Impact of Vaccination, Prior Infection and Therapy on Omicron Infection and Mortality.

BACKGROUND: Understanding the immunity against omicron infection and severe outcomes conferred by Covid-19 vaccination, prior SARS-CoV-2 infection, and monoclonal antibody therapy will inform intervention strategies. METHODS: We considered 295,691 patients who were tested for SARS-CoV-2 at Cleveland Clinic between October 1, 2021 and January 31, 2022. We used logistic regression to investigate the association of vaccination and prior infection with the risk of SARS-CoV-2 infection and used Cox regression to investigate the association of vaccination, prior infection and monoclonal antibody therapy with the risks of intensive care unit (ICU) stay and death. RESULTS: Vaccination and prior infection were less effective against omicron than delta infection but provided strong protection against ICU admission and death. Boosting greatly increased vaccine effectiveness against omicron infection and severe outcomes, though the effectiveness waned rapidly over time. Monoclonal antibody therapy considerably reduced the risks of ICU admission and death. Finally, the relatively low mortality of the omicron variant was due to both the reduced lethality of this variant and the increased population immunity acquired from booster vaccination and previous infection. CONCLUSIONS: Booster vaccination and prior SARS-CoV-2 infection provide strong protection against ICU admission and death from omicron infection. Monoclonal antibody therapy is also beneficial.

COVID-19 Home Monitoring After Diagnosis and Health Care Utilization in an Integrated Health System.

This cohort study examines health care utilization patterns for patients with COVID-19 who were enrolled vs not enrolled in a home monitoring program.

Protective heterologous T cell immunity in COVID-19 induced by the trivalent MMR and Tdap vaccine antigens.

BACKGROUND: T cells control viral infection, promote vaccine durability, and in coronavirus disease 2019 (COVID-19) associate with mild disease. We investigated whether prior measles-mumps-rubella (MMR) or tetanus-diphtheria-pertussis (Tdap) vaccination elicits cross-reactive T cells that mitigate COVID-19. METHODS: Antigen-presenting cells (APC) loaded ex vivo with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), MMR, or Tdap antigens and autologous T cells from COVID-19-convalescent participants, uninfected individuals, and COVID-19 mRNA-vaccinated donors were co-cultured. T cell activation and phenotype were detected by interferon-γ (IFN-γ) enzyme-linked immunospot (ELISpot) assays and flow cytometry. ELISAs (enzyme-linked immunosorbant assays) and validation studies identified the APC-derived cytokine(s) driving T cell activation. TCR clonotyping and single-cell RNA sequencing (scRNA-seq) identified cross-reactive T cells and their transcriptional profile. A propensity-weighted analysis of COVID-19 patients estimated the effects of MMR and Tdap vaccination on COVID-19 outcomes. FINDINGS: High correlation was observed between T cell responses to SARS-CoV-2 (spike-S1 and nucleocapsid) and MMR and Tdap proteins in COVID-19-convalescent and -vaccinated individuals. The overlapping T cell population contained an effector memory T cell subset (effector memory re-expressing CD45RA on T cells [TEMRA]) implicated in protective, anti-viral immunity, and their detection required APC-derived IL-15, known to sensitize T cells to activation. Cross-reactive TCR repertoires detected in antigen-experienced T cells recognizing SARS-CoV-2, MMR, and Tdap epitopes had TEMRA features. Indices of disease severity were reduced in MMR- or Tdap-vaccinated individuals by 32%-38% and 20%-23%, respectively, among COVID-19 patients. CONCLUSIONS: Tdap and MMR memory T cells reactivated by SARS-CoV-2 may provide protection against severe COVID-19. FUNDING: This study was supported by a National Institutes of Health (R01HL065095, R01AI152522, R01NS097719) donation from Barbara and Amos Hostetter and the Chleck Foundation.

Phenotypes and Subphenotypes of Patients With COVID-19: A Latent Class Modeling Analysis.

BACKGROUND: Since COVID-19 was identified, its clinical and biological heterogeneity has been recognized. Identifying COVID-19 phenotypes might help guide basic, clinical, and translational research efforts. RESEARCH QUESTION: Does the clinical spectrum of patients with COVID-19 contain distinct phenotypes and subphenotypes? STUDY DESIGN AND METHODS: We included adult patients (≥ 18 years) positive for laboratory-confirmed SARS-CoV-2 infection from a prospective COVID-19 registry database in the Cleveland Clinic Health System in Ohio and Florida. The patients were split into training and testing sets. Using latent class analysis (LCA), we first identified phenotypic clusters of patients with COVID-19 based on demographics, comorbidities, and presenting symptoms. We then identified subphenotypes of hospitalized patients with additional blood biomarker data measured on hospital admission. The associations of phenotypes/subphenotypes and clinical outcomes were investigated. Multivariable prediction models were established to predict assignment to the LCA-defined phenotypes and subphenotypes and then evaluated on an independent testing set. RESULTS: We analyzed data for 20,572 patients. Seven phenotypes were identified on the basis of different profiles of presenting COVID-19 symptoms and existing comorbidities, including the following groups: young, no symptoms; young, symptoms; middle-aged, no symptoms; middle-aged, symptoms; middle-aged, comorbidities; old, no symptoms; and old, symptoms. The rates of inpatient hospitalization for the phenotypes were significantly different (P < .001). Five subphenotypes were identified for the subgroup of hospitalized patients, including the following subgroups: young, elevated WBC and platelet counts; middle-aged, lymphopenic with elevated C-reactive protein; middle-aged, hyperinflammatory; old, leukopenic with comorbidities; and old, hyperinflammatory with kidney dysfunction. The hospital mortality and the times from hospitalization to ICU transfer or death were significantly different (P < .001). The models for predicting the LCA-defined phenotypes and subphenotypes showed high discrimination (concordance index, 0.92 and 0.91). INTERPRETATION: Hypothesis-free LCA-defined phenotypes and subphenotypes of patients with COVID-19 can be identified. These may help clinical investigators conduct stratified analyses in clinical trials and assist basic science researchers in characterizing the pathobiology of the spectrum of COVID-19 presentations.

An Algorithm for Classifying Patients Most Likely to Develop Severe Coronavirus Disease 2019 Illness.

OBJECTIVES: To develop an algorithm that predicts an individualized risk of severe coronavirus disease 2019 illness (i.e., ICU admission or death) upon testing positive for coronavirus disease 2019. DESIGN: A retrospective cohort study. SETTING: Cleveland Clinic Health System. PATIENTS: Those hospitalized with coronavirus disease 2019 between March 8, 2020, and July 13, 2020. INTERVENTIONS: A temporal coronavirus disease 2019 test positive cut point of June 1 was used to separate the development from validation cohorts. Fine and Gray competing risk regression modeling was performed. MEASUREMENTS AND MAIN RESULTS: The development set contained 4,520 patients who tested positive for coronavirus disease 2019 between March 8, 2020, and May 31, 2020. The validation set contained 3,150 patients who tested positive between June 1 and July 13. Approximately 9% of patients were admitted to the ICU or died of coronavirus disease 2019 within 2 weeks of testing positive. A prediction cut point of 15% was proposed. Those who exceed the cutoff have a 21% chance of future severe coronavirus disease 2019, whereas those who do not have a 96% chance of avoiding the severe coronavirus disease 2019. In addition, application of this decision rule identifies 89% of the population at the very low risk of severe coronavirus disease 2019 (< 4%). CONCLUSIONS: We have developed and internally validated an algorithm to assess whether someone is at high risk of admission to the ICU or dying from coronavirus disease 2019, should he or she test positive for coronavirus disease 2019. This risk should be a factor in determining resource allocation, protection from less safe working conditions, and prioritization for vaccination.

Individualizing Risk Prediction for Positive Coronavirus Disease 2019 Testing: Results From 11,672 Patients.

BACKGROUND: Coronavirus disease 2019 (COVID-19) is sweeping the globe. Despite multiple case-series, actionable knowledge to tailor decision-making proactively is missing. RESEARCH QUESTION: Can a statistical model accurately predict infection with COVID-19? STUDY DESIGN AND METHODS: We developed a prospective registry of all patients tested for COVID-19 in Cleveland Clinic to create individualized risk prediction models. We focus here on the likelihood of a positive nasal or oropharyngeal COVID-19 test. A least absolute shrinkage and selection operator logistic regression algorithm was constructed that removed variables that were not contributing to the model's cross-validated concordance index. After external validation in a temporally and geographically distinct cohort, the statistical prediction model was illustrated as a nomogram and deployed in an online risk calculator. RESULTS: In the development cohort, 11,672 patients fulfilled study criteria, including 818 patients (7.0%) who tested positive for COVID-19; in the validation cohort, 2295 patients fulfilled criteria, including 290 patients who tested positive for COVID-19. Male, African American, older patients, and those with known COVID-19 exposure were at higher risk of being positive for COVID-19. Risk was reduced in those who had pneumococcal polysaccharide or influenza vaccine or who were on melatonin, paroxetine, or carvedilol. Our model had favorable discrimination (c-statistic = 0.863 in the development cohort and 0.840 in the validation cohort) and calibration. We present sensitivity, specificity, negative predictive value, and positive predictive value at different prediction cutoff points. The calculator is freely available at https://riskcalc.org/COVID19. INTERPRETATION: Prediction of a COVID-19 positive test is possible and could help direct health-care resources. We demonstrate relevance of age, race, sex, and socioeconomic characteristics in COVID-19 susceptibility and suggest a potential modifying role of certain common vaccinations and drugs that have been identified in drug-repurposing studies.

Impact of the COVID-19 Pandemic on Healthcare Workers' Risk of Infection and Outcomes in a Large, Integrated Health System.

BACKGROUND: Understanding the impact of the COVID-19 pandemic on healthcare workers (HCW) is crucial. OBJECTIVE: Utilizing a health system COVID-19 research registry, we assessed HCW risk for COVID-19 infection, hospitalization, and intensive care unit (ICU) admission. DESIGN: Retrospective cohort study with overlap propensity score weighting. PARTICIPANTS: Individuals tested for SARS-CoV-2 infection in a large academic healthcare system (N = 72,909) from March 8-June 9, 2020, stratified by HCW and patient-facing status. MAIN MEASURES: SARS-CoV-2 test result, hospitalization, and ICU admission for COVID-19 infection. KEY RESULTS: Of 72,909 individuals tested, 9.0% (551) of 6145 HCW tested positive for SARS-CoV-2 compared to 6.5% (4353) of 66,764 non-HCW. The HCW were younger than the non-HCW (median age 39.7 vs. 57.5, p < 0.001) with more females (proportion of males 21.5 vs. 44.9%, p < 0.001), higher reporting of COVID-19 exposure (72 vs. 17%, p < 0.001), and fewer comorbidities. However, the overlap propensity score weighted proportions were 8.9 vs. 7.7 for HCW vs. non-HCW having a positive test with weighted odds ratio (OR) 1.17, 95% confidence interval (CI) 0.99-1.38. Among those testing positive, weighted proportions for hospitalization were 7.4 vs. 15.9 for HCW vs. non-HCW with OR of 0.42 (CI 0.26-0.66) and for ICU admission: 2.2 vs. 4.5 for HCW vs. non-HCW with OR of 0.48 (CI 0.20-1.04). Those HCW identified as patient facing compared to not had increased odds of a positive SARS-CoV-2 test (OR 1.60, CI 1.08-2.39, proportions 8.6 vs. 5.5), but no statistically significant increase in hospitalization (OR 0.88, CI 0.20-3.66, proportions 10.2 vs. 11.4) and ICU admission (OR 0.34, CI 0.01-3.97, proportions 1.8 vs. 5.2). CONCLUSIONS: In a large healthcare system, HCW had similar odds for testing SARS-CoV-2 positive, but lower odds of hospitalization compared to non-HCW. Patient-facing HCW had higher odds of a positive test. These results are key to understanding HCW risk mitigation during the COVID-19 pandemic.

Development and validation of a model for individualized prediction of hospitalization risk in 4,536 patients with COVID-19.

BACKGROUND: Coronavirus Disease 2019 is a pandemic that is straining healthcare resources, mainly hospital beds. Multiple risk factors of disease progression requiring hospitalization have been identified, but medical decision-making remains complex. OBJECTIVE: To characterize a large cohort of patients hospitalized with COVID-19, their outcomes, develop and validate a statistical model that allows individualized prediction of future hospitalization risk for a patient newly diagnosed with COVID-19. DESIGN: Retrospective cohort study of patients with COVID-19 applying a least absolute shrinkage and selection operator (LASSO) logistic regression algorithm to retain the most predictive features for hospitalization risk, followed by validation in a temporally distinct patient cohort. The final model was displayed as a nomogram and programmed into an online risk calculator. SETTING: One healthcare system in Ohio and Florida. PARTICIPANTS: All patients infected with SARS-CoV-2 between March 8, 2020 and June 5, 2020. Those tested before May 1 were included in the development cohort, while those tested May 1 and later comprised the validation cohort. MEASUREMENTS: Demographic, clinical, social influencers of health, exposure risk, medical co-morbidities, vaccination history, presenting symptoms, medications, and laboratory values were collected on all patients, and considered in our model development. RESULTS: 4,536 patients tested positive for SARS-CoV-2 during the study period. Of those, 958 (21.1%) required hospitalization. By day 3 of hospitalization, 24% of patients were transferred to the intensive care unit, and around half of the remaining patients were discharged home. Ten patients died. Hospitalization risk was increased with older age, black race, male sex, former smoking history, diabetes, hypertension, chronic lung disease, poor socioeconomic status, shortness of breath, diarrhea, and certain medications (NSAIDs, immunosuppressive treatment). Hospitalization risk was reduced with prior flu vaccination. Model discrimination was excellent with an area under the curve of 0.900 (95% confidence interval of 0.886-0.914) in the development cohort, and 0.813 (0.786, 0.839) in the validation cohort. The scaled Brier score was 42.6% (95% CI 37.8%, 47.4%) in the development cohort and 25.6% (19.9%, 31.3%) in the validation cohort. Calibration was very good. The online risk calculator is freely available and found at https://riskcalc.org/COVID19Hospitalization/. LIMITATION: Retrospective cohort design. CONCLUSION: Our study crystallizes published risk factors of COVID-19 progression, but also provides new data on the role of social influencers of health, race, and influenza vaccination. In a context of a pandemic and limited healthcare resources, individualized outcome prediction through this nomogram or online risk calculator can facilitate complex medical decision-making.