Background: Surgical site infections (SSIs) following colorectal surgery are associated with increased morbidity, mortality, length of stay, and costs. During our hospital’s fiscal year (FY) 2024 reporting period (8/2023–7/2024), we had15 colorectal SSIs with a standardized infection ratio (SIR) of 2.43, indicating a need for focused improvement. Methods: In August 2024, we convened a multidisciplinary team in our 600-bed academic hospital, including perioperative nursing and education, infection prevention, anesthesia, colorectal surgery, infectious diseases, hospital epidemiology, quality and patient safety. After a gap analysis, the team implemented evidence-based interventions (Figure 1) guided by the Lean Six Sigma framework—Define, Measure, Analyze, Improve, and Control. The interventions included: 1) initiating apparent cause analysis (ACA) reviews for all colorectal SSIs with a multidisciplinary team; 2) standardizing colorectal surgery practices with wound protectors and a protocolized closing bundle (i.e. glove/gown change and closing tray); 3) creation of a perioperative antibiotic prophylaxis quick reference guide; and 4) clinical documentation improvement strategies for infections present at time of surgery (PATOS). We compared process measures (adherence to closing bundle, wound protector, and perioperative antibiotics) and outcome measures (number of SSI and SIR) between FY2024, FY2025 (8/2024-7/2025) and FY2026 (8/2025-10/2025). A run chart and Power BI were used for data visualization and metrics were reviewed monthly with the working group. A statistical process control (SPC) XmR chart was constructed retrospectively to assess process stability and detect special cause variation after the implementation of initiatives. Results: Compared to FY2024, we had fewer colorectal SSIs (15 vs 5) and a lower SIR (2.43 (95% confidence interval [CI] 1.41–3.91) vs 0.82 (95% CI 0.30-1.83) in FY2025 (Table 1). In the first quarter FY2026 we had two colorectal SSIs. Compared to FY2024, we observed increased adherence with the closing bundle (21%), wound protector use (13%), and perioperative antibiotics (4%) with continued increases in FY2026 (Table 1). Over time, early special cause variation was followed by stabilization with a centerline shift in SIR from 3.21 to 1.40 and upper control limit (UCL) reduction from 10.50 to 4.59 following implementation of quality improvement interventions (Figure 2). Conclusion: Iterative and continual process improvement with a multidisciplinary, engaged team significantly reduced colorectal SSIs at our hospital and improved compliance with key infection prevention measures. Ongoing efforts are focused on continued implementation of our key initiatives and sustainment.
Background: Contact precautions were used during COVID-19 to reduce nosocomial transmission, though these practices have environmental impacts. We sought to examine the effect of de-implementing contact precautions in a regional healthcare system. Methods: We utilized the electronic medical record to identify all hospital-onset COVID-19 cases among hospitalized patients in nine regional hospitals from Results: There were 88,963 patient-days of isolation during implementation, and 23,378 patient-days of isolation after implementation. Conclusions: Despite de-implementing glove/gown use, we observed no increase in HO COVID-19; however, we benefited from a considerable decrease in both global warming potential and waste.
Background: Patients in the intensive care unit (ICU) often require two concurrent central venous catheters (CVCs) – a temporary CVC for medication administration and a hemodialysis (HD) catheter (HDC). Having concurrent CVCs increases the risk of central line-associated bloodstream infections (CLABSIs). Triple-lumen HDCs have an additional lumen, which may reduce the need for concurrent CVCs as compared to double-lumen HDCs. We evaluated CVC utilization and prevalence of concurrent CVCs in patients with HDCs in an academic ICU after implementing triple-lumen HDC use. Methods: We analyzed data from patients with newly placed HDCs, admitted to a 20-bed medical ICU in an academic hospital in Atlanta, GA from November 2022-January 2025. Triple-lumen HDCs were introduced February 2024. Data were obtained from the electronic health records. Concurrent CVC was defined as having a newly placed HDC and any other simultaneous CVC for at least one calendar day during the ICU admission. Central line-days were defined as number of CVCs in place per 1000 patient-days and lumen-days were defined as number of lumens in place per 1000 patient-days. We analyzed CVC and lumen utilization and prevalence of concurrent CVCs after the intervention by interrupted time series analysis (ITS). ITS with Poisson regression determined if there was a decrease in lumen-days or admissions with concurrent CVCs in the post-intervention vs. pre-intervention time period. An exploratory analysis compared the number of CLABSIs (as defined by NHSN) pre- and post-intervention. Results: Conclusion: After the introduction of triple-lumen HDCs to an academic medical ICU, the total prevalence of patients on HD requiring concurrent CVCs decreased, however our sample size was too small to demonstrate statistical significance by ITS. Decreasing the need for concurrent CVCs may reduce CLABSIs in this ICU but additional time post-intervention is needed.
Background: Rapid identification of patients with carbapenem-resistant Enterobacterales (CRE) on admission to an acute care hospital is critical to prompt initiation of infection control measures. Clinical risk prediction tools can assist in identifying high risk patients and allow facilities to perform targeted CRE screening. We aimed to prospectively validate a previously developed CRE risk prediction tool which incorporates data from current and prior hospital encounters and was incorporated into our electronic medical record (EMR). Method: From 6/2024 – 12/2024 we used an automated daily EMR report to calculate a CRE risk score (probability of a CRE clinical culture within 3 days of hospitalization) for all admissions from the previous day at two hospitals in an academic healthcare network in Atlanta, GA. On select days of the week, we approached a convenience sample of approximately 10 patients with the highest risk scores and obtained a peri-rectal swab on consented patients. Swabs were broth enriched and tested on CHROMagarTM ESBL plates. We used MALDI-TOF and/or the Vitek®2 GN74 panel for species identification and antibiotic susceptibility testing. To evaluate testing accuracy, we defined individuals as high-risk if they had a CRE risk prediction score in the top quartile of scores among patients approached. We calculated the sensitivity, specificity, and positive and negative predictive value of this threshold to predict patients with CRE peri-rectal carriage. Results: 9,422 admissions occurred on sampling days; we approached 720 of which 282 (39%) were consented and tested (Figure 1). Ten patients (3.5%) were positive for CRE: 4 Klebsiella pneumoniae, 3 Escherichia coli, 2 Enterobacter cloacae, and 1 Pantoea species. Among tested individuals, patients with CRE had a higher median CRE risk score (0.19% vs 0.04%), more healthcare exposures, a higher Elixhauser score, and more antibiotic days of therapy (Figure 2). Of the 72 (25%) patients at high-risk (CRE risk prediction ≥0.1%) 6 (8.3%) were CRE positive; using this threshold the sensitivity and specificity were 60% and 80%, respectively, and the positive and negative predictive value were 8% and 98%, respectively. Conclusion: Utilizing an EMR-based risk prediction tool can help identify patients at high-risk for CRE colonization. In healthcare facilities with a low CRE-prevalence, identifying a high-risk subset of patients, even with an 8% probability of CRE, could be a clinically meaningful infection prevention measure. Individual healthcare facilities could adjust the testing threshold based on the hospital and population needs.
Background: Carbapenem-resistant Pseudomonas aeruginosa (CRPA) can cause healthcare-associated infections associated with poor outcomes. Unlike other carbapenem-resistant organisms, CRPA is often susceptible to at least one traditional anti-pseudomonal β-lactam antibiotic including cefepime, ceftazidime, and piperacillin-tazobactam (“S-CRPA”). This study aimed to determine if treatment of S-CRPA infections with a novel β-lactam/β-lactamase inhibitor (βL/ βLI) as opposed to a traditional antipseudomonal β-lactam which may increase the likelihood of resistance, was associated with improved clinical outcomes. Methods: We retrospectively analyzed all incident S-CRPA isolates in a four-hospital academic healthcare system in Atlanta, GA from 1/1/2013 to 9/30/2022. Patients receiving either a βL/ βLI or a traditional antipseudomonal β-lactam for definitive antibiotic therapy, defined as having received at least 3 days of the specified antibiotic between 4-14 days after the culture was obtained, were included. We excluded patients with cystic fibrosis. We compared patients who received definitive treatment with a βL/ βLI to those who received definitive therapy with a traditional anti-pseudomonal β-lactam. The primary outcome was mortality (in-hospital mortality and those discharged to hospice). We performed univariable and multivariable logistic regression analysis using SAS 9.4. A causal diagram was created to a priori to identify potential confounders for inclusion in a multivariate analysis including Elixhauser comorbidity score, specimen source, β-lactam resistance pattern, and whether the patient had a hospital-onset infection (culture obtained ≥3 days after admission) Results: There were 258 patients with S-CRPA who received definitive treatment with either βL/ βLI (n=22) or traditional anti-pseudomonal β-lactams (n= 236). Those who received definitive βL/ βLI therapy had higher Elixhauser comorbidity scores, longer lengths of stay, were more likely to have bacteremia, and more likely to have resistance to one or more traditional anti-pseudomonal β-lactams than those treated with traditional anti-pseudomonal β-lactams (Figure 1). In the univariable analysis, patients who received definitive BL/BLI therapy had increased mortality and patients without bacteremia and hospital-onset infection had decreased mortality, but the associations were not statistically significant (Figure 2). In the multivariable analysis, treatment with traditional anti-pseudomonal β-lactams was not significantly associated with mortality (OR 1.9 95%CI 0.6 – 5.7). Conclusions: In patients with S-CRPA we did not observe a significant difference in mortality comparing definitive antibiotic treatments. A low number of S-CRPA isolates treated with a βL/ βLI limited our ability to assess the true effect of traditional anti-pseudomonal β-lactams versus βL/ βLI on mortality.
Background: Dichotomous outcomes rarely capture the range of potential outcomes important to patients and clinicians. To address this limitation, the Desirability of Outcome Ranking (DOOR) score was created to rank potential outcomes from least to most desirable. Currently, there is no standardized method to develop a DOOR score and data are limited on whether patients and their clinicians rank outcomes similarly. We aimed: (a) to develop a novel DOOR score for adults hospitalized with community-acquired pneumonia (CAP) by surveying patients and clinicians on their preferred outcome ranking and (b) to compare their relative DOOR rankings. Methods: We created nine clinical scenarios describing the spectrum of potential outcomes of patients with CAP two weeks after initial emergency department visit. To ascertain clinician DOOR score, we used a snowball sampling method to recruit a target of 25 clinicians in specialties that regularly treat CAP. For the patient DOOR score, we recruited patients hospitalized with CAP by reviewing electronic patient lists for adults hospitalized with pneumonia. Respondents were asked to rank the 9 cases from most to least desirable in REDCap. To create the final DOOR score, we used Friedman rank sum tests to combine/collapse DOOR outcomes with scores that did not significantly differ. We used the Mann Whitney U test to compare DOOR rankings between physicians and patients. Final study results were presented to a national hospital medicine patient and family advisory committee (PFAC) for their impressions. Results: 22 patients (71% response rate) and 25 clinicians responded to our DOOR survey. Their ranked order of DOOR outcomes is shown in Table 1. Combining non-significantly different DOOR outcomes resulted in collapsing of 6 cases into 2 categories for 5 overall DOOR scores that significantly differed from each other (Table 1 for final ranking). Patients and clinicians had significantly different preferred ranking for 6 DOOR cases. Our PFAC had several hypotheses as to why rankings differed (Table 2). Conclusion: We present a novel DOOR score derived from patient and clinician reported preferences for outcomes of hospitalized adult patients with CAP. Clinicians and patients differed in their perception of certain outcomes with patients ranking symptoms that were uncomfortable but not potentially life-threatening as less desirable than physicians. Physicians tended to rank quality linked metrics such as readmission as worse than patients. When designing future trials using DOOR scores, researchers should consider including patients in DOOR score design as their perspectives may differ from clinicians.
Background: Interventions targeting urine culture stewardship can improve diagnostic accuracy for urinary tract infections (UTI) and decrease inappropriate antibiotic treatment of asymptomatic bacteriuria. We aimed to determine if a clinical decision support (CDS) tool which provided guidance on and required documentation of the indications would decrease inappropriately ordered urine cultures in an academic healthcare network that already uses conditional (e.g. reflex) urine testing. Methods: In October 2022, four hospitals within one academic healthcare network transitioned to a new electronic health record (EHR). We developed an embedded CDS tool that provided guidance on ordering either a urinalysis (UA) with reflex to urine culture or a non-reflex urine culture (e.g. for pregnant patients) based on the indication for testing (Figure 1). We compared median monthly UA with reflex culture and non-reflex urine culture order rates pre- (8/2017–9/2022) and post- (10/2022–9/2023) intervention using the Wilcoxon rank-sum test. We used interrupted time-series analyses allowing a one-month time window for the intervention effect to assess changes in monthly UA with reflex culture, non-reflex urine culture, and total urine culture order rates associated with the intervention. Using SAS 9.4, we generated Durbin-Watson statistics to assess for autocorrelation and adjusted for this using a stepwise autoregressive model. Result: The median monthly UA with reflex culture order rates per 1000 patient-days were similar pre- and post- intervention at 36.7 (interquartile range [IQR]: 31.0–39.7) and 35.4 (IQR: 32.8–37.0), respectively (Figure 2). Non-reflex and total urine culture rates per 1000 patient-days decreased from 8.5 (IQR: 8.1–9.1) to 4.9 (IQR: 4.7–5.1) and from 20.0 (IQR: 18.9–20.7) to 14.4 (IQR: 14.0–14.6) post-intervention, respectively. Interrupted time-series analyses revealed that the intervention was associated with a decrease in the monthly non-reflex urine culture by 4.8 cultures/1000 patient-days (p< 0.001) and in the total urine culture monthly order rates by 5.0 cultures/ 1000 patient-days (p < 0 .001) [Figures 3a and b]. The UA with reflex order rate did not significantly change with the intervention (not pictured). Conclusion: In an academic healthcare network that already employed conditional urine testing, the implementation of an EHR-based diagnostic stewardship tool led to additional decreases in both non-reflex and total urine cultures ordered.
Background: Central line-associated bloodstream infections (CLABSI) and catheter-associated urinary tract infections (CAUTI) are key healthcare-associated infection (HAI) quality metrics. In this qualitative analysis, we aimed to identify common issues contributing to CLABSIs and CAUTIs occurring during the COVID-19 pandemic. Methods: In an academic healthcare network in Atlanta, GA, four hospitals perform real-time, apparent cause analyses (ACAs) for all CLABSIs and CAUTIs. Contributing factors are entered as free text into an electronic database. We analyzed data from 8/2020–8/2022. We first performed a qualitative open card sort of all reported contributing factors to CLABSI and created a novel framework based on mutually defined critical tasks (e.g., line insertion) and cross-cutting issues (e.g., communication breakdown). Contributing factors could describe ≥1 critical task and/or ≥1 cross-cutting issue. After establishing interrater reliability, a multidisciplinary group applied this framework to classify each contributing factor. For CAUTI, we used the same set of cross-cutting issues but identified new critical tasks via open card sorting. We then used the framework to classify each CAUTI contributing factor. We used descriptive statistics to identify frequent critical tasks and cross-cutting issues. Results: We reviewed 350 CLABSI ACAs with 602 contributing factors and 240 CAUTI ACAs with 405 contributing factors (Figure 1). Our classification framework comprised 11 cross-cutting issues and 9 critical tasks for CLABSI and 7 critical tasks for CAUTI (Figure 2). CLABSI: The critical tasks most often reported were bathing (19%), central line dressing maintenance (15%), and assessing central line indication (8%; Figure 3). Within these tasks, the most frequent issues described for bathing were the task not being performed (20%) and unclear documentation (18%); for dressing maintenance, the task was not performed (15%), not documented (15%), or poorly performed due to lack of competency (15%); and for assessing line indication, there was frequent communication breakdown (33%). CAUTI: The critical tasks most often reported were urinary catheter care (26%) and assessing the indication for urinary catheter (22%; Figure 4). Within these tasks, urinary catheter care was frequently not documented (38%) or not performed (16%); assessing urinary catheter necessity was often not documented (29%) or involved breakdown of communication (19%). Conclusion: We created a novel framework to evaluate common causes of HAIs in an academic healthcare network. This framework can be used to identify and track gaps over time and to develop quality improvement initiatives targeting key tasks and associated factors, such as communication difficulties when assessing device indications.
Disclosure: Colleen Kraft: Consultant - REbiotix/Ferring; Scientific Advisory Board - Seres, LLC
Background: The CDC recommends routine use of contact precautions for patients infected or colonized with multidrug-resistant organisms (MDROs). There is variability in implementation of and adherence to this recommendation, which we hypothesized may have been exacerbated by the COVID-19 pandemic. Methods: In September 2022, we emailed an 8-question survey to Emerging Infections Network (EIN) physician members with infection prevention and hospital epidemiology responsibilities. The survey asked about the respondent’s primary hospital’s recommendations on transmission-based precautions, adjunctive measures to reduce MDRO transmission, and changes that occurred during the COVID-19 pandemic. We sent 2 reminder emails over a 1-month period. We used descriptive statistics to summarize the data and to compare results to a similar EIN survey (n = 336) administered in 2014 (Russell D, et al. doi:10.1017/ice.2015.246). Results: Of 708 EIN members, 283 (40%) responded to the survey, and 201 were involved in infection prevention. Most respondents were adult infectious diseases physicians (n = 228, 80%) with at least 15 years of experience (n = 174, 63%). Respondents were well distributed among community, academic, and nonuniversity teaching facilities (Table 1). Most respondents reported that their facility routinely used CP for methicillin-resistant Staphylococcus aureus (MRSA, 66%) and vancomycin-resistant Enterococcus (VRE, 69%), compared to 93% and 92% respectively, in the 2014 survey. Nearly all (>90%) reported using contact precautions for Candida auris, carbapenem-resistant Enterobacterales (CRE), and carbapenem-resistant Acinetobacter spp, but there was variability in the use of contact precautions for carbapenem-resistant Pseudomonas aeruginosa and extended-spectrum β-lactamase–producing gram-negative organisms. In 2014, 81% reported that their hospital performed active surveillance testing for MRSA, and in 2022 this rate fell to 54% (Table 2). The duration of contact precautions varied by MDRO (Table 3). Compared to 2014, in 2022 facilities were less likely to use contact precautions indefinitely for MRSA (18% vs 6%) and VRE (31% vs 11%). Also, 180 facilities (90%) performed chlorhexidine bathing in at least some inpatients and 106 facilities (53%) used ultraviolet light or hydrogen peroxide vapor disinfection at discharge in some rooms. Furthermore, 89 facilities (44%) reported institutional changes to contact precautions policies after the start of the COVID-19 pandemic that remain in place. Conclusions: Use of contact precautions for patients with MDROs is heterogenous, and policies vary based on the organism. Although most hospitals still routinely use contact precautions for MRSA and VRE, this practice has declined substantially since 2014. Changes in contact-precaution policies may have been influenced by the COVID-19 pandemic, and more specifically, contemporary public health guidance is needed to define who requires contact precautions and for what duration.
Disclosures: None
Background: Prompt identification of patients colonized or infected with carbapenem-resistant Enterobacterales (CRE) upon admission can help ensure rapid initiation of infection prevention measures and may reduce intrafacility transmission of CRE. The Chicago CDC Prevention Epicenters Program previously created a CRE prediction model using state-wide public health data (doi: 10.1093/ofid/ofz483). We evaluated how well a similar model performed using data from a single academic healthcare system in Atlanta, Georgia, and we sought to determine whether including additional variables improved performance. Methods: We performed a case–control study using electronic medical record data. We defined cases as adult encounters to acute-care hospitals in a 4-hospital academic healthcare system from January 1, 2014, to December 31, 2021, with CRE identified from a clinical culture within the first 3 hospital days. Only the first qualifying encounter per patient was included. We frequency matched cases to control admissions (no CRE identified) from the same hospital and year. Using multivariable logistic regression, we compared 2 models. The “public health model” included 4 variables from the Chicago Epicenters model (age, number of hospitalizations in the prior 365 days, mean length of stay in hospitalizations in the prior 365 days, and hospital admission with an infection diagnosis in the prior 365 days). The “healthcare system model” added 4 additional variables (admission to the ICU in the prior 365 days, malignancy diagnosis, Elixhauser score and inpatient antibiotic days of therapy in the prior 365 days) to the public health model. We used billing codes to determine Elixhauser score, malignancy status, and recent infection diagnoses. We compared model performance using the area under the receiver operating curve (AUC). Results: We identified 105 cases and 441,460 controls (Table 1). CRE was most frequently identified in urine cultures (46%). All 4 variables included in the public health model and the 4 additional variables in the healthcare system model were all significantly associated with being a case in unadjusted analyses (Table 1). The AUC for the public health model was 0.76, and the AUC for the healthcare system model was 0.79 (Table 2; Fig. 1). In both models, a prior admission with an infection diagnosis was the most significant risk factor. Conclusions: A modified CRE prediction model developed using public health data and focused on prior healthcare exposures performed reasonably well when applied to a different academic healthcare system. The addition of variables accessible in large healthcare networks did not meaningfully improve model discrimination.
Disclosures: None
Background: Environmental contamination increases risk for Clostridioides difficile infection (CDI) given that spores can remain on a hospital bed, floor, sink, and light switch despite appropriate cleaning measures. Using real-time asset management software (AgileTrac, GE Healthcare) for beds we examined the risk of a patient developing hospital-onset CDI (HO-CDI) when staying in a hospital bed that had a previous occupant with CDI. Methods: We retrospectively identified all patients in tracked beds from April 2018 to August 2019 to identify hospital-onset CDI (HO-CDI), defined as a positive PCR test for C. difficile in a patient hospitalized for >3 days. A patient was defined as being exposed to a potentially “contaminated” bed if within the preceding 7 days from their HO-CDI diagnosis they resided in a hospital bed that, within the prior 90 days, had held an occupant with CDI (Fig. 1). We used multivariable logistic regression to evaluate the association between being exposed to a contaminated bed and HO-CDI. Model covariates were chosen a priori based on known risk factors for CDI. As a sensitivity analysis, we varied the length of time that a bed could stay contaminated from 90 to 60, 30, 14, and 7 days. Results: We analyzed 25,032 hospital encounters representing 18,860 unique patients; we identified 237 (0.9%) hospital encounters with HO-CDI (Table 1). The Elixhauser comorbidity score, being exposed to a contaminated bed, and receiving antibiotics or a proton pump inhibitor (PPI) during the hospital admission were all associated with HO-CDI in the univariable analysis (Table 2). In the adjusted multivariable model, being exposed to a contaminated bed remained a significant risk factor for HO-CDI (OR, 1.60; 95% CI, 1.22–2.08) even after controlling for known risk factors for CDI including age >65, elevated Elixhauser score, and recent antibiotic or PPI use (Table 2). In the sensitivity analysis in which we adjusted the time a bed was considered contaminated after CDI, being exposed to a contaminated bed remained a risk factor for HO-CDI, with a similar odds ratios as the original model (Table 2). Conclusions: Residing in a hospital bed that contained a previous occupant with CDI is a risk factor for developing HO-CDI. Hospital epidemiologists, infection control personnel, and environmental services staff should consider this association when developing CDI risk mitigation strategies.
Funding: None
Disclosures: None
