Vancomycin-resistant enterococci (VRE) can cause serious healthcare-associated infections. Patients can become colonized and infected through contact with healthcare workers, hospital surfaces, equipment, and other patients. We evaluated the utility of broadly applied whole-genome sequencing (WGS) surveillance of vancomycin-resistant Enterococcus faecium (VREfm) for detection of hospital-based transmission.

Retrospective genomic and epidemiologic analysis of clinical VREfm isolates

Brigham and Women’s Hospital, an 800-bed tertiary care center in Boston, MA, USA

VREfm was isolated from patient screening and diagnostic specimens. We sequenced the genomes of 156 VREfm isolates, 12 at the request of infection control and 144 as a convenience sample, and used single nucleotide polymorphism (SNP) differences to assess relatedness. For isolate pairs separated by 15 or fewer SNPs by two orthogonal comparison methods, we mapped epidemiologic connections to identify putative transmission clusters.

We found evidence for 16 putative transmission clusters comprising between two and four isolates each and involving 41/156 isolates (26.3%). Our analysis discovered 14 clusters that were missed by traditional surveillance methods and additional members of two clusters that were detected by traditional methods. Patients in four transmission clusters were linked only by exposure to the postanesthesia care unit.

We show that WGS surveillance for VREfm can support infection control investigations and detect transmission events missed by routine surveillance methods. We identify the postanesthesia care unit as a locus for VREfm transmission, which demonstrates how WGS surveillance could inform targeted interventions to prevent the spread of VREfm.

To characterize the relationship between chlorhexidine gluconate (CHG) skin concentration and skin microbial colonization.

Serial cross-sectional study.

Adult patients in medical intensive care units (ICUs) from 7 hospitals; from 1 hospital, additional patients colonized with carbapenemase-producing Enterobacterales (CPE) from both ICU and non-ICU settings. All hospitals performed routine CHG bathing in the ICU.

Skin swab samples were collected from adjacent areas of the neck, axilla, and inguinal region for microbial culture and CHG skin concentration measurement using a semiquantitative colorimetric assay. We used linear mixed effects multilevel models to analyze the relationship between CHG concentration and microbial detection. We explored threshold effects using additional models.

We collected samples from 736 of 759 (97%) eligible ICU patients and 68 patients colonized with CPE. On skin, gram-positive bacteria were cultured most frequently (93% of patients), followed by Candida species (26%) and gram-negative bacteria (20%). The adjusted odds of microbial recovery for every twofold increase in CHG skin concentration were 0.84 (95% CI, 0.80–0.87; P < .001) for gram-positive bacteria, 0.93 (95% CI, 0.89–0.98; P = .008) for Candida species, 0.96 (95% CI, 0.91–1.02; P = .17) for gram-negative bacteria, and 0.94 (95% CI, 0.84–1.06; P = .33) for CPE. A threshold CHG skin concentration for reduced microbial detection was not observed.

On a cross-sectional basis, higher CHG skin concentrations were associated with less detection of gram-positive bacteria and Candida species on the skin, but not gram-negative bacteria, including CPE. For infection prevention, targeting higher CHG skin concentrations may improve control of certain pathogens.

To assess whether measurement and feedback of chlorhexidine gluconate (CHG) skin concentrations can improve CHG bathing practice across multiple intensive care units (ICUs).

A before-and-after quality improvement study measuring patient CHG skin concentrations during 6 point-prevalence surveys (3 surveys each during baseline and intervention periods).

The study was conducted across 7 geographically diverse ICUs with routine CHG bathing.

Adult patients in the medical ICU.

CHG skin concentrations were measured at the neck, axilla, and inguinal region using a semiquantitative colorimetric assay. Aggregate unit-level CHG skin concentration measurements from the baseline period and each intervention period survey were reported back to ICU leadership, which then used routine education and quality improvement activities to improve CHG bathing practice. We used multilevel linear models to assess the impact of intervention on CHG skin concentrations.

We enrolled 681 (93%) of 736 eligible patients; 92% received a CHG bath prior to survey. At baseline, CHG skin concentrations were lowest on the neck, compared to axillary or inguinal regions (P < .001). CHG was not detected on 33% of necks, 19% of axillae, and 18% of inguinal regions (P < .001 for differences in body sites). During the intervention period, ICUs that used CHG-impregnated cloths had a 3-fold increase in patient CHG skin concentrations as compared to baseline (P < .001).

Routine CHG bathing performance in the ICU varied across multiple hospitals. Measurement and feedback of CHG skin concentrations can be an important tool to improve CHG bathing practice.

To assess coronavirus disease 2019 (COVID-19) infection policies at leading US medical centers in the context of the initial wave of the severe acute respiratory coronavirus virus 2 (SARS-CoV-2) omicron variant.

Electronic survey study eliciting hospital policies on masking, personal protective equipment, cohorting, airborne-infection isolation rooms (AIIRs), portable HEPA filters, and patient and employee testing.

“Hospital epidemiologists from U.S. News top 20 hospitals and 10 hospitals in the CDC Prevention Epicenters program.”  As it is currently written, it implies all 30 hospitals are from the CDC Prevention Epicenters program, but that only applies to 10 hospitals.  Alternatively, we could just say “Hospital epidemiologists from 30 leading US hospitals.”

Survey results were reported using descriptive statistics.

Of 30 hospital epidemiologists surveyed, 23 (77%) completed the survey between February 15 and March 3, 2022. Among the responding hospitals, 18 (78%) used medical masks for universal masking and 5 (22%) used N95 respirators. 16 hospitals (70%) required universal eye protection. 22 hospitals (96%) used N95s for routine COVID-19 care and 1 (4%) reserved N95s for aerosol-generating procedures. 2 responding hospitals (9%) utilized dedicated COVID-19 wards; 8 (35%) used mixed COVID-19 and non–COVID-19 units; and 13 (57%) used both dedicated and mixed units. 4 hospitals (17%) used AIIRs for all COVID-19 patients, 10 (43%) prioritized AIIRs for aerosol-generating procedures, 3 (13%) used alternate risk-stratification criteria (not based on aerosol-generating procedures), and 6 (26%) did not routinely use AIIRs. 9 hospitals (39%) did not use portable HEPA filters, but 14 (61%) used them for various indications, most commonly as substitutes for AIIRs when unavailable or for specific high-risk areas or situations. 21 hospitals (91%) tested asymptomatic patients on admission, but postadmission testing strategies and preferred specimen sites varied substantially. 5 hospitals (22%) required regular testing of unvaccinated employees and 1 hospital (4%) reported mandatory weekly testing even for vaccinated employees during the SARS-CoV-2 omicron surge.

COVID-19 infection control practices in leading hospitals vary substantially. Clearer public health guidance and transparency around hospital policies may facilitate more consistent national standards.

To assess the utility of an automated, statistically-based outbreak detection system to identify clusters of hospital-acquired microorganisms.

Multicenter retrospective cohort study.

The study included 43 hospitals using a common infection prevention surveillance system.

A space–time permutation scan statistic was applied to hospital microbiology, admission, discharge, and transfer data to identify clustering of microorganisms within hospital locations and services. Infection preventionists were asked to rate the importance of each cluster. A convenience sample of 10 hospitals also provided information about clusters previously identified through their usual surveillance methods.

We identified 230 clusters in 43 hospitals involving Gram-positive and -negative bacteria and fungi. Half of the clusters progressed after initial detection, suggesting that early detection could trigger interventions to curtail further spread. Infection preventionists reported that they would have wanted to be alerted about 81% of these clusters. Factors associated with clusters judged to be moderately or highly concerning included high statistical significance, large size, and clusters involving Clostridioides difficile or multidrug-resistant organisms. Based on comparison data provided by the convenience sample of hospitals, only 9 (18%) of 51 clusters detected by usual surveillance met statistical significance, and of the 70 clusters not previously detected, 58 (83%) involved organisms not routinely targeted by the hospitals’ surveillance programs. All infection prevention programs felt that an automated outbreak detection tool would improve their ability to detect outbreaks and streamline their work.

Automated, statistically-based outbreak detection can increase the consistency, scope, and comprehensiveness of detecting hospital-associated transmission.