Stethoscope barriers narrative review; It's time for a strategy unfriendly to multi-drug resistant organisms (MDROs).

The current standard of stethoscope hygiene doesn't eliminate the transmission of harmful pathogens, including multi-drug resistant organisms (MDROs). In the era of the increasing prevalence of MDRO infections, the use of new systems providing touch free barriers may improve patient safety versus traditional stethoscope cleaning practices with chemical agents. Our purpose was to provide a narrative literature review regarding barriers as an improvement over the current standard of care for stethoscope hygiene. Searching PubMed, articles were identified if they were in English and published after 1990, using the search term "stethoscope barrier", or if they were from a previously published stethoscope hygiene article using "author's name + stethoscope". Included articles evaluated or discussed stethoscope barriers. Of 28 manuscripts identified, 15 met the inclusion criteria. Barriers were considered superior to alternatives if they were single use, disposable, applied in a touch free fashion, were impervious to pathogens, provided an aseptic patient contact, and were acoustically invisible. Use of a practitioner's personal stethoscope with a disposable diaphragm barrier should be recommended as a new standard of care as this represents an improvement in patient safety and patient experience when compared to the disposable stethoscope or isopropyl alcohol stethoscope diaphragm cleaning.

Integrated Genomic and Social Network Analyses of SARS-CoV-2 Transmission in the Healthcare Setting.

BACKGROUND: Infection prevention (IP) measures are designed to mitigate the transmission of pathogens in healthcare. Using large-scale viral genomic and social network analyses, we determined if IP measures used during the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic were adequate in protecting healthcare workers (HCWs) and patients from acquiring SARS-CoV-2. METHODS: We performed retrospective cross-sectional analyses of viral genomics from all available SARS-CoV-2 viral samples collected at UC San Diego Health and social network analysis using the electronic medical record to derive temporospatial overlap of infections among related viromes and supplemented with contact tracing data. The outcome measure was any instance of healthcare transmission, defined as cases with closely related viral genomes and epidemiological connection within the healthcare setting during the infection window. Between November 2020 through January 2022, 12 933 viral genomes were obtained from 35 666 patients and HCWs. RESULTS: Among 5112 SARS-CoV-2 viral samples sequenced from the second and third waves of SARS-CoV-2 (pre-Omicron), 291 pairs were derived from persons with a plausible healthcare overlap. Of these, 34 pairs (12%) were phylogenetically linked: 19 attributable to household and 14 to healthcare transmission. During the Omicron wave, 2106 contact pairs among 7821 sequences resulted in 120 (6%) related pairs among 32 clusters, of which 10 were consistent with healthcare transmission. Transmission was more likely to occur in shared spaces in the older hospital compared with the newer hospital (2.54 vs 0.63 transmission events per 1000 admissions, P < .001). CONCLUSIONS: IP strategies were effective at identifying and preventing healthcare SARS-CoV-2 transmission.

Annual N95 respirator fit-testing: an unnecessary burden on healthcare.

US regulations mandate annual N95 mask fit testing for healthcare workers, but the optimal testing interval is unknown. In our study using data from 12,565 healthcare workers, the probability of survival free from fit-test failure after 3 years was 99.4%, suggesting that less frequent fit testing every 3 years would be safe.

Assessing past versus present severe acute respiratory coronavirus virus 2 (SARS-CoV-2) infection: A survey of criteria for discontinuing precautions in asymptomatic patients testing positive on admission.

Infection prevention program leaders report frequent use of criteria to distinguish recently recovered coronavirus disease 2019 (COVID-19) cases from actively infectious cases when incidentally positive asymptomatic patients were identified on routine severe acute respiratory coronavirus virus 2 (SARS-CoV-2) polymerase chain reaction (PCR) testing. Guidance on appropriate interpretation of high-sensitivity molecular tests can prevent harm from unnecessary precautions that delay admission and impede medical care.

Hepatitis C Virus Micro-elimination Among People With HIV in San Diego: Are We on Track?

Background: Rising incidence of hepatitis C virus (HCV) among people with HIV (PWH) in San Diego County (SDC) was reported. In 2018, the University of California San Diego (UCSD) launched a micro-elimination initiative among PWH, and in 2020 SDC launched an initiative to reduce HCV incidence by 80% across 2015-2030. We model the impact of observed treatment scale-up on HCV micro-elimination among PWH in SDC. Methods: A model of HCV transmission among people who inject drugs (PWID) and men who have sex with men (MSM) was calibrated to SDC. The model was additionally stratified by age, gender, and HIV status. The model was calibrated to HCV viremia prevalence among PWH in 2010, 2018, and 2021 (42.1%, 18.5%, and 8.5%, respectively), and HCV seroprevalence among PWID aged 18-39 years, MSM, and MSM with HIV in 2015. We simulate treatment among PWH, weighted by UCSD Owen Clinic (reaching 26% of HCV-infected PWH) and non-UCSD treatment, calibrated to achieve the observed HCV viremia prevalence. We simulated HCV incidence with observed and further treatment scale-up (+/- risk reductions) among PWH. Results: Observed treatment scale-up from 2018 to 2021 will reduce HCV incidence among PWH in SDC from a mean of 429 infections/year in 2015 to 159 infections/year in 2030. County-wide scale-up to the maximum treatment rate achieved at UCSD Owen Clinic (in 2021) will reduce incidence by 69%, missing the 80% incidence reduction target by 2030 unless accompanied by behavioral risk reductions. Conclusions: As SDC progresses toward HCV micro-elimination among PWH, a comprehensive treatment and risk reduction approach is necessary to reach 2030 targets.

A Risk-Based Clinical Decision Support System for Patient-Specific Antimicrobial Therapy (iBiogram): Design and Retrospective Analysis.

BACKGROUND: There is a pressing need for digital tools that can leverage big data to help clinicians select effective antibiotic treatments in the absence of timely susceptibility data. Clinical presentation and local epidemiology can inform therapy selection to balance the risk of antimicrobial resistance and patient risk. However, data and clinical expertise must be appropriately integrated into clinical workflows. OBJECTIVE: The aim of this study is to leverage available data in electronic health records, to develop a data-driven, user-centered, clinical decision support system to navigate patient safety and population health. METHODS: We analyzed 5 years of susceptibility testing (1,078,510 isolates) and patient data (30,761 patients) across a large academic medical center. After curating the data according to the Clinical and Laboratory Standards Institute guidelines, we analyzed and visualized the impact of risk factors on clinical outcomes. On the basis of this data-driven understanding, we developed a probabilistic algorithm that maps these data to individual cases and implemented iBiogram, a prototype digital empiric antimicrobial clinical decision support system, which we evaluated against actual prescribing outcomes. RESULTS: We determined patient-specific factors across syndromes and contexts and identified relevant local patterns of antimicrobial resistance by clinical syndrome. Mortality and length of stay differed significantly depending on these factors and could be used to generate heuristic targets for an acceptable risk of underprescription. Combined with the developed remaining risk algorithm, these factors can be used to inform clinicians' reasoning. A retrospective comparison of the iBiogram-suggested therapies versus the actual prescription by physicians showed similar performance for low-risk diseases such as urinary tract infections, whereas iBiogram recognized risk and recommended more appropriate coverage in high mortality conditions such as sepsis. CONCLUSIONS: The application of such data-driven, patient-centered tools may guide empirical prescription for clinicians to balance morbidity and mortality with antimicrobial stewardship.

SARS-CoV-2 detection status associates with bacterial community composition in patients and the hospital environment.

BACKGROUND: SARS-CoV-2 is an RNA virus responsible for the coronavirus disease 2019 (COVID-19) pandemic. Viruses exist in complex microbial environments, and recent studies have revealed both synergistic and antagonistic effects of specific bacterial taxa on viral prevalence and infectivity. We set out to test whether specific bacterial communities predict SARS-CoV-2 occurrence in a hospital setting. METHODS: We collected 972 samples from hospitalized patients with COVID-19, their health care providers, and hospital surfaces before, during, and after admission. We screened for SARS-CoV-2 using RT-qPCR, characterized microbial communities using 16S rRNA gene amplicon sequencing, and used these bacterial profiles to classify SARS-CoV-2 RNA detection with a random forest model. RESULTS: Sixteen percent of surfaces from COVID-19 patient rooms had detectable SARS-CoV-2 RNA, although infectivity was not assessed. The highest prevalence was in floor samples next to patient beds (39%) and directly outside their rooms (29%). Although bed rail samples more closely resembled the patient microbiome compared to floor samples, SARS-CoV-2 RNA was detected less often in bed rail samples (11%). SARS-CoV-2 positive samples had higher bacterial phylogenetic diversity in both human and surface samples and higher biomass in floor samples. 16S microbial community profiles enabled high classifier accuracy for SARS-CoV-2 status in not only nares, but also forehead, stool, and floor samples. Across these distinct microbial profiles, a single amplicon sequence variant from the genus Rothia strongly predicted SARS-CoV-2 presence across sample types, with greater prevalence in positive surface and human samples, even when compared to samples from patients in other intensive care units prior to the COVID-19 pandemic. CONCLUSIONS: These results contextualize the vast diversity of microbial niches where SARS-CoV-2 RNA is detected and identify specific bacterial taxa that associate with the viral RNA prevalence both in the host and hospital environment. Video Abstract.

Screening for Sexually Transmitted Infections During Hepatitis C Treatment to Predict Reinfection Among People With HIV.

BACKGROUND: Little is known about the risk of hepatitis C virus (HCV) reinfection among people with HIV (PWH) in the direct-acting antiviral (DAA) era. We evaluate HCV reinfection rates in the DAA era and characterize presustained virologic response (SVR) behavioral risk factors associated with reinfection among PWH at the University of California, San Diego (UCSD). METHODS: Observational longitudinal cohort of PWH treated with DAAs between 2014 and July 2019 who achieved SVR and had at least 1 subsequent HCV viral load measurement. HCV reinfection was defined as new HCV viremia after SVR. We examined whether screening for sexually transmitted infections (STIs) and substance use during the pre-SVR period could identify patients at greater risk for reinfection using exact Poisson regression to compare reinfection incidence rates between those with and without pre-SVR STIs and positive urine drug screens. RESULTS: Eight out of 200 PWH were reinfected with HCV a median ~26 weeks after SVR over 328.1 person-years of follow-up (PYFU), for an incidence rate of 2.44/100 PYFU. The observed HCV reinfection rate was highest among men who have sex with men who inject drugs (MSM IDU; 4.63/100 PFYU) and those aged 30-39 years (6.80/100 PYFU). Having a positive gonorrhea/chlamydia test during the pre-SVR period was a predictor of HCV reinfection. CONCLUSIONS: The HCV reinfection rate in the DAA era is similar to the rate observed in the interferon era in San Diego in PWH. STI screening during HCV treatment may help determine those at higher risk for HCV reinfection.

Hepatitis C Knowledge and Recent Diagnosis Affect Hepatitis C Treatment Willingness in Persons Living With HIV.

BACKGROUND: We assessed the impact of health literacy and hepatitis C (HCV) knowledge on HCV treatment willingness among people living with HIV (PLWH) at an academic HIV clinic. METHODS: Cross-sectional analysis of PLWH coinfected with HCV who completed health literacy, HIV literacy, and HCV knowledge inventories. We estimated the prevalence of low health literacy, HIV knowledge, and HCV knowledge sampled from 3-comparison groups: PLWH not referred for HCV, referred but who "not showed" to the HCV clinic, and referred and attended the HCV clinic. We used mixed-model linear and logistic regression to ascertain predictors of low health literacy, HIV knowledge, HCV knowledge, and predictors of willingness to start HCV treatment. RESULTS: We enrolled 151 PLWH; 17% were female, 38% non-White, and 60% without a high-school education. Approximately, 68% were men who have sex with men, of whom 62% used intravenous drugs. The prevalence of low health, HIV knowledge, and HCV knowledge was 10%, 32%, and 29%, respectively. Predictors of low health literacy were being Hispanic, cirrhotic, and not completing high-school education. Low HCV knowledge was observed in female, non-White, and those diagnosed with HCV for a decade. In adjusted analyses, PLWH living with HCV for a decade (OR: 0.23) were less likely to be very willing to be treated for HCV. By contrast, those with high HCV knowledge were more likely to be very willing to receive treatment (OR: 1.27). CONCLUSION: Low HCV knowledge and living with HCV for at least a decade are under-recognized negative predictors for PLWH's willingness to receive HCV treatment. CLINICAL TRIALS REGISTRATION: ClinicaTrials.gov identifier: NCT20170991.

The Impact of an Inpatient Nurse-Triggered Sepsis Alert on Antimicrobial Utilization.

INTRODUCTION: A nurse-triggered sepsis alert called "Code Sepsis" was implemented for early recognition and management of sepsis. The researchers analyzed its impact on antimicrobial use and identified factors associated with infection as source of Code Sepsis. METHODS: The medical records of hospitalized patients with Code Sepsis between January 1 and June 30, 2018, were reviewed. Patients were classified as "Infection" when probable or definitive infection was identified or "No Infection" when a probable or definitive noninfectious source was identified. Patients were categorized as "Escalation" with addition or change to broader-spectrum antimicrobials or "No Escalation" with no change or change to narrower-spectrum antimicrobials. Escalation was classified as "Indicated" with appropriate escalation or "Not Indicated" with inappropriate escalation. Logistic regression model was used to identify factors associated with Infection as Code Sepsis trigger. RESULTS: Code Sepsis was activated in 529 patients, with Escalation in 246 (46.5%) and No Escalation in 283 (53.5%) patients. Escalation was Indicated in 157 (63.8%) and Not Indicated in 89 (36.2%) patients. Infection was identified in 356 (67.3%) and No Infection in 173 (32.7%) patients. History of HIV (odds ratio [OR] = 2.75, p = 0.03), temperature > 38.3°C or < 36°C (OR = 2.63, p < 0.01), and respiratory rate > 20/minute (OR = 1.56, p = 0.02) were associated with Infection, while surgery within 3 days (OR = 0.30, p < 0.01) was associated with No Infection. CONCLUSION: One hospital system's Code Sepsis inadvertently identified patients without infections and led to antimicrobial overuse. By refocusing Code Sepsis on early recognition of severe sepsis and septic shock only, the organization hopes to optimize resource utilization and improve patient outcomes.

Microbial context predicts SARS-CoV-2 prevalence in patients and the hospital built environment.

Synergistic effects of bacteria on viral stability and transmission are widely documented but remain unclear in the context of SARS-CoV-2. We collected 972 samples from hospitalized ICU patients with coronavirus disease 2019 (COVID-19), their health care providers, and hospital surfaces before, during, and after admission. We screened for SARS-CoV-2 using RT-qPCR, characterized microbial communities using 16S rRNA gene amplicon sequencing, and contextualized the massive microbial diversity in this dataset in a meta-analysis of over 20,000 samples. Sixteen percent of surfaces from COVID-19 patient rooms were positive, with the highest prevalence in floor samples next to patient beds (39%) and directly outside their rooms (29%). Although bed rail samples increasingly resembled the patient microbiome throughout their stay, SARS-CoV-2 was less frequently detected there (11%). Despite surface contamination in almost all patient rooms, no health care workers providing COVID-19 patient care contracted the disease. SARS-CoV-2 positive samples had higher bacterial phylogenetic diversity across human and surface samples, and higher biomass in floor samples. 16S microbial community profiles allowed for high classifier accuracy for SARS-CoV-2 status in not only nares, but also forehead, stool and floor samples. Across these distinct microbial profiles, a single amplicon sequence variant from the genus Rothia was highly predictive of SARS-CoV-2 across sample types, and had higher prevalence in positive surface and human samples, even when comparing to samples from patients in another intensive care unit prior to the COVID-19 pandemic. These results suggest that bacterial communities contribute to viral prevalence both in the host and hospital environment.

The role of barriers to care on the propensity for hepatitis C virus nonreferral among people living with HIV.

: Twenty-five percent of HIV/hepatitis C virus (HCV) coinfected patients were not referred for HCV treatment despite unrestricted access in California to direct-acting antivirals (DAA) in 2018. Having unstable housing and ongoing drug use directly affected HCV treatment nonreferral. However, psychiatric history and alcohol use impacted HCV treatment nonreferral through the mediation of not being engaged in HIV care. Achieving HCV elimination requires DAA treatment outside conventional health settings, including substance rehabilitation centers, mental health crisis houses, and homeless shelters.

Temporal variations in bacterial community diversity and composition throughout intensive care unit renovations.

BACKGROUND: Inanimate surfaces within a hospital serve as a reservoir of microbial life that may colonize patients and ultimately result in healthcare associated infections (HAIs). Critically ill patients in intensive care units (ICUs) are particularly vulnerable to HAIs. Little is known about how the microbiome of the ICU is established or what factors influence its evolution over time. A unique opportunity to bridge the knowledge gap into how the ICU microbiome evolves emerged in our health system, where we were able to characterize microbial communities in an established hospital ICU prior to closing for renovations, during renovations, and then after re-opening. RESULTS: We collected swab specimens from ICU bedrails, computer keyboards, and sinks longitudinally at each renovation stage, and analyzed the bacterial compositions on these surfaces by 16S rRNA gene sequencing. Specimens collected before ICU closure had the greatest alpha diversity, while specimens collected after the ICU had been closed for over 300 days had the least. We sampled the ICU during the 45 days after re-opening; however, within that time frame, the alpha diversity never reached pre-closure levels. There were clear and significant differences in microbiota compositions at each renovation stage, which was driven by environmental bacteria after closure and human-associated bacteria after re-opening and before closure. CONCLUSIONS: Overall, we identified significant differences in microbiota diversity and community composition at each renovation stage. These data help to decipher the evolution of the microbiome in the most critical part of the hospital and demonstrate the significant impacts that microbiota from patients and staff have on the evolution of ICU surfaces. Video Abstract.

Public Health Role of Academic Medical Center in Community Outbreak of Hepatitis A, San Diego County, California, USA, 2016-2018.

During 2016-2018, San Diego County, California, USA, experienced one of the largest hepatitis A outbreaks in the United States in 2 decades. In close partnership with local healthcare systems, San Diego County Public Health led a public health response to the outbreak that focused on a 3-pronged strategy to vaccinate, sanitize, and educate. Healthcare systems administered nearly half of the vaccinations delivered in San Diego County. At University of California San Diego Health, the use of informatics tools assisted with the identification of at-risk populations and with vaccine delivery across outpatient and inpatient settings. In addition, acute care facilities helped prevent further disease transmission by delaying the discharge of patients with hepatitis A who were experiencing homelessness. We assessed the public health roles that acute care hospitals can play during a large community outbreak and the critical nature of ongoing collaboration between hospitals and public health systems in controlling such outbreaks.