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Background: Surgical site infection (SSI) following spinal fusion is a major complication that increases mortality and healthcare costs, with Staphylococcus aureus being the most common pathogen. Implementation of an SSI prevention bundle has been reported to significantly reduce SSI rates. This study evaluated changes in SSI rates among orthopedic spinal fusion patients after applying a prevention bundle that included S. aureus decolonization therapy and compared the results with non-intervention neurosurgical spinal fusion patients during the same period. Methods: At a tertiary care hospital in Seoul with 2,432 beds, an enhanced SSI prevention bundle including S. aureus decolonization therapy was implemented for all orthopedic spinal fusion patients beginning in December 2023. Decolonization consisted of a chlorhexidine shower the day before or the day of surgery and intranasal application of povidone-iodine within two hours before surgery. Additional bundle components included the use of colored inner gloves during auto-graft bone preparation to detect glove tearing, and additional sterile drapes when using a C-arm. The intervention was accompanied by staff education, promotion activities, awareness surveys, and ongoing monitoring. SSI rates were compared between the pre-intervention period (January-November 2023) and post-intervention period (December 2023 – October 2024) in orthopedic patients and further compared with neurosurgical surgery to evaluate the intervention’s effectiveness. Pediatric patients who underwent scoliosis surgery and patients with infections present at the time of surgery (PATOS) were excluded from the study. Diagnosis was based on the updated 2023 CDC/NHSN (National Healthcare Safety Network) definitions. Result: In the orthopedic department, the SSI rates significantly decreased from 2.65% (14/529) before the intervention to 0.34% (1/295) after the intervention (p=0.01). In the non-intervention neurosurgery department, there was no significant difference in SSI rates between the two periods (3.05% [12/393] vs. 2.16% [5/231], p=0.62). Although the post-intervention SSI rates in orthopedics were lower than that in neurosurgery (0.34% vs. 2.16%), the difference was not statistically significant (p=0.09). The SSI rates caused by Staphylococcus spp. in orthopedics decreased from 2.27% (12/529) before the intervention to 0% (0/295) after the intervention (p=0.01). In neurosurgery, there was no significant change in the incidence of SSIs caused by Staphylococcus spp. between the two periods (1.53% [6/393] vs. 1.30% [3/231], p<0.99). Conclusion: The enhanced SSI prevention bundle, which included S. aureus decolonization therapy, effectively reduced the SSI rate in orthopedic fusion surgeries. This effect was not observed in the neurosurgery department without the intervention, supporting the effectiveness of the implemented bundle.
Background: National Healthcare Safety Network (NHSN) data demonstrate substantial declines over time in most hospital-acquired infections (HAIs). In contrast, surgical site infection (SSI) rates have remained largely unchanged, raising concerns regarding the effectiveness of current prevention strategies. Public reporting and pay-for-performance programs primarily focus on Centers for Medicare & Medicaid Services (CMS)–mandated procedures, notably abdominal hysterectomy (HYST) and colon surgery (COLO). It remains unclear whether stagnant SSI rates reflect trends limited to these procedures or represent a broader phenomenon across surgical populations. We sought to determine whether temporal stagnation in SSI rates is confined to CMS-mandated procedures. Methods We conducted a retrospective cohort study using the Duke Infection Control Outreach Network (DICON) database, which includes standardized NHSN SSI surveillance data from participating hospitals. We included hospitals that were continuously enrolled in DICON for the entire study period (January 1, 2020–December 31, 2024). Procedures were excluded if a hospital performed fewer than 100 of a given procedure type in any calendar year. NHSN procedure categories were excluded entirely if <1,000 procedures were performed during the study period. Procedures were grouped as Group 1 (CMS-mandated HYST and COLO) and Group 2 (all other included procedures). Temporal trends in SSI rates were assessed using regression-based models with an interaction term between calendar year and procedure group, adjusting for age, sex, and wound class. Results A total of 64 hospitals met the inclusion criteria. The final dataset included 561,811 total procedures, of which 71,573 (12.7%) were in Group 1 (HYST/COLO) and 490,238 (87.3%) were in Group 2. SSI rates were 3.49 per 100 procedures in Group 1 and 0.77 per 100 procedures in Group 2. (Figure 1) Over the study period, annual SSI rates did not change significantly in Group 1 (annual rate ratio [aRR] 1.03; 95% CI 0.99–1.06, p=0.06) or Group 2 ([aRR] 1.01; 95% CI .98-1.03). (Figure 2) Moreover, there was no significant difference in annual SSI rates between COLO/HYST and other procedures (difference in annual trend aRR 0.98; 95% CI 0.94–1.02; p=0.25). Discussion In this multicenter analysis, stagnation in SSI rates was observed among both CMS-mandated procedures and non-mandated surgical procedures, suggesting that stable SSI rates reflect broader challenges in SSI prevention rather than artifacts related to surveillance or reporting. These findings highlight the need for novel, risk-adjusted prevention strategies.
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.
Introduction: Surgical site infections (SSI) account for 20% of all healthcare-associated infections (HAIs). SSI are associated with increased patient mortality, morbidity, length of hospitalization and risk of readmission with an associated total healthcare cost of $3.3 billion annually. Of the reported SSIs, colorectal (COLO) surgeries have been among the highest risk procedures. A crucial element to prevent HAI is the accurate identification of true events. This allows for comparison of facility-specific performance metrics with peer groups and the development and implementation of targeted interventions to reduce SSI burden. In this abstract, we assessed the impact of multidisciplinary intervention to accurately identify, and document visualized infections on SSI rates and Standardized Infection Ratios (SIR) at a tertiary care teaching facility. Methods: A quasi-experimental study was conducted from 02/01/2024 to 06/30/2025 to improve the accuracy of operative note documentation of visualized infections. Indicators to evaluate possible SSI events included readmission(s), chief complaint, surgical logs, diagnostic codes, antibiotic use, lab/culture data and keywords that may indicate a surgical site infection. Subsequently identified procedures were evaluated by the Surveillance Infection Preventionist team, the Medical Director of Infection Prevention, and the Surgical Quality Officer. If indicated, the operating surgeon was consulted to determine whether visualized infection criteria were met and/or correctly documented. Turnaround time for case reviews was determined. We compared the observed SSI rates and Standardized Infection Ratios (SIR) with the uncorrected rates and SIR accounting for the clarified events. Results: Of 1,142 procedures, 55 were initially identified as COLO SSI. After multidisciplinary review, 12 (22%) of the 55 were found to be present at the time of surgery (PATOS). An additional two events (4%) were determined to not meet SSI criteria. The table summarizes the results of the review process showing a statistically significant correction in SSI event rates from 4.82/100 surgeries to 3.59/100 surgeries (p<0.0001). The turnaround time from identification of a potential SSI event to final determination was four days (range: 1-6 days). Conclusion: Surgical site infection events are critical quality outcome measures for both regulatory and reputational agencies. However, the misclassification of SSIs, such as observed in our study, can create a misleading picture of institutional performance, divert attention from preventable cases, and undermine confidence in surveillance systems. Our multidisciplinary approach ensures a more accurate identification of SSI events. This method can serve as a model for enhancing surveillance accuracy and reliability across healthcare settings.
Background: Socio-economic factors may play a role in patients’ risk for healthcare associated infections (HAI), including surgical site infections (SSI). The Area Deprivation Index (ADI) is a mapping tool that characterizes neighborhood-level socioeconomic disadvantage based on income, education, employment, and housing quality. A higher ADI score indicates a greater disadvantage. ADI has been studied extensively as a predictor for various patient outcomes. The purpose of this study was to establish whether an association exists between the national ADI score and SSI following abdominal hysterectomy at a 1400-bed academic center in northeast Ohio. Methods: At the Cleveland Clinic, SSIs are identified by Infection Preventionists using NHSN surveillance definitions. Using our surveillance software (EPIC, Bugsy, Verona, Wisconsin), we extracted all NHSN-defined inpatient abdominal hysterectomy procedures in adult patients between January 1, 2023 and December 31, 2024, including those with and without an SSI. We queried our electronic medical record data warehouse for the national ADI score recorded closest to the surgical procedure. Patients without an ADI score were excluded. Characteristics of patients with and without a SSI were summarized. Categorical variables were compared using a chi-square test and a two-tailed t-test was used to compare mean ADI scores. Results: During the study period 682 patients met criteria that underwent an inpatient abdominal hysterectomy, 26 developed an NHSN-defined SSI, including 15 superficial incisional SSI and 11 organ space SSI. Counts and attack rates of SSI, stratified by relevant factors are shown in the Table. Patients with an SSI had an average ADI of 71.81 while patients with no SSI had an average ADI of 62.29 (p=0.0573). Conclusion: In this study, the mean ADI trended higher for patients who developed a SSI following an abdominal hysterectomy, indicating a possible association between socio-economic factors and risk of a SSI. Although the test for difference in mean ADI did not achieve statistical significance, it highlights ADI as a readily obtainable patient characteristic that may contribute to post-operative infection risk. Further exploration of obtainable patient characteristics, including ADI score, is beneficial to inform best practice for optimal outcomes following an abdominal hysterectomy procedure.
Background: Multidrug-resistant organisms (MDROs) are prevalent in skilled nursing facilities (SNFs), but patterns of pathogen movement in these facilities are not well understood. We used genomic surveillance to identify MDRO presence on environmental surfaces, focusing on shared mobile medical equipment (MME) and patient room surfaces, to assess contamination patterns in a skilled nursing facility setting. Methods: We conducted environmental microbiological sampling in an 18-bed floor of a community-based, ventilator-capable SNF over four consecutive days. We collected composite samples of patient room environments and five types of shared MME: glucometers, vital signs carts, shower chairs, shower beds, and patient lifts. The timing and location of each sampling event were recorded. 105 samples were collected, cultured on selective media (for methicillin-resistant Staphylococcus aureus (MRSA), vancomycin-resistant Enterococcus species, extended-spectrum ?-lactamase producing Enterobacteriaceae, and multidrug-resistant Acinetobacter species), and presumptive MDRO isolates were MALDI-TOF confirmed and whole genome sequenced. We assessed pairwise genomic relatedness using split-kmer analysis for any MDRO species that was isolated at least twice. We established genomic clusters based on two criteria: the proportion of kmers that matched between isolates (< 0.9) and a single-nucleotide polymorphism (SNP) threshold for each MDRO species based on established literature (e.g., < 7 SNPs). Results: In the 105 samples, four MDROs were detected more than once: vancomycin-resistant Enterococcus faecium (VREfm; N=17), methicillin-resistant Staphylococcus aureus (MRSA; N=9), Klebsiella pneumoniae (KP; N=4), and Acinetobacter baumannii (AB; N=2). To date, MRSA isolates have not been whole genome sequenced and are thus excluded from our results. MDRO detection rates varied across species and sample locations (Table 1). To assess whether these detections indicated pathogen movement, we assessed genomic relatedness (Figure 1) and found: 12 out of 17 VREfm isolates belonged to three multi-isolate clusters of sizes ranging from two to seven isolates; three out of four KP isolates formed a single cluster; while the two AB isolates were unrelated. All four clusters that were detected in the study included isolates collected from shared MME. Three of the four clusters detected involved samples taken in more than one location within the ward, with the largest cluster involving VRE isolates collected from five unique patient rooms and three equipment types stored in a hallway (Figure 1). Conclusions: Our findings suggest that MME may be an important reservoir for MDROs and can facilitate pathogen movement between patient rooms in SNFs. Adequate cleaning of shared MME is necessary to address the risk of fomite-based
Background: Carbapenemase-producing Enterobacterales (CPE) spread silently and offer limited treatment options, making contact screening essential. We evaluated the real-world implementation of contact screening, focusing on screening timing (at exposure vs. at readmission) and positivity rates by room type and carbapenemase enzyme. Methods: This retrospective observational study was conducted at a 2,700-bed tertiary care hospital and included patients identified as contacts of CPE index cases with exposure between 1 January and 31 May 2024. Contacts were defined as patients who shared a room or occupied open beds in the same intensive care unit (ICU) with an index case during the period from 4 days before the index patient’s positive specimen collection until the reporting of results. Screening was performed with stool or rectal swabs either at the time of exposure or upon readmission within 6 months. CPE acquisition was defined as detection of isolates harbouring the same carbapenemase enzyme as the corresponding index case. Acquisition rates were compared by screening timing, room type, and carbapenemase enzyme. Samples were processed using culture or the Xpert Carba-R assay. Result: Among 2,003 contacts linked to 336 index patients, 1,401 (70%) underwent screening, of whom 37 (2.6%) tested positive. Immediate screening identified 30/1,184 (2.5%) positives, while readmission screening detected 7/217 (3.2%); two additional acquisitions were identified from clinical specimens among unscreened readmitted contacts. Acquisition was more frequent after exposure to KPC-producing organisms than NDM-producing organisms (3.7% [26/706] vs. 1.5% [8/520]; p=0.02) and higher in multi-patient rooms than in two-bed or open-bed ICU settings combined (3.4% [34/1004] vs 0.8% [3/397]; p=0.006). Conclusion: CPE acquisition was more likely following exposure to KPC-producing organisms and in multi-patient rooms. Systematic readmission screening identified additional carriers who would otherwise have been missed. These findings support tailored infection-prevention strategies that consider carbapenemase enzyme and the physical care environment, particularly in healthcare systems where multi-patient rooms are common.
Background: Hospital onset bacteremia and fungemia (HOB) is under development as a Center for Disease Control and Prevention (CDC) automated digital quality measure. We evaluated the use of generative artificial intelligence (GenAI) in assessing attribution and preventability of HOB. Methods This is a retrospective cohort study of hospitalized patients with positive blood cultures at 11 US Veterans Affairs (VA) hospitals. We included a random sample of positive blood cultures collected after day 3 of admission where admission date is day 1. GenAI (OpenAI GPT-4o) was prompted to provide the most likely source and adjudicate the preventability of HOB using standardized prompts and clinical data (GenAI). IP experts used GenAI output to provide GenAI-assisted determinations of source and preventability of HOB (IP-AI). HOB event preventability was rated by IP expert using a 6-point Likert scale, responses were combined to 3 categories for analysis. We compared initial GenAI with IP-AI determinations of source and preventability. A separate human-only review by a different IP expert was performed (IP-alone) on a random subset to compare time required for review. Results We included 52 HOB events for IP-AI and a subset of 21 for IP-alone. Enterobacterales were the most common organisms (22/52, 42%) followed by Staphylococcus aureus (9/52, 17%). Median duration to HOB event was 12 (IQR 5 – 31) days. GenAI most commonly attributed bacteremia to gastrointestinal (GI) (14/52, 26.9%), followed by genitourinary (GU) (12/52, 23%), central line (CLABSI) (8/52, 15%), and skin and soft tissue infections (SSTI) (7/52, 14%) sources (Figure 1). IP-AI determination of source agreed with GenAI source in 67% (35/52) of cases. Most agreement between IP-AI and GenAI occurred when HOB was attributed to a bone and joint infection (2/2, 100%), followed by GI source (13/14, 93%), SSTI (5/7, 71%), and GU source (7/12, 58%). Most disagreements (17/52, 33%) occurred when IP-AI attribution was to an unknown source (5/17, 29%) or blood culture contamination (3/17, 18%). GenAI adjudication of preventability matched IP-AI adjudication in 20/52 (39%). The median time to complete an IP-AI review was 11.5 (IQR 6 – 20.5) minutes compared to a median of 25 (IQR 13 – 33) minutes for IP-alone. Conclusion IP experts often agreed with GenAI for source of HOB but disagreed with its preventability. GenAI rarely acknowledged unknown source. Using GenAI for HOB detection is faster than human review but must account for differences in preventability.
Background: Hospital-acquired infections remain a significant challenge for healthcare systems despite well-established infection prevention (IP) strategies, with ventilator-associated infections (VAI) being particularly challenging. This is especially true for pediatric patients, who often require mechanical ventilation for extended periods, increasing the risk of infection. Gram-negative bacteria, primarily Pseudomonas aeruginosa, are the leading causes of VAI. Traditional IP strategies are often reactive and may miss potential transmissions within the hospital, particularly for pathogens that are endemic to the hospital environment. Whole-genome sequencing (WGS) and genomic epidemiology have demonstrated value in identifying such events in adult hospitals, but their application in pediatric facilities remains limited. Here, we conduct a retrospective analysis to determine how this technology could be utilized in a pediatric facility. Methods: We conducted a retrospective genomic surveillance study of Gram-negative isolates collected between 2020 and 2024 at a pediatric tertiary hospital. Briefly, clones were selected by colony morphology, cultured, and DNA was extracted. DNA libraries were prepared and sequenced on the Illumina NextSeq 1000 platform. Phylogenetic analyses were performed using core-genome alignment pipelines. Likely transmission events were defined as bacterial clones differing by fewer than 10 single-nucleotide polymorphisms, based on previously described literature. Genetically related clones were evaluated with patient location and temporal hospital metadata to assess epidemiologic linkage. Results: A total of 687 clones from roughly 500 clinical samples were sequenced. We identified over 50 groups of genetically related isolates from multiple Gram-negative species. P. aeruginosa accounted for most of the genetically related groups, with over 40 discovered. Additional pathogens suspected to have genetic relatedness include Serratia marcescens, Klebsiella pneumoniae, Stenotrophomonas maltophilia, and Achromobacter xylosoxidans. These groups range in size from 2 to 15 patients. Pairwise genomic analysis confirmed at least 16 groups consistent with likely transmission events. To date, epidemiologic review has confirmed that 2 of these groups were plausible based on temporal and geographic relationships between patients. The remaining 14 groups are under investigation. Conclusion: WGS-based surveillance identified multiple transmission events that were not recognized by conventional IP methods, highlighting the added value of WGS for hospital surveillance. This is especially true in the case of pathogens that are commonly found in hospitals. Integrating WGS with genomic epidemiology and hospital metadata can enhance patient safety and reduce healthcare costs. These findings indicate that reliance on traditional surveillance alone may underestimate the true burden of hospital outbreaks, particularly those caused by common Gram-negative respiratory pathogens in pediatric populations.
Purpose: Wastewater surveillance effectively monitors pathogens. This pilot study evaluated the feasibility and utility of hospital-level wastewater surveillance by integrating wastewater and electronic health record (EHR) data. Analyses focused on hospital-acquired infections (HAI) and temporal lags between wastewater and clinical detection. Methods: From August to December 2024, wastewater autosamplers operated across five hospital pavilions at Yale New Haven Hospital, collecting samples every five minutes during a 24-hour period three times per week. Samples were analyzed by dPCR for SARS-CoV-2, Influenza (A/B), and additional pathogens. Deidentified EHR data included admissions, diagnoses, and laboratory data. The primary focus was lab-confirmed HAI SARS-CoV-2 and Influenza. HAI was defined as infections diagnosed during hospitalization without evidence at admission. Clinical and wastewater data were used to calculate the proportion of positive samples, and correlation was assessed using Spearman’s rank correlation coefficient (rho). Correlations were evaluated for lagged associations across 1–3-week lags. A sensitivity analysis was conducted by including all SARS-CoV-2 encounters. Results: Among 33,579 patient encounters, 62 SARS-CoV-2 and 74 Influenza HAI encounters were identified. This corresponded to 97 and 148 tests for SARS-CoV-2 and Influenza, respectively. Of 187 wastewater samples collected 60 (31.1%) and 1 (0.5%) were positive for SARS-CoV-2 and Influenza respectively. Due to only a single detection of Influenza in the wastewater, correlation analysis was limited to SARS-CoV-2. A correlation test of the data found no statistically significant correlation between wastewater and clinical data when aligned temporally (rho: -0.21, p: 0.51). Lagged correlations between wastewater and clinical SARS-CoV-2 positivity were evaluated across 1–3-week temporal lags. These lagged correlations were not statistically significant, but rho increased in magnitude from a 1-week lag (rho: -0.04, p: 0.89) to a 3-weeks lag (rho: 0.46, p:0.21). The sensitivity analysis found no statistically significant correlation between wastewater and clinical positivity. Conclusions: Hospital-level wastewater surveillance shows potential as an early indicator of HAI SARS-CoV-2 infections, with exploratory trends suggesting a ~3-week lead time results in stronger associations between clinical and wastewater data. Although limited by small HAI sample sizes and a short wastewater sampling period, these findings support further evaluation in larger cohorts and highlights pathogen-specific limitations as observed for Influenza. Follow-up studies should employ longer wastewater sampling windows and further refine methods to account for community-associated SARS-CoV-2 contributions to hospital wastewater, an area of active investigation by our group.
