, 11 (40.7%). One person developed a rash and malaise. The median time from vaccination to mumps was 16.8 days. No cases of meningitis or other complications were reported. The average duration of symptoms was 8.2 days. No secondary mumps cases were detected. Discussion: Post-vaccination mumps and aseptic meningitis after MMR vaccination are rare and infrequently reported, though prevalence varies by strain. Despite similar efficacy among MMR vaccines, the Serum Institute of India vaccine includes the L-Z strain, which carries a higher risk of mumps (approximately 4.4%) than the Jeryl-Lynn strain used in the MMR-II® (0.5-1.6%), which is primarily used in high-income countries. In our cohort, the prevalence of post-vaccine mumps with the Serum Institute of India MMR, a lower-cost vaccine, was low and consistent with other reports. No cases of aseptic meningitis were reported. With the rising number of measles cases worldwide and the need to increase vaccination coverage against measles in under-vaccinated populations, reinforcing the similar efficacy and safety of different vaccines is crucial. All HCWs must be protected.

Background: Ventilator-associated events (VAEs) are episodes of respiratory deterioration after stability during mechanical ventilation, posing significant risks for morbidity and mortality. Extremely low birth weight neonates (<750 g), particularly those born at 22–24 weeks gestation, are highly vulnerable. In our Neonatal Intensive Care Unit (NICU), eight VAEs occurred in the previous year, leading to prolonged ventilation, increased infection risk, and higher mortality. Reducing VAEs is essential for improving patient safety, health equity, and family experience while meeting NHSN benchmarks and regulatory goals. Methods: A structured, evidence-based approach was implemented using failure mode effects analysis (FMEA) to identify risk factors and guide interventions. A multidisciplinary team developed a formalized VAE prevention bundle, which included: - Formation of a multidisciplinary task force - Process measures tied to critical clinical tasks - Creation of an EHR tool for real-time compliance monitoring - Staff education and workflow alignment - Cultural engagement initiatives to reinforce best practices Pre- and post-intervention data were collected and analyzed for neonates <750 g per NHSN guidelines. Result: Post-implementation, VAE rates decreased from 9.8 to zero per 1,000 ventilator days, achieving an SIR ?1.0 and meeting national benchmarks. Mortality among extremely low birth weight neonates declined by 60%. Integration of EHR visualization and Power BI dashboards enabled real-time monitoring and sustained compliance. The initiative generated an estimated annual cost savings of $2.1 million through reduced length of stay and infection-related expenses. Conclusion: Structured care bundles, integrated data tools, and multidisciplinary engagement can eliminate VAEs in high-risk neonates. Broader adoption across NICUs could enhance patient safety, reduce healthcare costs, and improve outcomes systemwide. Innovative clinical practice plays a pivotal role in sustaining these improvements through adherence to prevention bundles, leveraging technology for real-time compliance, and fostering a culture of accountability. Standardized frameworks and education strategies can be scaled across institutions for continuous improvement. References Bondarev DJ, Ryan RM, Mukherjee D. (2024). The spectrum of pneumonia among intubated neonates in the neonatal intensive care unit. Journal of Perinatology, 44(9), 1235–1243. https://doi.org/10.1038/s41372-024-01973-9 Niedzwiecka T, Patton D, Walsh S, Moore Z, O'Connor T, Nugent L (2019, October). What are the effects of care bundles on the incidence of ventilator-associated pneumonia in pediatric and neonatal intensive care units? A systematic review. Journal for Specialists in Pediatric Nursing: JSPN. https://pubmed.ncbi.nlm.nih.gov/31332968/