To save content items to your account,
please confirm that you agree to abide by our usage policies.
If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account.
Find out more about saving content to .
To save content items to your Kindle, first ensure email@example.com
is added to your Approved Personal Document E-mail List under your Personal Document Settings
on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part
of your Kindle email address below.
Find out more about saving to your Kindle.
Note you can select to save to either the @free.kindle.com or @kindle.com variations.
‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi.
‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.
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.
Setting and participants:
“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.
We interviewed 1,208 healthcare workers with positive SARS-CoV-2 tests between October 2020 and June 2021 to determine likely exposure sources. Overall, 689 (57.0%) had community exposures (479 from household members), 76 (6.3%) had hospital exposures (64 from other employees including 49 despite masking), 11 (0.9%) had community and hospital exposures, and 432 (35.8%) had no identifiable source of exposure.
Group Name: CDC Prevention Epicenters Program Background: Reverse-transcriptase polymerase chain reaction (RT-PCR) tests are the reference standard for diagnosing SARS-CoV-2 infection, but false positives can occur and viral RNA may persist for weeks-to-months following recovery. Isolating such patients increases pressure on limited hospital resources and may impede care. Therefore, we quantified the percentage of patients who tested positive by RT-PCR yet were unlikely to be infectious and could be released from isolation. Methods: We prospectively identified all adults hospitalized at Brigham and Women’s Hospital (Boston, MA) who tested positive for SARS-CoV-2 by RT-PCR (primarily Hologic Panther Fusion or Cepheid Xpert platforms) between December 24, 2020, and January 24, 2021. Each case was assessed by infection control staff for possible discontinuation of isolation using an algorithm that incorporated the patient’s prior history of COVID-19, current symptoms, RT-PCR cycle threshold (Ct) values, repeat RT-PCR testing at least 24 hours later, and SARS-CoV-2 serologies (Figure 1). Results: Overall, 246 hospitalized patients (median age, 66 years [interquartile range, 50–74]; 131 [53.3%] male) tested positive for SARS-CoV-2 by RT-PCR during the study period. Of these, 201 (81.7%) were deemed new diagnoses of active disease on the basis of low Ct values and/or progressive symptoms. Moreover, 44 patients (17.9%) were deemed noninfectious: 35 (14.2%) had prior known resolved infections (n = 21) or unknown prior infection but positive serology (n = 14), high Ct values on initial testing, and negative or stably high Ct values on repeat testing. Also, 5 (2.0%) had recent infection but >10 days had passed since symptom onset and they were clinically improving. In addition, 4 (1.6%) results were deemed false positives based on lack of symptoms and at least 1 negative repeat RT-PCR test (Figure 2). One patient was asymptomatic with Ct value <35 but was discharged before further testing could be obtained. Among the 44 noninfectious patients, isolation was discontinued a median of 3 days (IQR, 2–4) after the first positive test. We did not identify any healthcare worker infections attributable to early discontinuation of isolation in these patients. Conclusions: During the winter COVID-19 second surge in Massachusetts, nearly 1 in 5 hospitalized patients who tested positive for SARS-CoV-2 by RT-PCR were deemed noninfectious and eligible for discontinuation of precautions. Most of these cases were consistent with residual RNA from prior known or undiagnosed infections. Active assessments of SARS-CoV-2 RT-PCR tests by infection control practitioners using clinical data, Ct values, repeat tests, and serologies can safely validate the release many patients from isolation and thereby conserve resources and facilitate patient care.
Elective surgical patients routinely bathe with chlorhexidine gluconate (CHG) at home days prior to their procedures. However, the impact of home CHG bathing on surgical site CHG concentration is unclear. We examined 3 different methods of applying CHG and hypothesized that different application methods would impact resulting CHG skin concentration.
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.
We report on COVID-19 risk among HCWs exposed to a patient diagnosed with COVID-19 on day 13 of hospitalization. There were 44 HCWs exposed to the patient before contact and droplet precautions were implemented: of these, 2 of 44 (5%) developed COVID-19 potentially attributable to the exposure.
US hospitals are engaged in an infection control arms race. Hospitals, specialties, and professional groups are spurring one another on to adopt progressively more aggressive measures in response to COVID-19 that often exceed federal and international standards. Examples include universal masking of providers and patients; decreasing thresholds to test asymptomatic patients; using face shields and N95 respirators regardless of symptoms and test results; novel additions to the list of aerosol-generating procedures; and more comprehensive personal protective equipment including hair, shoe, and leg covers. Here, we review the factors underlying this arms race, including fears about personal safety, ongoing uncertainty around how SARS-CoV-2 is transmitted, confusion about what constitutes an aerosol-generating procedure, increasing recognition of the importance of asymptomatic infection, and the limited accuracy of diagnostic tests. We consider the detrimental effects of a maximal infection control approach and the research studies that are needed to eventually de-escalate hospitals and to inform more evidence-based and measured strategies.
Timely identification of outbreaks of hospital-associated infections is needed to implement control measures and minimize impact. Survey results from 33 hospitals indicated that most hospitals lacked a formal cluster definition and all targeted a very limited group of prespecified pathogens. Standardized, statistically based outbreak detection could greatly improve current practice.
Infect. Control Hosp. Epidemiol. 2016;37(4):466–468
Email your librarian or administrator to recommend adding this to your organisation's collection.