To evaluate the impact of administering probiotics to prevent Clostridioides difficile infection (CDI) among patients receiving therapeutic antibiotics.

Stepped-wedge cluster-randomized trial between September 1, 2016, and August 31, 2019.

This study was conducted in 4 acute-care hospitals across an integrated health region.

Hospitalized patients, aged ≥55 years.

Patients were given 2 probiotic capsules daily (Bio-K+, Laval, Quebec, Canada), containing 50 billion colony-forming units of Lactobacillus acidophilus CL1285, L. casei LBC80R, and L. rhamnosus CLR2. We measured hospital-acquired CDI (HA-CDI) and the number of positive C. difficile tests per 10,000 patient days as well as adherence to administration of Bio-K+ within 48 and 72 hours of antibiotic administration. Mixed-effects generalized linear models, adjusted for influenza admissions and facility characteristics, were used to evaluate the impact of the intervention on outcomes.

Overall adherence of Bio-K+ administration ranged from 76.9% to 84.6% when stratified by facility and periods. Rates of adherence to administration within 48 and 72 hours of antibiotic treatment were 60.2% –71.4% and 66.7%–75.8%, respectively. In the adjusted analysis, there was no change in HA-CDI (incidence rate ratio [IRR], 0.92; 95% confidence interval [CI], 0.68–1.23) or C. difficile positivity rate (IRR, 1.05; 95% CI, 0.89–1.24). Discharged patients may not have received a complete course of Bio-K+. Our hospitals had a low baseline incidence of HA-CDI. Patients who did not receive Bio-K+ may have differential risks of acquiring CDI, introducing selection bias.

Hospitals considering probiotics as a primary prevention strategy should consider the baseline incidence of HA-CDI in their population and timing of probiotics relative to the start of antimicrobial administration.

To systematically review the methodology, performance, and generalizability of diagnostic models for predicting the risk of healthcare-facility–onset (HO) Clostridioides difficile infection (CDI) in adult hospital inpatients (aged ≥18 years).

CDI is the most common cause of healthcare-associated diarrhea. Prediction models that identify inpatients at risk of HO-CDI have been published; however, the quality and utility of these models remain uncertain.

Two independent reviewers evaluated articles describing the development and/or validation of multivariable HO-CDI diagnostic models in an inpatient setting. All publication dates, languages, and study designs were considered. Model details (eg, sample size and source, outcome, and performance) were extracted from the selected studies based on the CHARMS checklist. The risk of bias was further assessed using PROBAST.

Of the 3,030 records evaluated, 11 were eligible for final analysis, which described 12 diagnostic models. Most studies clearly identified the predictors and outcomes but did not report how missing data were handled. The most frequent predictors across all models were advanced age, receipt of high-risk antibiotics, history of hospitalization, and history of CDI. All studies reported the area under the receiver operating characteristic curve (AUROC) as a measure of discriminatory ability. However, only 3 studies reported the model calibration results, and only 2 studies were externally validated. All of the studies had a high risk of bias.

The studies varied in their ability to predict the risk of HO-CDI. Future models will benefit from the validation on a prospective external cohort to maximize external validity.

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.

Monitoring antibiotic consumption is a key component to steer antimicrobial stewardship programs, including in nursing homes. We analyzed changes in antibiotic consumption in French nursing homes during 5 years, including the COVID-19 pandemic, to identify potential priorities for improvement.

A multicenter survey was conducted between 2018 and 2022.

The study was conducted across 220 French nursing homes with on-site pharmacies.

Antibiotic consumption data were collected from pharmacy records and are expressed as defined daily doses per 1,000 resident days. Antibiotic indicators promoted by health authorities were calculated from quantitative data to evaluate the quality of prescribing.

Antibiotic consumption significantly decreased between 2018 and 2022, particularly during the coronavirus disease 2019 (COVID-19) pandemic, despite a slight increase in 2022. During the study period, the most used antibiotic classes were penicillins (61.9% in 2022) followed by cephalosporins (10.5%), macrolides–lincosamides–streptogramins (7.3%) then fluoroquinolones (7.0%). Amoxicillin–clavulanic acid was the most consumed antibiotic; amoxicillin and ceftriaxone ranked second and third. Azithromycin consumption increased from 2020, as did the indicator regarding broad-spectrum antibiotics.

The decreasing trend in antibiotic use and control of fluoroquinolone use over the study period suggest compliance with antibiotic use guidelines. However, changes in the use of broad-spectrum antibiotics and the substantial use of amoxicillin-clavulanic acid, although it is rarely a first-line antibiotic, highlight the need for antimicrobial stewardship activities and the usefulness of antibiotic consumption surveillance to identify priorities.

We compared the number of blood-culture events before and after the introduction of a blood-culture algorithm and provider feedback. Secondary objectives were the comparison of blood-culture positivity and negative safety signals before and after the intervention.

Prospective cohort design.

Two surgical intensive care units (ICUs): general and trauma surgery and cardiothoracic surgery

Patients aged ≥18 years and admitted to the ICU at the time of the blood-culture event.

We used an interrupted time series to compare rates of blood-culture events (ie, blood-culture events per 1,000 patient days) before and after the algorithm implementation with weekly provider feedback.

The blood-culture event rate decreased from 100 to 55 blood-culture events per 1,000 patient days in the general surgery and trauma ICU (72% reduction; incidence rate ratio [IRR], 0.38; 95% confidence interval [CI], 0.32–0.46; P < .01) and from 102 to 77 blood-culture events per 1,000 patient days in the cardiothoracic surgery ICU (55% reduction; IRR, 0.45; 95% CI, 0.39–0.52; P < .01). We did not observe any differences in average monthly antibiotic days of therapy, mortality, or readmissions between the pre- and postintervention periods.

We implemented a blood-culture algorithm with data feedback in 2 surgical ICUs, and we observed significant decreases in the rates of blood-culture events without an increase in negative safety signals, including ICU length of stay, mortality, antibiotic use, or readmissions.

Incidence and risk factors for recurrent Clostridioides difficile infection (rCDI) are well established in adults, though data are lacking in pediatrics. We aimed to determine incidence of and risk factors for rCDI in pediatrics.

This retrospective cohort study of pediatric patients was conducted at 3 tertiary-care hospitals in Canada with laboratory-confirmed CDI between April 1, 2012, and March 31, 2017. rCDI was defined as an episode of CDI occurring 8 weeks or less from diagnostic test date of the primary episode. We used logistic regression to determine and quantify risk factors significantly associated with rCDI.

In total, 286 patients were included in this study. The incidence proportion for rCDI was 12.9%. Among hospitalized patients, the incidence rate was estimated at 2.6 cases of rCDI per 1,000 hospital days at risk (95% confidence interval [CI], 1.7–3.9). Immunocompromised patients had higher incidence of rCDI (17.5%; P = .03) and higher odds of developing rCDI independently of antibiotic treatment given for the primary episode (odds ratio [OR], 2.31; 95% CI, 1.12–5.09). Treatment with vancomycin monotherapy did not show statistically significant protection from rCDI, independently of immunocompromised status (OR, 0.33; 95% CI, 0.05–1.15]).

The identification of increased risk of rCDI in immunocompromised pediatric patients warrants further research into alternative therapies, prophylaxis, and prevention strategies to prevent recurrent disease burden within these groups. Treatment of the initial episode with vancomycin did not show statistically significant protection from rCDI.

Determination of whether vascular catheter disinfecting antiseptic-containing caps alone are effective at decreasing microbial colonization of connectors compared to antiseptic-containing caps plus a 5-second alcohol manual disinfection.

The study was conducted in a 718-bed, tertiary-care, academic hospital.

A convenience sample of adult patients across intensive care units and acute care wards with peripheral and central venous catheters covered with antiseptic-containing caps.

Quality improvement study completed over 5 days. The standard-of-care group consisted of catheter connectors with antiseptic-containing caps cleaned with a 5-second alcohol wipe scrub prior to culture. The comparison group consisted of catheter connectors with antiseptic-containing caps without a 5-second alcohol wipe scrub prior to culture. The connectors were pressed directly onto blood agar plates and incubated. Plates were assessed for growth after 48-72 hours.

In total, 356 catheter connectors were cultured: 165 in the standard-of-care group, 165 in the comparison group, and 26 catheters connectors without an antiseptic-containing cap, which were designated as controls. Overall, 18 catheter connectors (5.06%) yielded microbial growth. Of the 18 connectors with microbial growth, 2 (1.21%) were from the comparison group, 1 (0.61%) was from the standard-of-care group, and 15 were controls without an antiseptic-containing cap.

Bacterial colonization rates were similar between the catheter connectors cultured with antiseptic-containing caps alone and catheter connectors with antiseptic-containing caps cultured after a 5-second scrub with alcohol. This finding suggests that the use of antiseptic-containing caps precludes the need for additional disinfection.

To evaluate the risk of surgical site infection (SSI) following complicated appendectomy in individual patients receiving delayed primary closure (DPC) versus primary closure (PC) after adjustment for individual risk factors.

Secondary analysis of randomized controlled trial (RCT) with prediction model.

Referral centers across Thailand.

Adult patients who underwent appendectomy via a lower-right-quadrant abdominal incision due to complicated appendicitis.

A secondary analysis of a published RCT was performed applying a counterfactual prediction model considering interventions (PC vs DPC) and other significant predictors. A multivariable logistic regression was applied, and a likelihood-ratio test was used to select significant predictors to retain in a final model. Factual versus counterfactual SSI risks for individual patients along with individual treatment effect (iTE) were estimated.

In total, 546 patients (271 PC vs 275 DPC) were included in the analysis. The individualized prediction model consisted of allocated intervention, diabetes, type of complicated appendicitis, fecal contamination, and incision length. The iTE varied between 0.4% and 7% for PC compared to DPC; ∼38.1% of patients would have ≥2.1% lower SSI risk following PC compared to DPC. The greatest risk reduction was identified in diabetes with ruptured appendicitis, fecal contamination, and incision length of 10 cm, where SSI risks were 47.1% and 54.1% for PC and DPC, respectively.

In this secondary analysis, we found that most patients benefited from early PC versus DPC. Findings may be used to inform SSI prevention strategies for patients with complicated appendicitis.

US policies require robust nursing home (NH) infection prevention and control (IPC) programs to ensure safe care. We assessed IPC resources and practices related to catheter and non–catheter-associated urinary tract infection (CAUTI and UTI) prevention among NHs.

We conducted a mixed-methods study from April 2018 through November 2019. Quantitative surveys assessed NH IPC program resources, practices, and communication during resident transfer. Semistructured qualitative interviews focused on IPC programs, CAUTI and UTI prevention practices, and resident transfer processes. Using a matrix as an analytic tool, findings from the quantitative survey data were combined with the qualitative data in the form of a joint display.

Representatives from 51 NHs completed surveys; interviews were conducted with 13 participants from 7 NHs. Infection preventionists (IPs) had limited experience and/or additional roles, and in 36.7% of NHs, IPs had no specific IPC training. IP turnover was often mentioned during interviews. Most facilities were aware of their CAUTI and UTI rates and reported using prevention practices, such as hydration (85.7%) or nurse-initiated catheter discontinuation (65.3%). Qualitative interviewees confirmed use of these practices and expressed additional concerns about overuse of urine testing and antibiotics. Although transfer sheets were used by 84% to communicate about infections, the information received was described as suboptimal.

NHs identified IP challenges related to turnover, limited education, and serving multiple roles. However, most NHs reported awareness of their CAUTI and UTI rates as well as their use of prevention practices. Importantly, we identified opportunities to enhance communication between NHs and hospitals to improve resident care and safety.

The number of hospitalized patients with severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) does not differentiate between patients admitted due to coronavirus disease 2019 (COVID-19) (ie, primary cases) and incidental SARS-CoV-2 infection (ie, incidental cases). We developed an adaptable method to distinguish primary cases from incidental cases upon hospital admission.

Retrospective cohort study.

Data were obtained from 3 German tertiary-care hospitals.

The study included patients of all ages who tested positive for SARS-CoV-2 by a standard quantitative reverse-transcription polymerase chain reaction (RT-PCR) assay upon admission between January and June 2022.

We present 2 distinct models: (1) a point-of-care model that can be used shortly after admission based on a limited range of parameters and (2) a more extended point-of-care model based on parameters that are available within the first 24–48 hours after admission. We used regression and tree-based classification models with internal and external validation.

In total, 1,150 patients were included (mean age, 49.5±28.5 years; 46% female; 40% primary cases). Both point-of-care models showed good discrimination with area under the curve (AUC) values of 0.80 and 0.87, respectively. As main predictors, we used admission diagnosis codes (ICD-10-GM), ward of admission, and for the extended model, we included viral load, need for oxygen, leucocyte count, and C-reactive protein.

We propose 2 predictive algorithms based on routine clinical data that differentiate primary COVID-19 from incidental SARS-CoV-2 infection. These algorithms can provide a precise surveillance tool that can contribute to pandemic preparedness. They can easily be modified to be used in future pandemic, epidemic, and endemic situations all over the world.

Evaluation of adult antibiotic order sets (AOSs) on antibiotic stewardship metrics has been limited. The primary outcome was to evaluate the standardized antimicrobial administration ratio (SAAR). Secondary outcomes included antibiotic days of therapy (DOT) per 1,000 patient days (PD); selected antibiotic use; AOS utilization; Clostridioides difficile infection (CDI) cases; and clinicians’ perceptions of the AOS via a survey following the final study phase.

This 5-year, single-center, quasi-experimental study comprised 5 phases from 2017 to 2022 over 10-month periods between August 1 and May 31.

The study was conducted in a 752-bed tertiary care, academic medical center.

Our institution implemented AOSs in the electronic medical record (EMR) for common infections among hospitalized adults.

For the primary outcome, a statistically significant decreases in SAAR were detected from phase 1 to phase 5 (1.0 vs 0.90; P < .001). A statistically significant decreases were detected in DOT per 1,000 PD (4,884 vs 3,939; P = .001), fluoroquinolone orders (407 vs 175; P < .001), carbapenem orders (147 vs 106; P = .024), and clindamycin orders (113 vs 73; P = .01). No statistically significant change in mean vancomycin orders was detected (991 vs 902; P = .221). A statistically significant decrease in CDI cases was also detected (7.8, vs 2.4; P = .002) but may have been attributable to changes in CDI case diagnosis. Clinicians indicated that the AOSs were easy to use overall and that they helped them select the appropriate antibiotics.

Implementing AOS into the EMR was associated with a statistically significant reduction in SAAR, antibiotic DOT per 1,000 PD, selected antibiotic orders, and CDI cases.

The days of therapy (DOT) metric, used to estimate antimicrobial consumption, has some limitations. Days of antibiotic spectrum coverage (DASC), a novel metric, overcomes these limitations. We examined the difference between these 2 metrics of inpatient intravenous antimicrobial consumption in assessing antimicrobial stewardship efficacy and antimicrobial resistance using vector autoregressive (VAR) models with time-series analysis.

Differences between DOT and DASC were investigated at a tertiary-care center over 8 years using VAR models with 3 variables in the following order: (1) the monthly proportion of prospective audit and feedback (PAF) acceptance as an index of antimicrobial stewardship efficacy; (2) monthly DOT and DASC adjusted by 1,000 days present as indices of antimicrobial consumption; and (3) the monthly incidence of 5 organisms as an index of antimicrobial resistance.

The Granger causality test, which evaluates whether incorporating lagged variables can help predict other variables, showed that PAF activity contributed to DOT and DASC, which, in turn, contributed to the incidence of drug-resistant P. aeruginosa. Notably, only DASC helped predict the incidence of drug-resistant Enterobacterales. Another VAR analysis demonstrated that a high proportion of PAF acceptance was accompanied by decreased DASC in a given month, whereas increased DASC was accompanied by an increased incidence of drug-resistant Enterobacterales, unlike with DOT.

The VAR models of PAF activity, antimicrobial consumption, and antimicrobial resistance suggested that DASC may more accurately reflect the impact of PAF on antimicrobial consumption and be superior to DOT for predicting the incidence of drug-resistant Enterobacterales.

Despite the increasing rates of carbapenem-resistant Acinetobacter baumannii (CRAB) carriage among hospitalized patients in endemic settings, the role of active surveillance cultures and cohorting is still debated. We sought to determine the long-term effect of a multifaceted infection-control intervention on the incidence of CRAB in an endemic setting.

A prospective, quasi-experimental study was performed at a 670-bed, acute-care hospital. The study consisted of 4 phases. In phase I, basic infection control measures were used. In phase II, CRAB carriers were cohorted in a single ward with dedicated nursing and enhanced environmental cleaning. In phase III large-scale screening in high-risk units was implemented. Phase IV comprised a 15-month follow-up period.

During the baseline period, the mean incidence rate (IDR) of CRAB was 44 per 100,000 patient days (95% CI, 37.7–54.1). No significant decrease was observed during phase II (IDR, 40.8 per 100,000 patient days; 95% CI, 30.0–56.7; P = .97). During phase III, despite high compliance with control measures, ongoing transmission in several wards was observed and the mean IDR was 53.9 per 100,000 patient days (95% CI, 40.5–72.2; P = .55). In phase IV, following the implementation of large-scale screening, a significant decrease in the mean IDR was observed (25.8 per 100,000 patient days; 95% CI, 19.9–33.5; P = .03). An overall reduction of CRAB rate was observed between phase I and phase IV (rate ratio, 0.6; 95% CI, 0.4–0.9; P < .001).

The comprehensive intervention that included intensified control measures with routine active screening cultures was effective in reducing the incidence of CRAB in an endemic hospital setting.

To assess the relative risk of hospital-onset Clostridioides difficile (HO-CDI) during each month of the early coronavirus disease 2019 (COVID-19) pandemic and to compare it with historical expectation based on patient characteristics.

This study used a retrospective cohort design. We collected secondary data from the institution’s electronic health record (EHR).

The Ohio State University Wexner Medical Center, Ohio, a large tertiary healthcare system in the Midwest.

All adult patients admitted to the inpatient setting between January 2018 and May 2021 were eligible for the study. Prisoners, children, individuals presenting with Clostridioides difficile on admission, and patients with <4 days of inpatient stay were excluded from the study.

After controlling for patient characteristics, the observed numbers of HO-CDI cases were not significantly different than expected. However, during 3 months of the pandemic period, the observed numbers of cases were significantly different from what would be expected based on patient characteristics. Of these 3 months, 2 months had more cases than expected and 1 month had fewer.

Variations in HO-CDI incidence seemed to trend with COVID-19 incidence but were not fully explained by our case mix. Other factors contributing to the variability in HO-CDI incidence beyond listed patient characteristics need to be explored.

To evaluate the incidence of a candidate definition of healthcare facility-onset, treated Clostridioides difficile (CD) infection (cHT-CDI) and to identify variables and best model fit of a risk-adjusted cHT-CDI metric using extractable electronic heath data.

We analyzed 9,134,276 admissions from 265 hospitals during 2015–2020. The cHT-CDI events were defined based on the first positive laboratory final identification of CD after day 3 of hospitalization, accompanied by use of a CD drug. The generalized linear model method via negative binomial regression was used to identify predictors. Standardized infection ratios (SIRs) were calculated based on 2 risk-adjusted models: a simple model using descriptive variables and a complex model using descriptive variables and CD testing practices. The performance of each model was compared against cHT-CDI unadjusted rates.

The median rate of cHT-CDI events per 100 admissions was 0.134 (interquartile range, 0.023–0.243). Hospital variables associated with cHT-CDI included the following: higher community-onset CDI (CO-CDI) prevalence; highest-quartile length of stay; bed size; percentage of male patients; teaching hospitals; increased CD testing intensity; and CD testing prevalence. The complex model demonstrated better model performance and identified the most influential predictors: hospital-onset testing intensity and prevalence, CO-CDI rate, and community-onset testing intensity (negative correlation). Moreover, 78% of the hospitals ranked in the highest quartile based on raw rate shifted to lower percentiles when we applied the SIR from the complex model.

Hospital descriptors, aggregate patient characteristics, CO-CDI burden, and clinical testing practices significantly influence incidence of cHT-CDI. Benchmarking a cHT-CDI metric is feasible and should include facility and clinical variables.

Although the incidence of hospital-associated respiratory virus infection (HARVI) is well recognized, the risk factors for infection and impact on patient outcomes are not well characterized.

We identified a cohort of all inpatient admissions ≥24 hours duration at a single academic medical center from 2017 to 2020. HARVI were defined as respiratory virus detected in a test ordered after the 95th percentile of the virus-specific incubation period. Risk factors for HARVI were assessed using Cox proportional hazards models of the competing outcomes of HARVI and discharge. The associations between time-varying HARVI status and the rates of ICU admission, discharge, and in-hospital death were estimated using Cox-proportional hazards models in a competing risk framework.

HARVI incidences were 8.8 and 3.0 per 10,000 admission days for pediatric and adult patients, respectively. For adults, congestive heart failure, renal disease, and cancer increased HARVI risk independent of their associations with length of stay. HARVI risk was also elevated for patients admitted in September–June relative to July admissions. For pediatric patients, cardiovascular and respiratory conditions, cancer, medical device dependence, and admission in December increased HARVI risk. Lengths of stay were longer for adults with HARVI compared to those without, and hospital-associated influenza A was associated with increased risk of death. Rates of ICU admission were increased in the 5 days after HARVI identification for adult and pediatric patients. HARVI was not associated with length of stay or death among pediatric patients.

HARVI is associated chronic health conditions and increases morbidity and mortality.

Vancomycin is often initiated in hospitalized patients; however, it may be unnecessary or continued for longer durations than needed. Oversight of all vancomycin orders may not be feasible with widespread prescribing and strategies to enlist other clinicians to serve as stewards of vancomycin use are needed. We implemented 2 sequential interventions: a protocol in which the pharmacist orders MRSA nasal swab followed by a protocol requiring approval from pharmacists to continue vancomycin for >72 hours.

In this single-center, retrospective, quasi-experimental study, we evaluated vancomycin use after implementation of a pharmacy-driven MRSA nasal-swab ordering protocol and a vancomycin 72-hour restriction protocol. The primary outcome was the change in the standardized antibiotic administration ratio (SAAR) for antibacterial agents for resistant gram-positive infections. We also evaluated the impact on antibiotic utilization.

Following the MRSA swab protocol, the SAAR decreased from 1.26 to 1.13 (P < .001; 95% confidence interval [CI], 1.16–1.25). After the 72-hour approval process, the SAAR was 0.96 (P < .001; 95% CI, 1.0–1.12). Vancomycin utilization decreased from 138.9 to 125.3 days of therapy per 1,000 patient days following the MRSA swab protocol (P < .001) and to 112.7 (P < .001) following the 72-hour approval protocol. Interrupted time-series analysis identified a similar rate of decline in utilization following the 2 interventions (−0.3 and −0.5; P = .16). Both interventions combined resulted in a significant reduction (−1.5; P < .001).

Implementation of a pharmacist-driven MRSA nasal-swab ordering protocol, followed by a 72-hour approval protocol, was associated with a significant reduction in the SAAR for antibiotics used in the treatment of resistant gram-positive infections and a reduction in vancomycin utilization. Leveraging the oversight of primary service clinical pharmacists through these protocols proved to be an effective strategy.

The gold standard for hand hygiene (HH) while wearing gloves requires removing gloves, performing HH, and donning new gloves between WHO moments. The novel strategy of applying alcohol-based hand rub (ABHR) directly to gloved hands might be effective and efficient.

A mixed-method, multicenter, 3-arm, randomized trial.

Adult and pediatric medical-surgical, intermediate, and intensive care units at 4 hospitals.

Healthcare personnel (HCP).

HCP were randomized to 3 groups: ABHR applied directly to gloved hands, the current standard, or usual care.

Gloved hands were sampled via direct imprint. Gold-standard and usual-care arms were compared with the ABHR intervention.

Bacteria were identified on gloved hands after 432 (67.4%) of 641 observations in the gold-standard arm versus 548 (82.8%) of 662 observations in the intervention arm (P < .01). HH required a mean of 14 seconds in the intervention and a mean of 28.7 seconds in the gold-standard arm (P < .01). Bacteria were identified on gloved hands after 133 (98.5%) of 135 observations in the usual-care arm versus 173 (76.6%) of 226 observations in the intervention arm (P < .01). Of 331 gloves tested 6 (1.8%) were found to have microperforations; all were identified in the intervention arm [6 (2.9%) of 205].

Compared with usual care, contamination of gloved hands was significantly reduced by applying ABHR directly to gloved hands but statistically higher than the gold standard. Given time savings and microbiological benefit over usual care and lack of feasibility of adhering to the gold standard, the Centers for Disease Control and Prevention and the World Health Organization should consider advising HCP to decontaminate gloved hands with ABHR when HH moments arise during single-patient encounters.

Trial Registration: NCT03445676.

We sought to determine whether increased antimicrobial use (AU) at the onset of the coronavirus disease 2019 (COVID-19) pandemic was driven by greater AU in COVID-19 patients only, or whether AU also increased in non–COVID-19 patients.

In this retrospective observational ecological study from 2019 to 2020, we stratified inpatients by COVID-19 status and determined relative percentage differences in median monthly AU in COVID-19 patients versus non–COVID-19 patients during the COVID-19 period (March–December 2020) and the pre–COVID-19 period (March–December 2019). We also determined relative percentage differences in median monthly AU in non–COVID-19 patients during the COVID-19 period versus the pre–COVID-19 period. Statistical significance was assessed using Wilcoxon signed-rank tests.

The study was conducted in 3 acute-care hospitals in Chicago, Illinois.

Hospitalized patients.

Facility-wide AU for broad-spectrum antibacterial agents predominantly used for hospital-onset infections was significantly greater in COVID-19 patients versus non–COVID-19 patients during the COVID-19 period (with relative increases of 73%, 66%, and 91% for hospitals A, B, and C, respectively), and during the pre–COVID-19 period (with relative increases of 52%, 64%, and 66% for hospitals A, B, and C, respectively). In contrast, facility-wide AU for all antibacterial agents was significantly lower in non–COVID-19 patients during the COVID-19 period versus the pre–COVID-19 period (with relative decreases of 8%, 7%, and 8% in hospitals A, B, and C, respectively).

AU for broad-spectrum antimicrobials was greater in COVID-19 patients compared to non–COVID-19 patients at the onset of the pandemic. AU for all antibacterial agents in non–COVID-19 patients decreased in the COVID-19 period compared to the pre–COVID-19 period.

Nursing home residents may be particularly vulnerable to coronavirus disease 2019 (COVID-19). Therefore, a question is when and how often nursing homes should test staff for COVID-19 and how this may change as severe acute respiratory coronavirus virus 2 (SARS-CoV-2) evolves.

We developed an agent-based model representing a typical nursing home, COVID-19 spread, and its health and economic outcomes to determine the clinical and economic value of various screening and isolation strategies and how it may change under various circumstances.

Under winter 2023–2024 SARS-CoV-2 omicron variant conditions, symptom-based antigen testing averted 4.5 COVID-19 cases compared to no testing, saving $191 in direct medical costs. Testing implementation costs far outweighed these savings, resulting in net costs of $990 from the Centers for Medicare & Medicaid Services perspective, $1,545 from the third-party payer perspective, and $57,155 from the societal perspective. Testing did not return sufficient positive health effects to make it cost-effective [$50,000 per quality-adjusted life-year (QALY) threshold], but it exceeded this threshold in ≥59% of simulation trials. Testing remained cost-ineffective when routinely testing staff and varying face mask compliance, vaccine efficacy, and booster coverage. However, all antigen testing strategies became cost-effective (≤$31,906 per QALY) or cost saving (saving ≤$18,372) when the severe outcome risk was ≥3 times higher than that of current omicron variants.

SARS-CoV-2 testing costs outweighed benefits under winter 2023–2024 conditions; however, testing became cost-effective with increasingly severe clinical outcomes. Cost-effectiveness can change as the epidemic evolves because it depends on clinical severity and other intervention use. Thus, nursing home administrators and policy makers should monitor and evaluate viral virulence and other interventions over time.