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Background: Candida auris (C auris) is a fungal pathogen that has the potential for environmental persistence leading to outbreaks in health care settings. There has been a worldwide surge in C auris outbreaks during the COVID-19 pandemic. In this report, we describe an outbreak of C auris, its control, patient outcomes, and lessons learned., Methods: The outbreak occurred in a 600-bed adult academic tertiary care hospital. Contact tracing was initiated immediately after identification of the index case and surveillance testing for C auris was obtained from patients who were exposed to the index case. Infection prevention measures were closely followed., Results: A total of 560 cultures were performed on 453 unique patients between August 2021 and December 2021. Of those, 31 cultures (5.5%) were positive for C auris; 27 (87.1%) were colonized with C auris, while 4 patients developed a clinical infection (12.9%). The secondary attack rate was 6.8% (31/453). The 30-day all-cause mortality rate for all patients who tested positive for C auris was 9.7%., Discussion: C auris can cause protracted outbreaks that result in colonization and invasive infections. Multidisciplinary work to improve adherence to infection prevention measures as well as targeted admission screening are essential to limit outbreaks., (Copyright © 2024 Association for Professionals in Infection Control and Epidemiology, Inc. Published by Elsevier Inc. All rights reserved.)

Background: Hospital acquired infections (HAIs) are a major driver of morbidity and cost in health systems. Central line-associated bloodstream infections (CLABSIs) require intensive surveillance and review. All-cause hospital-onset bacteremia (HOB) may be a simpler reporting metric, correlates with CLABSI, and is viewed positively by HAI experts. Despite the ease in the collection, the proportion of HOBs that are actionable and preventable is unknown. Moreover, quality improvement strategies targeting it may be more challenging. In this study, we describe the bedside provider-perceived sources of HOB in order to provide insight into this new metric as a target for HAI prevention., Methods: All cases of HOBs in 2019 from an academic tertiary care hospital were retrospectively reviewed. Information was collected to assess provider-perceived etiology and associated clinical factors (microbiology, severity, mortality, and management). HOB was categorized as preventable or not preventable based on the perceived source from the care team and management decisions. Preventable causes included device-associated bacteremias, pneumonias, surgical complications, and contaminated blood cultures., Results: Of the 392 instances of HOB, 56.0% (n = 220) had episodes that were determined not preventable by providers. Excluding blood culture contaminates, the most common cause of preventable HOB was secondary to CLABSIs (9.9%, n = 39). Of the HOBs that were not preventable, the most common sources were gastrointestinal and abdominal (n = 62), neutropenic translocation (n = 37), and endocarditis (n = 23). Patients with HOB were generally medically complex with an average Charlson comorbidity index of 4.97. This translated into a higher average length of stay (29.23 vs 7.56, P < .001) and higher inpatient mortality (odds ratio 8.3, confidence interval [6.32-10.77]) when compared to admissions without HOB., Conclusions: The majority of HOBs were not preventable and the HOB metric may be a marker of a sicker patient population making it a less actionable target for quality improvement. Standardization across the patient mix is important if the metric becomes linked to reimbursement. If the HOB metric were to be used in lieu of CLABSI, large tertiary care health systems that house sicker patients may be unfairly financially penalized for caring for more medically complex patients., (Copyright © 2023 Association for Professionals in Infection Control and Epidemiology, Inc. Published by Elsevier Inc. All rights reserved.)

Background: Surgeons use indwelling bladder catheters (IBCs) to avoid urinary retention in patients with epidural analgesic catheters. Reduction of IBC-days is associated with improved catheter-associated urinary tract infection rates (CAUTI). This study investigates real world application of a Nurse-Driven Catheter Removal Protocol (NDCRP) to reduce IBC-days in this patient population., Methods: Patients with epidural catheters and IBC were targeted for IBC removal on post-operative day 1 (POD1). Patients were followed for application of the NDCRP, catheterization need, IBC re-anchoring, and complications., Results: One hundred and thirty-three patients had IBCs removed on POD1 (Protocol Group) and 50 patients did not (Non-Protocol Group). There was a reduction in IBC-days in the Protocol Group despite incomplete adherence to the NDCRP (1.55 days vs 4.64 days; P < .001). Ninety-three patients (70%) were able to spontaneously void after early IBC removal. Fourteen patients (11%) were able to spontaneously void after serial in-and-out catheterization (I/O). No significant difference in re-anchoring was found between the protocol and non-protocol groups (26 vs 4 patients; P = .09)., Conclusions: Early removal of IBCs (POD1) in patients with epidural catheters with the assistance of an NDCRP is a safe and successful strategy to reduce IBC-days in the hospital., Competing Interests: Conflicts of interest KK serves on the professional practice committee for SHEA in an unpaid role. All the other authors have no conflict of interest., (Copyright © 2021 Association for Professionals in Infection Control and Epidemiology, Inc. Published by Elsevier Inc. All rights reserved.)

Background: COVID-19 is a novel disease caused by SARS-CoV-2., Methods: We conducted a retrospective evaluation of patients admitted with COVID-19 to one site in March 2020. Patients were stratified into 3 groups: survivors who did not receive mechanical ventilation (MV), survivors who received MV, and those who received MV and died during hospitalization., Results: There were 140 hospitalizations; 22 deaths (mortality rate 15.7%), 83 (59%) survived and did not receive MV, 35 (25%) received MV and survived; 18 (12.9%) received MV and died. Thee mean age of each group was 57.8, 55.8 and 72.7 years, respectively (P = .0001). Of those who received MV and died, 61% were male (P = .01). More than half the patients (n = 90, 64%) were African American. First measured d-dimer >575.5 ng/mL, procalcitonin > 0.24 ng/mL, lactate dehydrogenase >445.6 units/L, and brain natriuretic peptide (BNP) >104.75 pg/mL had odds ratios of 10.5, 5, 4.5 and 2.9, respectively for MV (P < .05 for all). Peak BNP >167.5 pg/mL had an odds ratio of 6.7 for inpatient mortality when mechanically ventilated (P = .02)., Conclusions: Age and gender may impact outcomes in COVID-19. D-dimer, procalcitonin, lactate dehydrogenase and BNP may serve as early indicators of disease trajectory., (Copyright © 2020 Association for Professionals in Infection Control and Epidemiology, Inc. Published by Elsevier Inc. All rights reserved.)

Over diagnosis of catheter-associated urinary tract infection (CAUTI) contributes to unnecessary and excessive antibiotic use, selection for resistant organisms, increased risk for Clostridiodes difficile infections, as well as a false elevation in CAUTI rates. Utilizing agile implementation to implement a urine culture algorithm achieved statistically significant reduction in CAUTI rates in a critical care unit resulting in sustainment and spread throughout the system., (Copyright © 2020 Association for Professionals in Infection Control and Epidemiology, Inc. Published by Elsevier Inc. All rights reserved.)

Disinfecting port protectors are a supplement to the central line-associated bloodstream infection prevention bundle as an optional recommendation from the Centers for Disease Control and Prevention. Despite evidence of effectiveness, few centers have successfully reported systematic, sustained implementation of these devices. In this article, we discuss a successful implementation in a large tertiary care teaching hospital, using an evidence-based, multidisciplinary approach. Infection prevention; Bacteremia; Ethanol caps; Bundle measures; Quality improvement; Hub infection., (Copyright © 2019 Association for Professionals in Infection Control and Epidemiology, Inc. Published by Elsevier Inc. All rights reserved.)

Background: Central line-associated bloodstream infections (CLABSIs) are among the most common hospital-acquired infections and can lead to increased patient morbidity and mortality rates. Implementation of practice guidelines and recommended prevention bundles has historically been suboptimal, suggesting that improvements in implementation methods could further reductions in CLABSI rates. In this article, we describe the agile implementation methodology and present details of how it was successfully used to reduce CLABSI., Methods: We conducted an observational study of patients with central line catheters at 2 adult tertiary care hospitals in Indianapolis from January 2015 to June 2017., Results: The intervention successfully reduced the CLABSI rate from 1.76 infections per 1,000 central line days to 1.24 (rate ratio = 0.70; P = .011). We also observed reductions in the rates of Clostridium difficile and surgical site infections, whereas catheter-associated urinary tract infections remained stable., Conclusions: Using the AI model, we were able to successfully implement evidence-based practices to reduce the rate of CLABSIs at our facility., (Copyright © 2018 Association for Professionals in Infection Control and Epidemiology, Inc. Published by Elsevier Inc. All rights reserved.)

Background: Central line-associated bloodstream infections (CLABSIs) contribute to increased morbidity, length of hospital stay, and cost. Despite progress in understanding the risk factors, there remains a need to accurately predict the risk of CLABSIs and, in real time, prevent them from occurring., Methods: A predictive model was developed using retrospective data from a large academic healthcare system. Models were developed with machine learning via construction of random forests using validated input variables., Results: Fifteen variables accounted for the most significant effect on CLABSI prediction based on a retrospective study of 70,218 unique patient encounters between January 1, 2013, and May 31, 2016. The area under the receiver operating characteristic curve for the best-performing model was 0.82 in production., Discussion: This model has multiple applications for resource allocation for CLABSI prevention, including serving as a tool to target patients at highest risk for potentially cost-effective but otherwise time-limited interventions., Conclusions: Machine learning can be used to develop accurate models to predict the risk of CLABSI in real time prior to the development of infection., (Copyright © 2018 Association for Professionals in Infection Control and Epidemiology, Inc. Published by Elsevier Inc. All rights reserved.)