National Healthcare Safety Network Research Articles

Association of Surgeon Self-Reported Gender and Surgical Outcomes in Current U.S. Practice.

This study aimed to evaluate the association of surgeon self-reported gender on clinical outcomes in contemporary U.S. surgical practice. Previous research has suggested that there are potentially improved surgical outcomes for female surgeons, yet the underlying causal path for this association remains unclear. Using the Vizient® Clinical Database(2016-2021), 39 operations categorized by the CDC's National Healthcare Safety Network were analyzed. Surgeon self-reported gender was the primary exposure. The primary outcome was a composite of in-hospital death, complications, and/or 30-day readmission. Multivariable logistic regression and propensity score matching was used for risk adjustment. The analysis included 4,882,784 patients operated on by 11,955 female surgeons(33% of surgeons, performing 21% of procedures) and 23,799 male surgeons(67% of surgeons, performing 79% of procedures). Female surgeons were younger(45±9 vs. males-53±11y;P<0.0001) and had lower operative volumes. Unadjusted incidence of the primary outcome was 13.6%(10.7%-female surgeons, 14.3%-male surgeons;P<0.0001). After propensity matching, the primary outcome occurred in 13.0% of patients(12.9%-female, 13.0%-male; OR[M vs. F]=1.02, 95%CI 1.01-1.03;P=.001), with female surgeons having small statistical associations with lower mortality and complication rates but not readmissions. Procedure-specific analyses revealed inconsistent or no surgeon-gender associations. In the largest analysis to date, surgeon self-reported gender had a small statistical, clinically marginal correlation with postoperative outcomes. The variation across surgical specialties and procedures suggests that the association with surgeon gender is unlikely causal for the observed differences in outcomes. Patients should be reassured that surgeon gender alone does not have a clinically meaningful impact on their outcome.

Clinical Outcomes After Admission of Patients With COVID-19 to Skilled Nursing Facilities

During the COVID-19 pandemic, stabilized COVID-19-positive patients were discharged to skilled nursing facilities (SNFs) to alleviate hospital crowding. These discharges generated controversy due to fears of seeding outbreaks, but there is little empirical evidence to inform policy. To assess the association between the admission to SNFs of COVID-19-positive patients and subsequent COVID-19 cases and death rates among residents. This cohort study analyzed survey data from the National Healthcare Safety Network of the Centers for Disease Control and Prevention. The cohort included SNFs in the US from June 2020 to March 2021. Exposed facilities (ie, with initial admission of COVID-19-positive patients) were matched to control facilities (ie, without initial admission of COVID-19-positive patients) in the same county and with similar preadmission case counts. Data were analyzed from June 2023 to February 2024. The week of the first observable admission of COVID-19-positive patients (defined as those previously diagnosed with COVID-19 and continued to require transmission-based precautions) during the study period. Weekly counts of new cases of COVID-19, COVID-19-related deaths, and all-cause deaths per 100 residents in the week prior to the initial admission. A stacked difference-in-differences approach was used to compare outcomes for 10 weeks before and 15 weeks after the first admission. Additional analyses examined whether outcomes differed in facilities with staff or personal protective equipment (PPE) shortages. A matched group of 264 exposed facilities and 518 control facilities was identified. Over the 15-week follow-up period, exposed SNFs had a cumulative increase of 6.94 (95% CI, 2.91-10.98) additional COVID-19 cases per 100 residents compared with control SNFs, a 31.3% increase compared with the sample mean (SD) of 22.2 (26.4). Exposed facilities experienced 2.31 (95% CI, 1.39-3.24) additional cumulative COVID-19-related deaths per 100 residents compared with control facilities, representing a 72.4% increase compared with the sample mean (SD) of 3.19 (5.5). Exposed facilities experiencing potential staff shortage and PPE shortage had larger increases in COVID-19 cases per 100 residents (additional 10.97 [95% CI, 2.76-19.19] cases and additional 14.81 [95% CI, 2.38-27.25] cases, respectively) compared with those without such shortages. This cohort study suggests that admission of COVID-19-positive patients into SNFs early in the pandemic was associated with preventable COVID-19 cases and mortality among residents, particularly in facilities with potential staff and PPE shortages. The findings speak to the importance of equipping SNFs to adhere to infection-control best practices as they continue to face COVID-19 strains and other respiratory diseases.

Results of an Italian multi-Long-Term Care Facilities survey on diagnostic, therapeutic, and infection control topics: state of the art and future perspective

Background and Aims: current trends in the world’s demographic structure indicate increasing requirements for chronic and Long-Term Care Facilities (LTCFs). As the above settings may act as reservoirs for Multidrug-Resistant Organisms (MDROs), it is essential to acquire information about diagnostic, therapeutic, and infection control practices, aiming to optimize strategies for the near future. Materials and Methods: the annual survey form for assessment of the MDRO management in LTCFs and Residential Homes (RHs) for non-self-sufficient elderly people, promoted by the Centers for Disease Control (CDC), and adapted by the version of the American National Healthcare Safety Network, was sent to LTCFs healthcare operators by the AMCLI GLISTer Working Group. Topics considered were interactions with clinical microbiology laboratories, infection prevention/control and antibiotic stewardship practices, and electronic medical records data availability. Results and Conclusions: sixteen structures, mostly from 120 to 280 beds, took part in the survey. We registered that i) MDRO colonization screening is usually not adopted in Italian LTCFs for new admissions (14; 87,5%) at present, and ii) microbiological results are usually provided by an external laboratory service. Furthermore, infection control interventions are regularly adopted, and antibiotic prescriptions are registered only in 62,5% and 75% of structures, respectively. About 70% of facilities do not have a strategy for antibiotic use optimization. Planning of 1st/2nd/3rd level MDRO surveillance programs, enhanced training activities, and improved antibiotic consumption control, whether for prophylaxis, empirical, and targeted therapy, appears of paramount importance in the complex reality of LTCFs.

COVID-19 Vaccination Coverage, and Rates of SARS-CoV-2 Infection and COVID-19-Associated Hospitalization Among Residents in Nursing Homes - National Healthcare Safety Network, United States, October 2023-February 2024.

Nursing home residents are at increased risk for developing severe COVID-19. Nursing homes report weekly facility-level data on SARS-CoV-2 infections, COVID-19-associated hospitalizations, and COVID-19 vaccination coverage among residents to CDC's National Healthcare Safety Network. This analysis describes rates of incident SARS-CoV-2 infection, rates of incident COVID-19-associated hospitalization, and COVID-19 vaccination coverage during October 16, 2023-February 11, 2024. Weekly rates of SARS-CoV-2 infection ranged from 61.4 to 133.8 per 10,000 nursing home residents. The weekly percentage of facilities reporting one or more incident SARS-CoV-2 infections ranged from 14.9% to 26.1%. Weekly rates of COVID-19-associated hospitalization ranged from 3.8 to 7.1 per 10,000 residents, and the weekly percentage of facilities reporting one or more COVID-19-associated hospitalizations ranged from 2.6% to 4.7%. By February 11, 2024, 40.5% of nursing home residents had received a dose of the updated 2023-2024 COVID-19 vaccine that was first recommended in September 2023. Although the peak rate of SARS-CoV-2 infection among nursing home residents was lower during the 2023-24 respiratory virus season than during the three previous respiratory virus seasons, nursing home residents continued to be disproportionately affected by SARS-CoV-2 infection and related severe outcomes. Vaccination coverage remains suboptimal in this population. Ongoing surveillance for SARS-CoV-2 infections and COVID-19-associated hospitalizations in this population is necessary to develop and evaluate evidence-based interventions for protecting nursing home residents.

The National Healthcare Safety Network's digital quality measures: CDC's automated measures for surveillance of patient safety.

This article presents the National Healthcare Safety Network (NHSN)'s approach to automation for public health surveillance using digital quality measures (dQMs) via an open-source tool (NHSNLink) and piloting of this approach using real-world data in a newly established collaborative program (NHSNCoLab). The approach leverages Health Level Seven Fast Healthcare Interoperability Resources (FHIR) application programming interfaces to improve data collection and reporting for public health and patient safety beginning with common, clinically significant, and preventable patient harms, such as medication-related hypoglycemia, healthcare facility-onset Clostridioides difficile infection, and healthcare-associated venous thromboembolism. The NHSN's FHIR dQMs hold the promise of minimizing the burden of reporting, improving accuracy, quality, and validity of data collected by NHSN, and increasing speed and efficiency of public health surveillance.

Association of antibiotic duration and all-cause mortality in a prospective study of patients with ventilator-associated pneumonia in a tertiary-level critical care unit in Southern India

ObjectivesTo estimate all-cause mortality in ventilator-associated pneumonia (VAP) and determine whether antibiotic duration beyond 8 days is associated with reduction in all-cause mortality in patients admitted with VAP in the...

Epidemiology of healthcare-associated Pseudomonas aeruginosa in intensive care units: are sink drains to blame?

Pseudomonas aeruginosa (PA) is a common cause of healthcare-associated infection (PA-HAI) in the intensive care unit (ICU). To describe the epidemiology of PA-HAI in ICUs in Ontario, Canada, and to identify episodes of sink-to-patient PA transmission. This was a prospective cohort study of patients in six ICUs from 2018 to 2019, with retrieval of PA clinical isolates, and PA-screening of antimicrobial-resistant organism surveillance rectal swabs, and of sink drain, air, and faucet samples. All PA isolates underwent whole-genome sequencing. PA-HAI was defined using US National Healthcare Safety Network criteria. ICU-acquired PA was defined as PA isolated from specimens obtained ≥48h after ICU admission in those with prior negative rectal swabs. Sink-to-patient PA transmission was defined as ICU-acquired PA with close genomic relationship to isolate(s) previously recovered from sinks in a room/bedspace occupied 3-14 days prior to collection date of the relevant patient specimen. Over ten months, 72PA-HAIs occurred among 60/4263 admissions. The rate of PA-HAI was 2.40 per 1000 patient-ICU-days; higher in patients who were PA-colonized on admission. PA-HAI was associated with longer stay (median: 26 vs 3 days uninfected; P < 0.001) and contributed to death in 22/60 cases (36.7%). Fifty-eight admissions with ICU-acquired PA were identified, contributing 35/72 (48.6%) PA-HAIs. Four patients with five PA-HAIs (6.9%) had closely related isolates previously recovered from their room/bedspace sinks. Nearly half of PA causing HAI appeared to be acquired in ICUs, and 7% of PA-HAIs were associated with sink-to-patient transmission. Sinks may be an under-recognized reservoir for HAIs.

Comparative epidemiology of hospital-onset bloodstream infections (HOBSIs) and central line-associated bloodstream infections (CLABSIs) across a three-hospital health system.

To evaluate the comparative epidemiology of hospital-onset bloodstream infection (HOBSI) and central line-associated bloodstream infection (CLABSI). Retrospective observational study of HOBSI and CLABSI across a three-hospital healthcare system from 01/01/2017 to 12/31/2021. HOBSIs were identified as any non-commensal positive blood culture event on or after hospital day 3. CLABSIs were identified based on National Healthcare Safety Network (NHSN) criteria. We performed a time-series analysis to assess comparative temporal trends among HOBSI and CLABSI incidence. Using univariable and multivariable regression analyses, we compared demographics, risk factors, and outcomes between non-CLABSI HOBSI and CLABSI, as HOBSI and CLABSI are not exclusive entities. HOBSI incidence increased over the study period (IRR 1.006 HOBSI/1,000 patient days; 95% CI 1.001-1.012; P=.03), while no change in CLABSI incidence was observed (IRR .997 CLABSIs/1,000 central line days, 95% CI .992-1.002, P=.22). Differing demographic, microbiologic, and risk factor profiles were observed between CLABSIs and non-CLABSI HOBSIs. Multivariable analysis found lower odds of mortality among patients with CLABSIs when adjusted for covariates that approximate severity of illness (OR .27; 95% CI .11-.64; P<.01). HOBSI incidence increased over the study period without a concurrent increase in CLABSI in our study population. Furthermore, risk factor and outcome profiles varied between CLABSI and non-CLABSI HOBSI, which suggest that these metrics differ in important ways worth considering if HOBSI is adopted as a quality metric.

Assisting the infection preventionist: Use of artificial intelligence for health care–associated infection surveillance

Health care-associated infection (HAI) surveillance is vital for safety in health care settings. It helps identify infection risk factors, enhancing patient safety and quality improvement. However, HAI surveillance is complex, demanding specialized knowledge and resources. This study investigates the use of artificial intelligence (AI), particularly generative large language models, to improve HAI surveillance. We assessed 2 AI agents, OpenAI's chatGPT plus (GPT-4) and a Mixtral 8×7b-based local model, for their ability to identify Central Line-Associated Bloodstream Infection (CLABSI) and Catheter-Associated Urinary Tract Infection (CAUTI) from 6 National Health Care Safety Network training scenarios. The complexity of these scenarios was analyzed, and responses were matched against expert opinions. Both AI models accurately identified CLABSI and CAUTI in all scenarios when given clear prompts. Challenges appeared with ambiguous prompts including Arabic numeral dates, abbreviations, and special characters, causing occasional inaccuracies in repeated tests. The study demonstrates AI's potential in accurately identifying HAIs like CLABSI and CAUTI. Clear, specific prompts are crucial for reliable AI responses, highlighting the need for human oversight in AI-assisted HAI surveillance. AI shows promise in enhancing HAI surveillance, potentially streamlining tasks, and freeing health care staff for patient-focused activities. Effective AI use requires user education and ongoing AI model refinement.

Hemodialysis-Related Infections: A 4-Year Surveillance

Objective: To analyze the results from the surveillance of hemodialysis-related infections.Material and Methods: Data was prospectively gathered from outpatients attending a hemodialysis unit from April 2019 until March 2023. The National Healthcare Safety Network (NHSN) Dialysis Event Surveillance was used to identify three types of infection-related dialysis events. Event rates were calculated and stratified by vascular access type, standardized infection ratios for bloodstream infections (BSI), intravenous antimicrobial starts, and described pathogens identified among BSI.Results: A total of 2,288 patient-month follow-ups were included. There were 79 infection-related dialysis events (24 BSI; 46 intravenous antimicrobial starts, nine pus, redness, or increased swelling at the vascular access site). The incidence of BSI per 100 patient-months was 1.05 (0.59 arteriovenous fistula, 0.83 arteriovenous graft, and 2.22 central venous catheter). Seventeen BSI were vascular access-related. Access-related BSI per 100 patient-months was 0.74 (0.39 arteriovenous fistula, 0.41 arteriovenous graft, and 1.85 central venous catheter). Intravenous antimicrobial starts per 100 patient-months was 2.01 (0.98 arteriovenous fistula, 2.62 arteriovenous graft, and 3.14 central venous catheter). Most events occurred in patients with a central venous catheter. When benchmarked with the 2014 NHSN, the standardized infection ratio of BSI, access-related BSI, and intravenous antimicrobial starts were 1.40, 1.26, and 0.55, respectively. The most serious outcome was BSI; resulting in 83.3% hospitalizations, 25% loss of vascular access, and 15.8% deaths. Conclusion: Surveillance of infection-related dialysis events is important for prevention. These events were highest among patients with a central venous catheter compared with other vascular access types.

Implementation of a multi-modal intervention adopting new technologies, clinical services, and feedback improves catheter-associated urinary tract infections

ABSTRACT Objectives Catheter-associated urinary tract infections (CAUTI) are a significant cause of morbidity and financial burden to healthcare systems. The aim of this study was to develop and implement a program designed to reduce Foley catheter use days and associated CAUTI rates. Methods A retrospective evaluation of a multi-modal program aimed toward reducing Foley catheter use days and associated CAUTI rates across the Indiana University Health (IUH) system was conducted. IUH is comprised of 16 hospitals and 2,703 available beds. The program included the adoption of new technologies with evidence-based clinical training materials based on change management and feedback from clinicians. National Healthcare Safety Network Standardized Infection Ratio (SIR), Standardized Utilization Ratio (SUR), and Incidence Density Ratio (IDR) rates were assessed. Results Implementation of the multi-modal program resulted in a significant reduction in SIR, SUR, and IDR in the post-implementation compared to the baseline periods. Conclusions Through CAUTI bundle workflow assessments, education, and interventions designed to standardize practice variation, the program was successfully implemented and resulted in a significant decrease in Foley Catheter use days, and associated CAUTI rates.

Transfusion-related errors and associated adverse reactions and blood product wastage as reported to the National Healthcare Safety Network Hemovigilance Module, 2014-2022.

Transfusion-related errors are largely preventable but may lead to blood product wastage and adverse reactions, resulting in patient harm. In the United States, the incidence of transfusion-related errors is poorly understood nationally. We used data from the National Healthcare Safety Network (NHSN) Hemovigilance Module to describe and quantify transfusion-related errors, as well as associated transfusion-related adverse reactions and blood product wastage. During 2014-2022, data from the NHSN Hemovigilance Module were used to analyze errors, including near misses (errors with no transfusion), incidents (errors with transfusion), and associated serious adverse reactions (severe, life-threatening, or death). During 2014-2022, 80 acute care facilities (75 adult; 5 pediatric) reported 63,900 errors. Most errors occurred during patient blood sample collection (21,761, 34.1%) and blood sample handling (16,277, 25.5%). Less than one-fifth of reported errors (9822, 15.4%) had a completed incident form. Of those, 8780 (89.3%) were near misses and 1042 (10.7%) incidents. More than a third of near misses (3363, 38.3%) were associated with a discarded blood product, resulting in 4862 discarded components. Overall, 87 adverse reactions were associated with errors; six (7%) were serious. Over half of the transfusion-related errors reported to the Hemovigilance Module occurred during blood sample collection or sample handling. Some serious adverse reactions identified were associated with errors, suggesting that additional safety interventions may be beneficial. Increased participation in the Hemovigilance Module could enhance generalizability and further inform policy development regarding error prevention.

Unraveling the epidemiology of urinary tract infections in neonates: Perspective from a Brazilian NICU

BackgroundUrinary tract infections (UTIs) can lead to neonatal complications like sepsis, worsened by empirical treatment, contributing to antimicrobial resistance (AMR). This study examined the incidence, etiology, risk factors, and antimicrobial susceptibility of uropathogens in a Neonatal Intensive Care Unit (NICU) in Brazil. MethodsMedical records of neonates hospitalized in the NICU from January 2015 to June 2022 were retrospectively analyzed through the National Healthcare Safety Network system. ResultsAmong 1,474 neonates, 3.9% developed UTI, with an alarming 24-fold increase in incidence from 2015 to 2021. Genitourinary complications (odds ratio = 4.8) were a major risk factor. Of the 71 uropathogens, 74.6% were Gram-negative bacteria (GNB), 21.2% Gram-positive bacteria (GPB), and 4.2% Candida albicans. AMR was notable, with 13.3% of GPB and 20.7% of GNB exhibiting multidrug-resistant (MDR), while 6.6% of GPB and 1.9% of GNB showed extensive drug-resistant (XDR). UTI was associated with prolonged hospitalization (16-59 days). In 57 neonates with UTI, 40.3% had bloodstream infections, elevating the risk of death (odds ratio = 1.8). ConclusionsThe study underscores the urgency of implementing infection prevention and control measures in the NICU to curb rising UTI incidences, combat AMR, and mitigate severe complications in critically ill neonates.

Classification of posttransfusion adverse events using a publicly available artificial intelligence system.

Correct classification of transfusion reactions is important not only for effective patient care and donor management but also for accurate tracking of events in hemovigilance systems. We compared the ability of a generative artificial intelligence (AI) system to correctly diagnose hypothetical clinical situations as transfusion reactions in comparison to previous studies reporting the accuracy of transfusion medicine (TM) specialists in assessing these cases. An AI system was requested to assess 36 case scenarios to provide a diagnosis, severity, and imputability of the transfusion reactions using the CDC National Healthcare Safety Network (NHSN) criteria. Responses were compared to an expert panel's classifications and to the published responses of a panel of TM specialists. Additionally, the AI's responses were compared to the TM specialists' prior attempts to use the TrDDx web-based algorithm for the five most challenging cases. The AI's classification accuracy varied widely depending on the NHSN category. The AI accurately classified all transfusion-associated circulatory overload and transfusion-related acute lung injury cases, exceeding TM specialists' assessments. Conversely, it did not correctly identify any cases in select NHSN categories such as DSTR. Overall accuracy among all diagnostic categories was 48.7% for AI responses versus 72.1% for prior TM specialist responses (p = 0.005). AI-generated responses included non-standard terminology, limited severity assessments, and no imputability determinations. A generative AI system may have a role in helping healthcare providers to consider transfusion reaction categories that might be missed, but caution is advised in applying the AI's output to transfusion reaction classification at present.

Catheter-associated urinary tract infections (CAUTIs) and non-CAUTI hospital-onset urinary tract infections: Relative burden, cost, outcomes and related hospital-onset bacteremia and fungemia infections.

To describe the relative burden of catheter-associated urinary tract infections (CAUTIs) and non-CAUTI hospital-onset urinary tract infections (HOUTIs). A retrospective observational study of patients from 43 acute-care hospitals was conducted. CAUTI cases were defined as those reported to the National Healthcare Safety Network. Non-CAUTI HOUTI was defined as a positive, non-contaminated, non-commensal culture collected on day 3 or later. All HOUTIs were required to have a new antimicrobial prescribed within 2 days of the first positive urine culture. Outcomes included secondary hospital-onset bacteremia and fungemia (HOB), total hospital costs, length of stay (LOS), readmission risk, and mortality. Of 549,433 admissions, 434 CAUTIs and 3,177 non-CAUTI HOUTIs were observed. The overall rate of HOB likely secondary to HOUTI was 3.7%. Total numbers of secondary HOB were higher in non-CAUTI HOUTIs compared to CAUTI (101 vs 34). HOB secondary to non-CAUTI HOUTI was more likely to originate outside the ICU compared to CAUTI (69.3% vs 44.1%). CAUTI was associated with adjusted incremental total hospital cost and LOS of $9,807 (P < .0001) and 3.01 days (P < .0001) while non-CAUTI HOUTI was associated with adjusted incremental total hospital cost and LOS of $6,874 (P < .0001) and 2.97 days (P < .0001). CAUTI and non-CAUTI HOUTI were associated with deleterious outcomes. Non-CAUTI HOUTI occurred more often and was associated with a higher facility aggregate volume of HOB than CAUTI. Patients at risk for UTIs in the hospital represent a vulnerable population who may benefit from surveillance and prevention efforts, particularly in the non-ICU setting.

Infection Control Measure Performance in Long-Term Care Hospitals and Their Relationship to Joint Commission Accreditation

BackgroundThis study evaluated the relationship between Joint Commission accreditation and health care–associated infections (HAIs) in long-term care hospitals (LTCHs). MethodsThis observational study used Centers for Medicare & Medicaid Services (CMS) LTCH data for the period 2017 to June 2021. The standardized infection ratio (SIR) of three measures used by the Centers for Disease Control and Prevention's National Healthcare Safety Network were used as dependent variables in a random coefficient Poisson regression model (adjusting for CMS region, owner type, and bed size quartile): catheter-associated urinary tract infections (CAUTIs), Clostridioides difficile infections (CDIs), and central line–associated bloodstream infections (CLABSIs) for the periods 2017 to 2019 and July 1, 2020, to June 30, 2021. Data from January 1 to June 30, 2020, were excluded due to the COVID-19 pandemic. ResultsThe data set included 244 (73.3%) Joint Commission–accredited and 89 (26.7%) non–Joint Commission–accredited LTCHs. Compared to non–Joint Commission–accredited LTCHs, accredited LTCHs had significantly better (lower) SIRs for CLABSI and CAUTI measures, although no differences were observed for CDI SIRs. There were no significant differences in year trends for any of the HAI measures. For each year of the study period, a greater proportion of Joint Commission–accredited LTCHs performed significantly better than the national benchmark for all three measures (p = 0.04 for CAUTI, p = 0.02 for CDI, p = 0.01 for CLABSI). ConclusionAlthough this study was not designed to establish causality, positive associations were observed between Joint Commission accreditation and CLABSI and CAUTI measures, and Joint Commission–accredited LTCHs attained more consistent high performance over the four-year study period for all three measures. Influencing factors may include the focus of Joint Commission standards on infection control and prevention (ICP), including the hierarchical approach to selecting ICP–related standards as inputs into LTCH policy.

Hospital-acquired infections surveillance: The machine-learning algorithm mirrors National Healthcare Safety Network definitions.

Surveillance of hospital-acquired infections (HAIs) is the foundation of infection control. Machine learning (ML) has been demonstrated to be a valuable tool for HAI surveillance. We compared manual surveillance with a supervised, semiautomated, ML method, and we explored the types of infection and features of importance depicted by the model. From July 2021 to December 2021, a semiautomated surveillance method based on the ML random forest algorithm, was implemented in a Brazilian hospital. Inpatient records were independently manually searched by the local team, and a panel of independent experts reviewed the ML semiautomated results for confirmation of HAI. Among 6,296 patients, manual surveillance classified 183 HAI cases (2.9%), and a semiautomated method found 299 HAI cases (4.7%). The semiautomated method added 77 respiratory infections, which comprised 93.9% of the additional HAIs. The ML model considered 447 features for HAI classification. Among them, 148 features (33.1%) were related to infection signs and symptoms; 101 (22.6%) were related to patient severity status, 51 features (11.4%) were related to bacterial laboratory results; 40 features (8.9%) were related to invasive procedures; 34 (7.6%) were related to antibiotic use; and 31 features (6.9%) were related to patient comorbidities. Among these 447 features, 229 (51.2%) were similar to those proposed by NHSN as criteria for HAI classification. The ML algorithm, which included most NHSN criteria and >200 features, augmented the human capacity for HAI classification. Well-documented algorithm performances may facilitate the incorporation of AI tools in clinical or epidemiological practice and overcome the drawbacks of traditional HAI surveillance.

Comparison of factors associated with seasonal influenza and COVID-19 booster vaccination coverage among healthcare personnel working at acute care hospitals during 2021–2022 influenza season, National Healthcare Safety Network, United States

The simultaneous circulation of seasonal influenza virus and SARS-CoV-2 variants will likely pose unique challenges to public health during the future influenza seasons. Persons who are undergoing treatment in healthcare facilities may be particularly at risk. It is important for healthcare personnel to protect themselves and patients by receiving vaccines. The purpose of this study is to assess coverage of the seasonal influenza vaccine and COVID-19 monovalent booster among healthcare personnel working at acute care hospitals in the United States during the 2021–22 influenza season and to examine the demographic and facility characteristics associated with coverage. A total of 3260 acute care hospitals with over 7 million healthcare personnel reported vaccination data to National Healthcare Safety Network (NHSN) during the 2021–22 influenza season. Two separate negative binomial mixed models were developed to explore the factors associated with seasonal influenza coverage and COVID-19 monovalent booster coverage. At the end of the 2021–2022 influenza season, the overall pooled mean seasonal influenza coverage was 80.3%, and the pooled mean COVID-19 booster coverage was 39.5%. Several demographic and facility-level factors, such as employee type, facility ownership, and geographic region, were significantly associated with vaccination against influenza and COVID-19 among healthcare personnel working in acute care hospitals. Our findings highlight the need to increase the uptake of vaccination among healthcare personnel, particularly non-employees, those working in for-profit and non-medical school-affiliated facilities, and those residing in the South.

An Update From the National Healthcare Safety Network on Hospital Antibiotic Stewardship Programs in the United States, 2014-2021.

In 2014, the Centers for Disease Control and Prevention (CDC) released the Core Elements of Hospital Antibiotic Stewardship Programs (ASPs) and began monitoring uptake through the National Healthcare Safety Network (NHSN) Annual Hospital Survey. In 2019, CDC updated the Core Elements and in 2022 released the Priorities for Hospital Core Element Implementation. We describe Core Element uptake from 2014 to 2021, provide a snapshot of specific ASP practices in acute care hospitals in 2021, and describe how we plan to monitor stewardship moving forward. We used the NHSN Annual Hospital Survey to summarize facility demographics and ASP practices and to monitor uptake of Core Elements. Questions have been updated over time, so not all data could be compared across years. Uptake of all 7 Core Elements increased from 41% in 2014 to 95% in 2021. Uptake of all 6 Priority Elements was 10% in 2021, though 46% of hospitals met 4 or 5 of the possible 6 elements. Antibiotic stewardship was specifically listed in a contract or job description for about 60% of program leaders. The percentage of physician-pharmacist co-led programs rose from 23% to 64%. Seventy-six percent of hospitals reported implementing audit with feedback interventions. With nearly all acute care hospitals reporting uptake of the 7 Core Elements in 2021, and with more evidence for which ASP practices are most effective, the Priorities for Hospital Core Element Implementation were released in 2022 to help enhance the quality and impact of existing ASPs.

Postpartum infections and antimicrobial resistance of responsible pathogens in Ukraine: results a multicenter study (2020-2022).

Aim: To determine the current prevalence of postpartum infections and antimicrobial resistance and antimicrobial resistance of responsible pathogens in Ukraine. Materials and Methods: Multicenter prospective cohort study was conducted from January 2020 to December 2022 in fifteen hospitals from twelve regions of Ukraine. Definitions of healthcare- associated postpartum infection were adapted from the Centers for Disease Control and Prevention's National Healthcare Safety Network. Antibiotic susceptibility was done by the disc diffusion test as recommended by EUCAST. Results: Among 21,968 women, 6,175 (28.1%) postpartum infections were observed. Of all postpartum infection cases, 83.1% were detected after hospital discharge. The postpartum infection rates were 17.3% after cesarean section and 10.8% after vaginal delivery. The most common postpartum infection types were endometritis (17.3%), followed by urinary tract Infection (3.5%), mastitis (3.4%), surgical site infection (excluding endometritis) (2.4%), and episiotomy site infection (1.5%). The predominant postpartum infection pathogens in Ukraine were: Escherichia coli (10.4%), Enterococcus spp. (9.6%), Staphylococcus aureus (6.7%), Pseudomonas aeruginosa (5.8%), Enterobacter spp. (5.8%). In our study pathogens of postpartum infection had differently levels of resistance to antibiotics. Conclusions: Our results indicate that postpartum infections requiring medical attention are common in Ukraine and that most postpartum infections occur after hospital discharge, so that use of routine inpatient surveillance methods alone will lead to underestimation of postpartum infection rates. Optimizing the antibiotic prophylaxis may reduce the burden of postpartum infection, but prevention is the key element.