Data from phase 3 clinical trials of messenger RNA (mRNA) vaccines through November 2020 showed 94.1% efficacy for the prevention of symptomatic severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection at 14 days after the second dose of the mRNA-1273 vaccine (Moderna)¹ and 95% efficacy at 7 days after the second dose of the BNT162b2 vaccine (Pfizer).² Since the results of these trials were published, a nationwide surge in coronavirus disease 2019 (Covid-19) has been noted, SARS-CoV-2 variants with increased infectivity have emerged, the Food and Drug Administration has granted emergency use authorization for these two mRNA vaccines, and vaccination has been initiated across the United States.

In December 2020, the University of California San Diego Health (UCSDH) workforce experienced a dramatic increase in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections. Vaccination with mRNA vaccines began in mid-December 2020; by March, 76% of the workforce had been fully vaccinated, and by July, the percentage had risen to 87%. Infections had decreased dramatically by early February 2021.¹ Between March and June, fewer than 30 health care workers tested positive each month. However, coincident with the end of California’s mask mandate on June 15 and the rapid dominance of the B.1.617.2 (delta) variant that first emerged in mid-April and accounted for over 95% of UCSDH isolates by the end of July ( Figure 1 ), infections increased rapidly, including cases among fully vaccinated persons. Institutional review board approval was obtained for use of administrative data on vaccinations and case-investigation data to examine mRNA SARS CoV-2 vaccine effectiveness.

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.

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. 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. 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). IP strategies were effective at identifying and preventing healthcare SARS-CoV-2 transmission. This graphical abstract is also available at Tidbit: https://tidbitapp.io/tidbits/integrated-genomic-and-social-network-analyses-of-sars-cov-2-transmission-in-the-healthcare-setting-1d3824e5-4e59-4391-a778-4f9205ecca0a

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.

Background: The Karius Test (KT), a microbial cell-free DNA next-generation sequencing assay, is increasingly utilized in challenging infectious syndromes. However, its real-world clinical utility and cost-effectiveness remain uncertain. Methods: We conducted a retrospective review of 88 KT results from adult patients at UC San Diego Health between July 2017 and April 2024. Each case was evaluated for clinical impact using standardized criteria. We analyzed diagnostic yield, turnaround time, number needed to test (NNT), and institutional billing data for reimbursement and cost implications. Results: Of 88 unique tests, forty-nine (55.7%) identified at least one pathogen. Eleven (12.5%) had a positive clinical impact—eight provided the only microbiologic diagnosis, and three were faster than conventional methods. Vascular/graft infections showed the highest yield. Twenty-one tests had a neutral impact; fifty-six showed no clinical benefit. The Median turnaround time was 3 days. The NNT was 6.1 or 2.75 including neutral cases. Cost analysis revealed a substantial financial burden without transparent reimbursement mechanisms in inpatient settings. Conclusion: The KT demonstrates modest clinical utility with noteworthy benefits in select scenarios. Given its high cost and variable impact, we advocate for diagnostic stewardship led by infectious disease specialists to optimize test use and minimize unnecessary expense.