Chronic HIV infection influences the immune response during acute COVID-19 and long COVID

OBJECTIVES/GOALS: Despite highly effective antiretroviral therapy, people living with HIV (PLWH) experience chronic immune activation and inflammation which may influence the progression of infections such as SARS-CoV-2. Here, we explore the immune response and clinical outcomes in HIV(+) and HIV(-) individuals experiencing acute COVID-19 and long COVID (LC). METHODS/STUDY POPULATION: We performed flow cytometric analyses on peripheral blood mononuclear cells from the following: 1) HIV(-) individuals experiencing acute COVID-19, 2) PLWH experiencing acute COVID-19, and 3) pre-COVID-19 pandemic PLWH. Additionally, we will perform similar analyses for the following: 1) PLWH experiencing LC, 2) PLWH previously infected with SARS-CoV-2 who recovered, 3) pre-COVID-19 pandemic PLWH, and 4) HIV(-) individuals experiencing LC. Flow cytometry panels include surface markers for immune cell populations, activation and exhaustion surface markers (with and without SARS-CoV-2-specific antigen stimulation), and intracellular cytokine staining. We will also analyze how chronic HIV infection and other clinical and demographic factors (e.g., age, CD4 %) impact persistent symptomatic burden. RESULTS/ANTICIPATED RESULTS: Acute COVID-19 results–Overall, PLWH had higher baseline expression of activation markers OX40 and CD137 on CD4+ and CD8+ T cells, along with increased levels of TNFa producing CD8+ T cells. Interestingly, PLWH had increased expression of exhaustion markers PD1 and TIGIT but decreased expression of TIM3 on CD4+ and CD8+ T cells. Additionally, PLWH had decreased levels of IL-2 and IFNg producing CD4+ T cells which suggests functional exhaustion. Long COVID-19 expected results–we hypothesize that the activation and inflammation seen in chronic HIV infection will lead to more immune dysregulation and subsequently worsened symptomatic burden. Additionally, we hypothesize that PLWH may have different frequencies of certain LC manifestations, such as increased rates of neurocognitive impairment. DISCUSSION/SIGNIFICANCE: Our findings suggest that chronic HIV infection influences acute immune response during SARS-CoV-2 infection, and that PLWH have variable expression of exhaustion markers which warrants further study. Additionally, our findings in the LC cohort will aid in characterizing clinical manifestations and immunologic mechanisms of LC in PLWH.

Assessing the Impact of COVID-19 on Retention in HIV Primary Care: A Longitudinal Multisite Analysis.

We compared retention in care outcomes between a pre-COVID-19 (Apr19-Mar20) and an early-COVID-19 (Apr20-Mar21) period to determine whether the pandemic had a significant impact on these outcomes and assessed the role of patient sociodemographics in both periods in individuals enrolled in the Data for Care Alabama project (n = 6461). Using scheduled HIV primary care provider visits, we calculated a kept-visit measure and a missed-visit measure and compared them among the pre-COVID-19 and early-COVID-19 periods. We used logistic regression models to calculated odds ratios (OR) and accompanying 95% confidence intervals (CI). Overall, individuals had lowers odds of high visit constancy [OR (95% CI): 0.85 (0.79, 0.92)] and higher odds of no-shows [OR (95% CI): 1.27 (1.19, 1.35)] during the early-COVID-19 period. Compared to white patients, Black patients were more likely to miss an appointment and transgender people versus cisgender women had lower visit constancy in the early-COVID-19 period.

Role of Visit Modality in the HIV-Related No-Shows During the COVID-19 Pandemic: A Multisite Retrospective Cohort Study.

The emergence of the COVID-19 pandemic necessitated rapid expansion of telehealth as part of healthcare delivery. This study compared HIV-related no-shows by visit type (in-person; video; telephone) during the COVID-19 pandemic (April 2020-September 2021) from the Data for Care Alabama project. Using all primary care provider visits, each visit's outcome was categorized as no-show or arrived. A logistic regression model using generalized estimating equations accounting for repeat measures in individuals and within sites calculated odds ratios (OR) and their accompanying 95% confidence interval (CI) for no-shows by visit modality. The multivariable models adjusted for sociodemographic factors. In-person versus telephone visits [OR (95% CI) 1.64 (1.48-1.82)] and in-person versus video visits [OR (95% CI) 1.53 (1.25-1.85)] had higher odds of being a no-show. In-person versus telephone and video no-shows were significantly higher. This may suggest success of telehealth visits as a method for HIV care delivery even beyond COVID-19.

Developing a Clinic-Based, Vaccine-Promoting Intervention for African American Youth in Rural Alabama: Protocol for a Pilot Cluster-Randomized Controlled Implementation Science Trial.

BACKGROUND: African American youth in rural Alabama are clinically underserved and have limited knowledge about the human papillomavirus and the novel coronavirus 2019 (COVID-19) vaccines, including knowledge about the risk for developing cervical or oropharyngeal cancers or COVID-19. OBJECTIVE: In this 30-month study, we propose to develop an in-clinic, youth-tailored, vaccine-promoting intervention for vaccine hesitancy reduction that can be seamlessly integrated into the existing environments of pediatric and family practice settings in rural Alabama. METHODS: This exploratory, sequential mixed methods study will be conducted in 3 phases. In the first phase, we will assess stakeholders' knowledge, sentiments, and beliefs related to vaccination in general, COVID-19 vaccination, and human papillomavirus vaccination. We will also assess stakeholders' perceptions of barriers to vaccination that exist in rural Alabama. This will be followed by a second phase wherein we will use the data collected in the first phase to inform the development and finalization of a noninvasive, modular, synchronous counseling intervention that targets the behaviors of 15- to 26-year-old adolescents. In the third phase, we will conduct a pilot hybrid type 1 effectiveness-implementation cluster-randomized controlled trial to assess intervention acceptability and feasibility (clinics: N=4; African American youth: N=120) while assessing a "clinical signal" of effectiveness. We will document implementation contexts to provide real-world insight and support dissemination and scale-up. RESULTS: The study was funded at the end of December 2020. Approval from the University of Alabama at Birmingham Institutional Review Board was obtained in May 2021, and the qualitative data collection process outlined in the first phase of this project concluded in November 2021. The entire study is expected to be complete at the end of December 2023. CONCLUSIONS: The results of the trial will provide much needed information on vaccine hesitancy in rural Alabama, and if found efficacious, the intervention could notably increase rates of vaccinations in one of the most underserved parts of the United States. The results from the trial will provide information that is valuable to public health practitioners and providers in rural settings to inform their efforts in increasing vaccination rates among 15- to 26-year-old African American youth in rural southern United States. TRIAL REGISTRATION: ClinicalTrials.gov NCT04604743; https://clinicaltrials.gov/ct2/show/NCT04604743. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/33982.

The University of Alabama at Birmingham COVID-19 Collaborative Outcomes Research Enterprise: Developing an institutional learning health system in response to the global pandemic.

Introduction: As a local response to the COVID-19 global pandemic, the University of Alabama at Birmingham (UAB) established the UAB COVID-19 Collaborative Outcomes Research Enterprise (CORE), an institutional learning health system (LHS) to achieve an integrated health services outcomes and research response. Methods: We developed a network of expertise and capabilities to rapidly develop and deploy an institutional-level interdisciplinary LHS. Based upon a scoping review of the literature and the Knowledge to Action Framework, we adopted a LHS framework identifying contributors and components necessary to developing a system within and between the university academic and medical centers. We used social network analysis to examine the emergence of informal work patterns and diversified network capabilities based on the LHS framework. Results: This experience report details three principal characteristics of the UAB COVID-19 CORE LHS development: (a) identifying network contributors and components; (b) building the institutional network; and (c) diversifying network capabilities. Contributors and committees were identified from seven components of LHS: (a) collaborative and executive leadership committee, (b) research coordinating committee, (c) oversight and ethics committee, (d) thematic scientific working groups, (e) programmatic working groups, (f) informatics capabilities, and (g) patient advisory groups. Evolving from the topical interests of the initial CORE participants, scientific working groups emerged to support the learning system network. Programmatic working groups were charged with developing a comprehensive and mutually accessible COVID-19 database. Discussion: Our LHS framework allowed for effective integration of multiple academic and medical centers into a cohesive institutional-level learning system. Network analysis indicated diversity of institutional disciplines, professional rank, and topical focus pertaining to COVID-19, with each center leveraging existing institutional responsibilities to minimize gaps in network capabilities. Conclusion: Incorporating an adapted LHS framework designed for academic medical centers served as a foundational resource supporting further institutional-level efforts to develop agile and responsive learning networks.

Sociodemographic and clinical features predictive of SARS-CoV-2 test positivity across healthcare visit-types.

BACKGROUND: Despite increased testing efforts and the deployment of vaccines, COVID-19 cases and death toll continue to rise at record rates. Health systems routinely collect clinical and non-clinical information in electronic health records (EHR), yet little is known about how the minimal or intermediate spectra of EHR data can be leveraged to characterize patient SARS-CoV-2 pretest probability in support of interventional strategies. METHODS AND FINDINGS: We modeled patient pretest probability for SARS-CoV-2 test positivity and determined which features were contributing to the prediction and relative to patients triaged in inpatient, outpatient, and telehealth/drive-up visit-types. Data from the University of Washington (UW) Medicine Health System, which excluded UW Medicine care providers, included patients predominately residing in the Seattle Puget Sound area, were used to develop a gradient-boosting decision tree (GBDT) model. Patients were included if they had at least one visit prior to initial SARS-CoV-2 RT-PCR testing between January 01, 2020 through August 7, 2020. Model performance assessments used area-under-the-receiver-operating-characteristic (AUROC) and area-under-the-precision-recall (AUPR) curves. Feature performance assessments used SHapley Additive exPlanations (SHAP) values. The generalized pretest probability model using all available features achieved high overall discriminative performance (AUROC, 0.82). Performance among inpatients (AUROC, 0.86) was higher than telehealth/drive-up testing (AUROC, 0.81) or outpatient testing (AUROC, 0.76). The two-week test positivity rate in patient ZIP code was the most informative feature towards test positivity across visit-types. Geographic and sociodemographic factors were more important predictors of SARS-CoV-2 positivity than individual clinical characteristics. CONCLUSIONS: Recent geographic and sociodemographic factors, routinely collected in EHR though not routinely considered in clinical care, are the strongest predictors of initial SARS-CoV-2 test result. These findings were consistent across visit types, informing our understanding of individual SARS-CoV-2 risk factors with implications for deployment of testing, outreach, and population-level prevention efforts.

A Continuously Benchmarked and Crowdsourced Challenge for Rapid Development and Evaluation of Models to Predict COVID-19 Diagnosis and Hospitalization.

Importance: Machine learning could be used to predict the likelihood of diagnosis and severity of illness. Lack of COVID-19 patient data has hindered the data science community in developing models to aid in the response to the pandemic. Objectives: To describe the rapid development and evaluation of clinical algorithms to predict COVID-19 diagnosis and hospitalization using patient data by citizen scientists, provide an unbiased assessment of model performance, and benchmark model performance on subgroups. Design, Setting, and Participants: This diagnostic and prognostic study operated a continuous, crowdsourced challenge using a model-to-data approach to securely enable the use of regularly updated COVID-19 patient data from the University of Washington by participants from May 6 to December 23, 2020. A postchallenge analysis was conducted from December 24, 2020, to April 7, 2021, to assess the generalizability of models on the cumulative data set as well as subgroups stratified by age, sex, race, and time of COVID-19 test. By December 23, 2020, this challenge engaged 482 participants from 90 teams and 7 countries. Main Outcomes and Measures: Machine learning algorithms used patient data and output a score that represented the probability of patients receiving a positive COVID-19 test result or being hospitalized within 21 days after receiving a positive COVID-19 test result. Algorithms were evaluated using area under the receiver operating characteristic curve (AUROC) and area under the precision recall curve (AUPRC) scores. Ensemble models aggregating models from the top challenge teams were developed and evaluated. Results: In the analysis using the cumulative data set, the best performance for COVID-19 diagnosis prediction was an AUROC of 0.776 (95% CI, 0.775-0.777) and an AUPRC of 0.297, and for hospitalization prediction, an AUROC of 0.796 (95% CI, 0.794-0.798) and an AUPRC of 0.188. Analysis on top models submitting to the challenge showed consistently better model performance on the female group than the male group. Among all age groups, the best performance was obtained for the 25- to 49-year age group, and the worst performance was obtained for the group aged 17 years or younger. Conclusions and Relevance: In this diagnostic and prognostic study, models submitted by citizen scientists achieved high performance for the prediction of COVID-19 testing and hospitalization outcomes. Evaluation of challenge models on demographic subgroups and prospective data revealed performance discrepancies, providing insights into the potential bias and limitations in the models.

Duration of post-COVID-19 symptoms is associated with sustained SARS-CoV-2-specific immune responses.

A subset of COVID-19 patients exhibit post-acute sequelae of COVID-19 (PASC), but little is known about the immune signatures associated with these syndromes. We investigated longitudinal peripheral blood samples in 50 individuals with previously confirmed SARS-CoV-2 infection, including 20 who experienced prolonged duration of COVID-19 symptoms (lasting more than 30 days; median = 74 days) compared with 30 who had symptom resolution within 20 days. Individuals with prolonged symptom duration maintained antigen-specific T cell response magnitudes to SARS-CoV-2 spike protein in CD4+ and circulating T follicular helper cell populations during late convalescence, while those without persistent symptoms demonstrated an expected decline. The prolonged group also displayed increased IgG avidity to SARS-CoV-2 spike protein. Significant correlations between symptom duration and both SARS-CoV-2-specific T cells and antibodies were observed. Activation and exhaustion markers were evaluated in multiple immune cell types, revealing few phenotypic differences between prolonged and recovered groups, suggesting that prolonged symptom duration is not due to persistent systemic inflammation. These findings demonstrate that SARS-CoV-2-specific immune responses are maintained in patients suffering from prolonged post-COVID-19 symptom duration in contrast to those with resolved symptoms and may suggest the persistence of viral antigens as an underlying etiology.

Development and Validation of ARC, a Model for Anticipating Acute Respiratory Failure in Coronavirus Disease 2019 Patients.

OBJECTIVES: To evaluate factors predictive of clinical progression among coronavirus disease 2019 patients following admission, and whether continuous, automated assessments of patient status may contribute to optimal monitoring and management. DESIGN: Retrospective cohort for algorithm training, testing, and validation. SETTING: Eight hospitals across two geographically distinct regions. PATIENTS: Two-thousand fifteen hospitalized coronavirus disease 2019-positive patients. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Anticipating Respiratory failure in Coronavirus disease (ARC), a clinically interpretable, continuously monitoring prognostic model of acute respiratory failure in hospitalized coronavirus disease 2019 patients, was developed and validated. An analysis of the most important clinical predictors aligns with key risk factors identified by other investigators but contributes new insights regarding the time at which key factors first begin to exhibit aberrency and distinguishes features predictive of acute respiratory failure in coronavirus disease 2019 versus pneumonia caused by other types of infection. Departing from prior work, ARC was designed to update continuously over time as new observations (vitals and laboratory test results) are recorded in the electronic health record. Validation against data from two geographically distinct health systems showed that the proposed model achieved 75% specificity and 77% sensitivity and predicted acute respiratory failure at a median time of 32 hours prior to onset. Over 80% of true-positive alerts occurred in non-ICU settings. CONCLUSIONS: Patients admitted to non-ICU environments with coronavirus disease 2019 are at ongoing risk of clinical progression to severe disease, yet it is challenging to anticipate which patients will develop acute respiratory failure. A continuously monitoring prognostic model has potential to facilitate anticipatory rather than reactive approaches to escalation of care (e.g., earlier initiation of treatments for severe disease or structured monitoring and therapeutic interventions for high-risk patients).

Occurrence and Timing of Subsequent Severe Acute Respiratory Syndrome Coronavirus 2 Reverse-transcription Polymerase Chain Reaction Positivity Among Initially Negative Patients.

Using data for 20 912 patients from 2 large academic health systems, we analyzed the frequency of severe acute respiratory syndrome coronavirus 2 reverse-transcription polymerase chain reaction test discordance among individuals initially testing negative by nasopharyngeal swab who were retested on clinical grounds within 7 days. The frequency of subsequent positivity within this window was 3.5% and was similar across institutions.

Sustained cellular immune dysregulation in individuals recovering from SARS-CoV-2 infection.

SARS-CoV-2 causes a wide spectrum of clinical manifestations and significant mortality. Studies investigating underlying immune characteristics are needed to understand disease pathogenesis and inform vaccine design. In this study, we examined immune cell subsets in hospitalized and nonhospitalized individuals. In hospitalized patients, many adaptive and innate immune cells were decreased in frequency compared with those of healthy and convalescent individuals, with the exception of an increase in B lymphocytes. Our findings show increased frequencies of T cell activation markers (CD69, OX40, HLA-DR, and CD154) in hospitalized patients, with other T cell activation/exhaustion markers (PD-L1 and TIGIT) remaining elevated in hospitalized and nonhospitalized individuals. B cells had a similar pattern of activation/exhaustion, with increased frequency of CD69 and CD95 during hospitalization followed by an increase in PD1 frequencies in nonhospitalized individuals. Interestingly, many of these changes were found to increase over time in nonhospitalized longitudinal samples, suggesting a prolonged period of immune dysregulation after SARS-CoV-2 infection. Changes in T cell activation/exhaustion in nonhospitalized patients were found to positively correlate with age. Severely infected individuals had increased expression of activation and exhaustion markers. These data suggest a prolonged period of immune dysregulation after SARS-CoV-2 infection, highlighting the need for additional studies investigating immune dysregulation in convalescent individuals.

Incidence of Health Care-Associated COVID-19 During Universal Testing of Medical and Surgical Admissions in a Large US Health System.

Concerns about severe acute respiratory syndrome coronavirus 2 exposure in health care settings may cause patients to delay care. Among 2992 patients testing negative on admission to an academic, 3-hospital system, 8 tested positive during hospitalization or within 14 days postdischarge. Following adjudication of each instance, health care-associated infection incidence ranged from 0.8 to 5.0 cases per 10 000 patient-days.

Impact of COVID-19 response on global surgical volumes: an ongoing observational study.

OBJECTIVE: To determine whether location-linked anaesthesiology calculator mobile application (app) data can serve as a qualitative proxy for global surgical case volumes and therefore monitor the impact of the coronavirus disease 2019 (COVID-19) pandemic. METHODS: We collected data provided by users of the mobile app "Anesthesiologist" during 1 October 2018-30 June 2020. We analysed these using RStudio and generated 7-day moving-average app use plots. We calculated country-level reductions in app use as a percentage of baseline. We obtained data on COVID-19 case counts from the European Centre for Disease Prevention and Control. We plotted changing app use and COVID-19 case counts for several countries and regions. FINDINGS: A total of 100 099 app users within 214 countries and territories provided data. We observed that app use was reduced during holidays, weekends and at night, correlating with expected fluctuations in surgical volume. We observed that the onset of the pandemic prompted substantial reductions in app use. We noted strong cross-correlation between COVID-19 case count and reductions in app use in low- and middle-income countries, but not in high-income countries. Of the 112 countries and territories with non-zero app use during baseline and during the pandemic, we calculated a median reduction in app use to 73.6% of baseline. CONCLUSION: App data provide a proxy for surgical case volumes, and can therefore be used as a real-time monitor of the impact of COVID-19 on surgical capacity. We have created a dashboard for ongoing visualization of these data, allowing policy-makers to direct resources to areas of greatest need.

Initial Clinical Impressions of the Critical Care of COVID-19 Patients in Seattle, New York City, and Chicago.

Since the first recognition of a cluster of novel respiratory viral infections in China in late December 2019, intensivists in the United States have watched with growing concern as infections with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus-now named coronavirus disease of 2019 (COVID-19)-have spread to hospitals in the United States. Because COVID-19 is extremely transmissible and can progress to a severe form of respiratory failure, the potential to overwhelm available critical care resources is high and critical care management of COVID-19 patients has been thrust into the spotlight. COVID-19 arrived in the United States in January and, as anticipated, has dramatically increased the usage of critical care resources. Three of the hardest-hit cities have been Seattle, New York City, and Chicago with a combined total of over 14,000 cases as of March 23, 2020.In this special article, we describe initial clinical impressions of critical care of COVID-19 in these areas, with attention to clinical presentation, laboratory values, organ system effects, treatment strategies, and resource management. We highlight clinical observations that align with or differ from already published reports. These impressions represent only the early empiric experience of the authors and are not intended to serve as recommendations or guidelines for practice, but rather as a starting point for intensivists preparing to address COVID-19 when it arrives in their community.