A quality-improvement approach to urgent-care antibiotic stewardship for respiratory tract infections during the COVID-19 pandemic: Lessons learned.

OBJECTIVE: We investigated a decrease in antibiotic prescribing for respiratory illnesses in 2 academic urgent-care clinics during the coronavirus disease 2019 (COVID-19) pandemic using semistructured clinician interviews. METHODS: We conducted a quality-improvement project from November 2020 to May 2021. We investigated provider antibiotic decision making using a mixed-methods explanatory design including interviews. We analyzed transcripts using a thematic framework approach to identify emergent themes. Our performance measure was antibiotic prescribing rate (APR) for encounters with respiratory diagnosis billing codes. We extracted billing and prescribing data from the electronic medical record and assessed differences using run charts, p charts and generalized linear regression. RESULTS: We observed significant reductions in the APR early during the COVID-19 pandemic (relative risk [RR], 0.20; 95% confidence interval [CI], 0.17-0.25), which was maintained over the study period (P < .001). The average APRs were 14% before the COVID-19 pandemic, 4% during the QI project, and 7% after the project. All providers prescribed less antibiotics for respiratory encounters during COVID-19, but only 25% felt their practice had changed. Themes from provider interviews included changing patient expectations and provider approach to respiratory encounters during COVID-19, the impact of increased telemedicine encounters, and the changing epidemiology of non-COVID-19 respiratory infections. CONCLUSIONS: Our findings suggest that the decrease in APR was likely multifactorial. The average APR decreased significantly during the pandemic. Although the APR was slightly higher after the QI project, it did not reach prepandemic levels. Future studies should explore how these factors, including changing patient expectations, can be leveraged to improve urgent-care antibiotic stewardship.

Association between Obesity and Length of COVID-19 Hospitalization: Unexpected Insights from the American Heart Association National COVID-19 Registry.

Background: The mechanism for possible association between obesity and poor clinical outcomes from Coronavirus Disease 2019 (COVID-19) remains unclear. Methods: We analyzed 22,915 adult COVID-19 patients hospitalized from March 2020 to April 2021 to non-intensive care using the American Heart Association National COVID Registry. A multivariable Poisson model adjusted for age, sex, medical history, admission respiratory status, hospitalization characteristics, and laboratory findings was used to calculate length of stay (LOS) as a function of body mass index (BMI). We similarly analyzed 5,327 patients admitted to intensive care for comparison. Results: Relative to normal BMI subjects, overweight, class I obese, and class II obese patients had approximately half-day reductions in LOS (-0.469 days, P<0.01; -0.480 days, P<0.01; -0.578 days, P<0.01, respectively). Conclusion: The model identified a dose-dependent, inverse relationship between BMI category and LOS for COVID-19, which was not seen when the model was applied to critically ill patients.

Incidence and Prevalence of Coronavirus Disease 2019 Within a Healthcare Worker Cohort During the First Year of the Severe Acute Respiratory Syndrome Coronavirus 2 Pandemic.

BACKGROUND: Preventing severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2_ infections in healthcare workers (HCWs) is critical for healthcare delivery. We aimed to estimate and characterize the prevalence and incidence of coronavirus disease 2019 (COVID-19) in a US HCW cohort and to identify risk factors associated with infection. METHODS: We conducted a longitudinal cohort study of HCWs at 3 Bay Area medical centers using serial surveys and SARS-CoV-2 viral and orthogonal serological testing, including measurement of neutralizing antibodies. We estimated baseline prevalence and cumulative incidence of COVID-19. We performed multivariable Cox proportional hazards models to estimate associations of baseline factors with incident infections and evaluated the impact of time-varying exposures on time to COVID-19 using marginal structural models. RESULTS: A total of 2435 HCWs contributed 768 person-years of follow-up time. We identified 21 of 2435 individuals with prevalent infection, resulting in a baseline prevalence of 0.86% (95% confidence interval [CI], .53%-1.32%). We identified 70 of 2414 incident infections (2.9%), yielding a cumulative incidence rate of 9.11 cases per 100 person-years (95% CI, 7.11-11.52). Community contact with a known COVID-19 case was most strongly correlated with increased hazard for infection (hazard ratio, 8.1 [95% CI, 3.8-17.5]). High-risk work-related exposures (ie, breach in protective measures) drove an association between work exposure and infection (hazard ratio, 2.5 [95% CI, 1.3-4.8). More cases were identified in HCWs when community case rates were high. CONCLUSIONS: We observed modest COVID-19 incidence despite consistent exposure at work. Community contact was strongly associated with infections, but contact at work was not unless accompanied by high-risk exposure.

COVIDNearTerm: A simple method to forecast COVID-19 hospitalizations.

Introduction: COVID-19 has caused tremendous death and suffering since it first emerged in 2019. Soon after its emergence, models were developed to help predict the course of various disease metrics, and these models have been relied upon to help guide public health policy. Methods: Here we present a method called COVIDNearTerm to "forecast" hospitalizations in the short term, two to four weeks from the time of prediction. COVIDNearTerm is based on an autoregressive model and utilizes a parametric bootstrap approach to make predictions. It is easy to use as it requires only previous hospitalization data, and there is an open-source R package that implements the algorithm. We evaluated COVIDNearTerm on San Francisco Bay Area hospitalizations and compared it to models from the California COVID Assessment Tool (CalCAT). Results: We found that COVIDNearTerm predictions were more accurate than the CalCAT ensemble predictions for all comparisons and any CalCAT component for a majority of comparisons. For instance, at the county level our 14-day hospitalization median absolute percentage errors ranged from 16 to 36%. For those same comparisons, the CalCAT ensemble errors were between 30 and 59%. Conclusion: COVIDNearTerm is a simple and useful tool for predicting near-term COVID-19 hospitalizations.

Lung Ultrasound Findings in Patients Hospitalized With COVID-19.

OBJECTIVES: Lung ultrasound (LUS) can accurately diagnose several pulmonary diseases, including pneumothorax, effusion, and pneumonia. LUS may be useful in the diagnosis and management of COVID-19. METHODS: This study was conducted at two United States hospitals from 3/21/2020 to 6/01/2020. Our inclusion criteria included hospitalized adults with COVID-19 (based on symptomatology and a confirmatory RT-PCR for SARS-CoV-2) who received a LUS. Providers used a 12-zone LUS scanning protocol. The images were interpreted by the researchers based on a pre-developed consensus document. Patients were stratified by clinical deterioration (defined as either ICU admission, invasive mechanical ventilation, or death within 28 days from the initial symptom onset) and time from symptom onset to their scan. RESULTS: N = 22 patients (N = 36 scans) were included. Eleven (50%) patients experienced clinical deterioration. Among N = 36 scans, only 3 (8%) were classified as normal. The remaining scans demonstrated B-lines (89%), consolidations (56%), pleural thickening (47%), and pleural effusion (11%). Scans from patients with clinical deterioration demonstrated higher percentages of bilateral consolidations (50 versus 15%; P = .033), anterior consolidations (47 versus 11%; P = .047), lateral consolidations (71 versus 29%; P = .030), pleural thickening (69 versus 30%; P = .045), but not B-lines (100 versus 80%; P = .11). Abnormal findings had similar prevalences between scans collected 0-6 days and 14-28 days from symptom onset. DISCUSSION: Certain LUS findings may be common in hospitalized COVID-19 patients, especially for those that experience clinical deterioration. These findings may occur anytime throughout the first 28 days of illness. Future efforts should investigate the predictive utility of these findings on clinical outcomes.

Race-ethnicity and COVID-19 Vaccination Beliefs and Intentions: A Cross-Sectional Study among the General Population in the San Francisco Bay Area.

OBJECTIVE: The study was designed to compare intentions to receive COVID-19 vaccination by race-ethnicity, to identify beliefs that may mediate the association between race-ethnicity and intention to receive the vaccine and to identify the demographic factors and beliefs most strongly predictive of intention to receive a vaccine. DESIGN: Cross-sectional survey conducted from November 2020 to January 2021, nested within a longitudinal cohort study of the prevalence and incidence of SARS-CoV-2 among a general population-based sample of adults in six San Francisco Bay Area counties (called TrackCOVID). Study Cohort: In total, 3161 participants among the 3935 in the TrackCOVID parent cohort responded. RESULTS: Rates of high vaccine willingness were significantly lower among Black (41%), Latinx (55%), Asian (58%), Multi-racial (59%), and Other race (58%) respondents than among White respondents (72%). Black, Latinx, and Asian respondents were significantly more likely than White respondents to endorse lack of trust of government and health agencies as a reason not to get vaccinated. Participants' motivations and concerns about COVID-19 vaccination only partially explained racial-ethnic differences in vaccination willingness. Concerns about a rushed government vaccine approval process and potential bad reactions to the vaccine were the two most important factors predicting vaccination intention. CONCLUSIONS: Vaccine outreach campaigns must ensure that the disproportionate toll of COVID-19 on historically marginalized racial-ethnic communities is not compounded by inequities in vaccination. Efforts must emphasize messages that speak to the motivations and concerns of groups suffering most from health inequities to earn their trust to support informed decision making.

Design of a population-based longitudinal cohort study of SARS-CoV-2 incidence and prevalence among adults in the San Francisco Bay Area.

PURPOSE: We describe the design of a longitudinal cohort study to determine SARS-CoV-2 incidence and prevalence among a population-based sample of adults living in six San Francisco Bay Area counties. METHODS: Using an address-based sample, we stratified households by county and by census-tract risk. Risk strata were determined by using regression models to predict infections by geographic area using census-level sociodemographic and health characteristics. We disproportionately sampled high and medium risk strata, which had smaller population sizes, to improve precision of estimates, and calculated a desired sample size of 3400. Participants were primarily recruited by mail and were followed monthly with PCR testing of nasopharyngeal swabs, testing of venous blood samples for antibodies to SARS-CoV-2 spike and nucleocapsid antigens, and testing of the presence of neutralizing antibodies, with completion of questionnaires about socio-demographics and behavior. Estimates of incidence and prevalence will be weighted by county, risk strata and sociodemographic characteristics of non-responders, and will take into account laboratory test performance. RESULTS: We enrolled 3842 adults from August to December 2020, and completed follow-up March 31, 2021. We reached target sample sizes within most strata. CONCLUSIONS: Our stratified random sampling design will allow us to recruit a robust general population cohort of adults to determine the incidence of SARS-CoV-2 infection. Identifying risk strata was unique to the design and will help ensure precise estimates, and high-performance testing for presence of virus and antibodies will enable accurate ascertainment of infections.

Active surveillance of serious adverse events following transfusion of COVID-19 convalescent plasma.

BACKGROUND: The reported incidence of adverse reactions following Coronavirus disease 2019 (COVID-19) convalescent plasma (CCP) transfusion has generally been lower than expected based on the incidence of transfusion reactions that have been observed in studies of conventional plasma transfusion. This raises the concern for under-reporting of adverse events in studies of CCP that rely on passive surveillance strategies. MATERIALS AND METHODS: Our institution implemented a protocol to actively identify possible adverse reactions to CCP transfusion. In addition, we retrospectively reviewed the charts of inpatients who received CCP at Stanford Hospital between May 13, 2020 and January 31, 2021. We determined the incidence of adverse events following CCP transfusion. RESULTS: A total of 49 patients received CCP. Seven patients (14%) had an increased supplemental oxygen requirement within 4 h of transfusion completion, including one patient who was intubated during the transfusion. An additional 11 patients (total of 18, 37%) had increased oxygen requirements within 24 h of transfusion, including 3 patients who were intubated. Six patients (12%) fulfilled criteria for transfusion-associated circulatory overload (TACO). CONCLUSION: Using an active surveillance strategy, we commonly observed adverse events following the transfusion of CCP to hospitalized patients. It was not possible to definitively determine whether or not these adverse events are related to CCP transfusion. TACO was likely over-diagnosed given overlap with the manifestations of COVID-19. Nevertheless, these results suggest that the potential adverse effects of CCP transfusion may be underestimated by reports from passive surveillance studies.

The COVID-19 Outpatient Pragmatic Platform Study (COPPS): Study design of a multi-center pragmatic platform trial.

More than 3000 clinical trials related to COVID-19 have been registered through clinicaltrials.gov. With so many trials, there is a risk that many will be inconclusive due to being underpowered or due to an inability to recruit patients. At academic medical centers, multiple trials are competing for the same resources; the success of one may come at the expense of another. The COVID-19 Outpatient Pragmatic Protocol Study (COPPS) is a flexible phase 2, multi-site, randomized, blinded trial based at Stanford University designed to overcome these issues by simultaneously evaluating multiple COVID-19 treatments in the outpatient setting in one common platform with shared controls. This approach reduces the overall number of patients required for statistical power, while improving the likelihood that any enrolled patient receives active treatment. The platform study has two main domains designed to evaluate COVID-19 treatments by assessing their ability to reduce viral shedding (Viral Domain), measured with self-collected nasal swabs, or improve clinical outcomes (Clinical Domain), measured through self-reported symptomology data. Data are collected on both domains for all participants enrolled. Participants are followed over a 28-day period. COPPS has the advantage of pragmatism created around its workflow that is also appealing to potential participants because of a lower probability of inactive treatment. At the conclusion of this clinical trial we expect to have identified potentially effective therapeutic strategy/ies for treating COVID-19 in the outpatient setting, which will have a transformative impact on medicine and public health.

Clinical trials in a COVID-19 pandemic: Shared infrastructure for continuous learning in a rapidly changing landscape.

BACKGROUND: Clinical trials, conducted efficiently and with the utmost integrity, are a key component in identifying effective vaccines, therapies, and other interventions urgently needed to solve the COVID-19 crisis. Yet launching and implementing trials with the rigor necessary to produce convincing results is a complicated and time-consuming process. Balancing rigor and efficiency involves relying on designs that employ flexible features to respond to a fast-changing landscape, measuring valid endpoints that result in translational actions and disseminating findings in a timely manner. We describe the challenges involved in creating infrastructure with potential utility for shared learning. METHODS: We have established a shared infrastructure that borrows strength across multiple trials. The infrastructure includes an endpoint registry to aid in selecting appropriate endpoints, a registry to facilitate establishing a Data & Safety Monitoring Board, common data collection instruments, a COVID-19 dedicated design and analysis team, and a pragmatic platform protocol, among other elements. RESULTS: The authors have relied on the shared infrastructure for six clinical trials for which they serve as the Data Coordinating Center and have a design and analysis team comprising 15 members who are dedicated to COVID-19. The authors established a pragmatic platform to simultaneously investigate multiple treatments for the outpatient with adaptive features to add or drop treatment arms. CONCLUSION: The shared infrastructure provides appealing opportunities to evaluate disease in a more robust manner with fewer resources and is especially valued during a pandemic where efficiency in time and resources is crucial. The most important element of the shared infrastructure is the pragmatic platform. While it may be the most challenging of the elements to establish, it may provide the greatest benefit to both patients and researchers.

Interobserver Agreement of Lung Ultrasound Findings of COVID-19.

BACKGROUND: Lung ultrasound (LUS) has received considerable interest in the clinical evaluation of patients with COVID-19. Previously described LUS manifestations for COVID-19 include B-lines, consolidations, and pleural thickening. The interrater reliability (IRR) of these findings for COVID-19 is unknown. METHODS: This study was conducted between March and June 2020. Nine physicians (hospitalists: n = 4; emergency medicine: n = 5) from 3 medical centers independently evaluated n = 20 LUS scans (n = 180 independent observations) collected from patients with COVID-19, diagnosed via RT-PCR. These studies were randomly selected from an image database consisting of COVID-19 patients evaluated in the emergency department with portable ultrasound devices. Physicians were blinded to any patient information or previous LUS interpretation. Kappa values (κ) were used to calculate IRR. RESULTS: There was substantial IRR on the following items: normal LUS scan (κ = 0.79 [95% CI: 0.72-0.87]), presence of B-lines (κ = 0.79 [95% CI: 0.72-0.87]), ≥3 B-lines observed (κ = 0.72 [95% CI: 0.64-0.79]). Moderate IRR was observed for the presence of any consolidation (κ = 0.57 [95% CI: 0.50-0.64]), subpleural consolidation (κ = 0.49 [95% CI: 0.42-0.56]), and presence of effusion (κ = 0.49 [95% CI: 0.41-0.56]). Fair IRR was observed for pleural thickening (κ = 0.23 [95% CI: 0.15-0.30]). DISCUSSION: Many LUS manifestations for COVID-19 appear to have moderate to substantial IRR across providers from multiple specialties utilizing differing portable devices. The most reliable LUS findings with COVID-19 may include the presence/count of B-lines or determining if a scan is normal. Clinical protocols for LUS with COVID-19 may require additional observers for the confirmation of less reliable findings such as consolidations.