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.

Common antibiotics, azithromycin and amoxicillin, affect gut metagenomics within a household.

BACKGROUND: The microbiome of the human gut serves a role in a number of physiological processes, but can be altered through effects of age, diet, and disturbances such as antibiotics. Several studies have demonstrated that commonly used antibiotics can have sustained impacts on the diversity and the composition of the gut microbiome. The impact of the two most overused antibiotics, azithromycin, and amoxicillin, in the human microbiome has not been thoroughly described. In this study, we recruited a group of individuals and unrelated controls to decipher the effects of the commonly used antibiotics amoxicillin and azithromycin on their gut microbiomes. RESULTS: We characterized the gut microbiomes by metagenomic sequencing followed by characterization of the resulting microbial communities. We found that there were clear and sustained effects of the antibiotics on the gut microbial community with significant alterations in the representations of Bifidobacterium species in response to azithromycin (macrolide antibiotic). These results were supported by significant increases identified in putative antibiotic resistance genes associated with macrolide resistance. Importantly, we did not identify these trends in the unrelated control individuals. There were no significant changes observed in other members of the microbial community. CONCLUSIONS: As we continue to focus on the role that the gut microbiome plays and how disturbances induced by antibiotics might affect our overall health, elucidating members of the community most affected by their use is of critical importance to understanding the impacts of common antibiotics on those who take them. Clinical Trial Registration Number NCT05169255. This trial was retrospectively registered on 23-12-2021.

Real world effectiveness of tixagevimab/cilgavimab (Evusheld) in the Omicron era.

BACKGROUND: Pre-exposure prophylaxis for COVID-19 with tixagevimab/cilgavimab (T/C) received Emergency Use Authorization (EUA) based on results of a clinical trial conducted prior to the emergence of the Omicron variant. The clinical effectiveness of T/C has not been well described in the Omicron era. We examined the incidence of symptomatic illness and hospitalizations among T/C recipients when Omicron accounted for virtually all local cases. METHODS: Through retrospective electronic medical record chart review, we identified patients who received T/C between January 1 -July 31, 2022 within our quaternary referral health system. We determined the incidence of symptomatic COVID-19 infections and hospitalizations due to or presumed to be caused by early Omicron variants before and after receiving T/C (pre-T/C and post-T/C). Chi square and Mann-Whitney Wilcoxon two-sample tests were used to examine differences between the characteristics of those who got COVID-19 before or after T/C prophylaxis, and rate ratios (RR) and 95% confidence intervals (CI) were calculated to assess differences in hospitalization rates for the two groups. RESULTS: Of 1295 T/C recipients, 105 (8.1%) developed symptomatic COVID-19 infection before receiving T/C, and 102 (7.9%) developed symptomatic disease after receiving it. Of the 105 patients who developed symptomatic infection pre-T/C, 26 (24.8%) were hospitalized, compared with six of the 102 patients (5.9%) who were diagnosed with COVID-19 post-T/C (RR = 0.24; 95% CI = 0.10-0.55; p = 0.0002). Seven of the 105 (6.7%) patients infected pre-T/C, but none of the 102 infected post-T/C required ICU care. No COVID-related deaths occurred in either group. The majority of COVID-19 cases among those infected pre-T/C treatment occurred during the Omicron BA.1 surge, while the majority of post-T/C cases occurred when Omicron BA.5 was predominant. In both groups, having at least one dose of vaccine strongly protected against hospitalization (pre-T/C group RR = 0.31, 95% CI = 0.17-0.57, p = 0.02; post-T/C group RR = 0.15; 95% CI = 0.03-0.94; p = 0.04). CONCLUSION: We identified COVID-19 infections after T/C prophylaxis. Among patients who received T/C at our institution, COVID-19 Omicron cases occurring after T/C were one-fourth as likely to require hospitalization compared to those with Omicron prior to T/C. However, due to the presence of changing vaccine coverage, multiple therapies, and changing variants, the effectiveness of T/C in the Omicron era remains difficult to assess.

Secondary peritonitis in a patient with cirrhosis involving Hyphopichia burtonii, an emerging fungal pathogen.

We present a case where Hyphopichia burtonii, a yeast, speciated from peritoneal fluid in a cirrhotic patient with secondary peritonitis. The patient, a man in his 60s with decompensated cirrhosis, was admitted for an upper gastrointestinal (GI) bleed. On admission, he was treated empirically for spontaneous bacterial peritonitis (SBP) but failed to improve with antibiotics. Serial paracenteses revealed polymicrobial peritonitis and rising peritoneal polymorphonuclear leukocytes (PMNs). These findings raised concerns for secondary peritonitis, prompting an abdominal computed tomography (CT) scan which revealed ischemic bowel. Among the peritoneal microbiota isolated, Hyphopichia burtonii predominated. Hyphopichia burtonii has only recently been reported as a human pathogen, previously it had only reported as a pathogen in bats[1,2].

Surgical wound classification in otolaryngology: A state-of-the-art review.

Objective: To describe the issues related to the assignment of surgical wound classification as it pertains to Otolaryngology-Head & Neck surgery, and to present a simple framework by which providers can assign wound classification. Data Sources: Literature review. Conclusion: Surgical wound classification in its current state is limited in its utility. It has recently been disregarded by major risk assessment models, likely due to inaccurate and inconsistent reporting by providers and operative staff. However, if data accuracy is improved, this metric may be useful to inform the risk of surgical site infection. In an era of quality-driven care and reimbursement, surgical wound classification may become an equally important indicator of quality.

Examination of Medication Use Patterns by Age Group, Comorbidity, and Month in COVID-19 Positive Patients in a Large Statewide Health System During the Pandemic in 2020.

Background: Understanding medication use patterns for patients with COVID-19 will provide needed insight into the evolution of COVID-19 treatment over the course of the SARS-CoV-2 pandemic and aid clinical management considerations. Objectives: To systematically determine most frequently used medications among COVID-19 patients overall and by hospitalization status. Secondary objective was use measurement of medications considered potential therapeutic options. Methods: Retrospective cohort study was performed using data from the University of California COVID Research Data Set (UC CORDS) patients between March 10, 2020, and December 31, 2020. Main outcomes were percentages of patients prescribed medications, overall, by age group, and by comorbidity based on hospitalization status for COVID-19 patients. Use percentage by month of COVID-19 diagnosis was measured. Cumulative count of potential therapeutic options was measured over time. Results: Dataset included 22 896 unique patients with COVID-19 (mean [SD] age, 42.4 [20.4] years; 12 154 [53%] women). Most frequently used medications in patients overall were acetaminophen (21.2%), albuterol (14.9%), ondansetron (13.9%), and enoxaparin (10.8%). Dexamethasone use increased from fewer than 50 total hospitalized patients through April who had received the medication, to more than 500 patients by mid-August. Cumulative count of enoxaparin users was the largest throughout the study period. Conclusion and Relevance: In this retrospective cohort study, across age and comorbidity groups, predominant utilization was for supportive care therapy. Dexamethasone and remdesivir experienced large increases in use. Conversely, hydroxychloroquine and azithromycin use markedly dropped. Medication utilization rapidly shifted toward more evidence-concordant treatment of patients with COVID-19 as rigorous study findings emerged.

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.

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.

The Effect of Clostridioides difficile Diagnostic Stewardship Interventions on the Diagnosis of Hospital-Onset Clostridioides difficile Infections.

BACKGROUND: Public reporting of Clostridioides difficile infection (CDI) using laboratory-identified events has led some institutions to revert from molecular-based tests to less sensitive testing modalities. At one academic medical center, researchers chose to use nucleic acid amplification test alone in CDI diagnosis with institutional protocols aimed at diagnostic stewardship. METHODS: A single-center, quasi-experimental study was conducted to introduce and analyze the effects of various diagnostic stewardship interventions. In April 2017 an order report was created to inform providers of patients' recent bowel movements, laxative use, and prior Clostridioides difficile (CD) testing (Intervention 1). In November 2017 nursing staff were empowered to not send nondiarrheal stools for testing (Intervention 2). In February 2019, an interruptive alert was implemented to prevent testing that was not indicated (Intervention 3). CD testing rates and healthcare facility-onset CDI (HO-CDI) rates were compared before and after the interventions using one-way analysis of variance (ANOVA). RESULTS: At baseline, testing for CD after 3 days of admission was performed at mean ± standard deviation of 15.9 ± 1.7 tests/1,000 patient-days. After Intervention 1, it decreased to 12.1 ± 1.1 tests. This further decreased to 10.6 ± 0.8 after Intervention 2 and to 8.1 ± 0.1 after Intervention 3 (p < 0.001). HO-CDI cases per 10,000 patient-days declined from 12.7 ± 1.4 cases at baseline to 10.7 ± 1.2 after Intervention 1, to 8.7 ± 2.4 after Intervention 2, and to 5.8 ± 0.2 after Intervention 3 (p = 0.03). CONCLUSION: A multidisciplinary approach optimizing electronic health record support tools and leveraging nursing education can reduce both testing and HO-CDI rates while using the most sensitive testing modality.

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.