Combined Use of RT-qPCR and NGS for Identification and Surveillance of SARS-CoV-2 Variants of Concern in Residual Clinical Laboratory Samples in Miami-Dade County, Florida.

Over the course of the COVID-19 pandemic, SARS-CoV-2 variants of concern (VOCs) with increased transmissibility and immune escape capabilities, such as Delta and Omicron, have triggered waves of new COVID-19 infections worldwide, and Omicron subvariants continue to represent a global health concern. Tracking the prevalence and dynamics of VOCs has clinical and epidemiological significance and is essential for modeling the progression and evolution of the COVID-19 pandemic. Next generation sequencing (NGS) is recognized as the gold standard for genomic characterization of SARS-CoV-2 variants, but it is labor and cost intensive and not amenable to rapid lineage identification. Here we describe a two-pronged approach for rapid, cost-effective surveillance of SARS-CoV-2 VOCs by combining reverse-transcriptase quantitative polymerase chain reaction (RT-qPCR) and periodic NGS with the ARTIC sequencing method. Variant surveillance by RT-qPCR included the commercially available TaqPath COVID-19 Combo Kit to track S-gene target failure (SGTF) associated with the spike protein deletion H69-V70, as well as two internally designed and validated RT-qPCR assays targeting two N-terminal-domain (NTD) spike gene deletions, NTD156-7 and NTD25-7. The NTD156-7 RT-qPCR assay facilitated tracking of the Delta variant, while the NTD25-7 RT-qPCR assay was used for tracking Omicron variants, including the BA.2, BA.4, and BA.5 lineages. In silico validation of the NTD156-7 and NTD25-7 primers and probes compared with publicly available SARS-CoV-2 genome databases showed low variability in regions corresponding to oligonucleotide binding sites. Similarly, in vitro validation with NGS-confirmed samples showed excellent correlation. RT-qPCR assays allow for near-real-time monitoring of circulating and emerging variants allowing for ongoing surveillance of variant dynamics in a local population. By performing periodic sequencing of variant surveillance by RT-qPCR methods, we were able to provide ongoing validation of the results obtained by RT-qPCR screening. Rapid SARS-CoV-2 variant identification and surveillance by this combined approach served to inform clinical decisions in a timely manner and permitted better utilization of sequencing resources.

The clinical effectiveness of REGEN-COV in SARS-CoV-2 infection with Omicron versus Delta variants.

BACKGROUND: In vitro studies suggesting that REGEN-COV (casirivimab plus imdevimab monoclonal antibodies) had poor efficacy against Omicron-variant SARS-CoV-2 infection led to amendment of REGEN-COV's Emergency Use Authorization to recommend use only in regions without high Omicron prevalence. REGEN-COV's relative clinical effectiveness for Omicron is unknown. METHODS AND FINDINGS: We conducted a retrospective cohort study of non-hospitalized adults who tested positive for SARS-CoV-2 by polymerase chain reaction at the University of Miami Health System from July 19 -November 21, 2021 (Delta period) and December 6, 2021 -January 7, 2022 (Omicron period). Subjects were stratified be REGEN-COV receipt within 72h of test positivity and by time period of infection. We constructed multivariable logistic regression models to assess the differential association of REGEN-COV receipt with hospitalization within 30 days (primary outcome) and ED presentation; all models included three exposure terms (REGEN-COV receipt, Omicron vs Delta period, interaction of REGEN-COV with time period) and potential confounders (vaccination status, vaccine boosting, cancer diagnosis). Our cohort consisted of 2,083 adults in the Delta period (213 [10.2%] received REGEN-COV) and 4,201 in the Omicron period (156 [3.7%] received REGEN-COV). Hospitalization was less common during the Omicron period than during Delta (0.9% vs 1.7%, p = 0.78) and more common for patients receiving REGEN-COV than not (5.7% vs 0.9%, p<0.001). After adjustment, we found no differential association of REGEN-COV use during Omicron vs Delta with hospitalization within 30d (adjusted odds ratio [95% confidence interval] for the interaction term: 2.31 [0.76-6.92], p = 0.13). Similarly, we found no differential association for hospitalization within 15d (2.45 [0.63-9.59], p = 0.20) or emergency department presentation within 30d (1.43 [0.57-3.51], p = 0.40) or within 15d (1.79 [0.65-4.82], p = 0.30). CONCLUSIONS: Within the limitations of this study's power to detect a difference, we identified no differential effectiveness of REGEN-COV in the context of Omicron vs Delta SARS-CoV-2 infection.

Real world SARS-COV-2 vaccine effectiveness in a Miami academic institution.

BACKGROUND: To assess the effectiveness of messenger RNA vaccines against severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) in preventing emergency department (ED) presentations for acute respiratory illness. BASIC PROCEDURES: We conducted a retrospective study assessing adult presentations (age ≥ 18) to the University of Miami Hospital's ED from January 1st through August 25th, 2021, with a SARS-COV-2 PCR test and acute respiratory infection symptoms. Vaccine effectiveness was calculated using a test-negative design. Both univariable and multivariable (adjusted for age, gender, race, insurance status, imputed body mass index [BMI], vaccine type, week of presentation) regression analyses were conducted for the full cohort and subgroups. MAIN FINDINGS: The cohort consisted of 13,203 ED presentations-3134 (23.7%) fully vaccinated and SARS-COV-2 negative, 108 (0.8%) fully vaccinated and SARS-COV-2 positive, 8817 (66.8%) unvaccinated and SARS-COV-2 negative, and 1144 (8.7%) unvaccinated and SARS-COV-2 positive. Unadjusted vaccination effectiveness was 73.4% (95% confidence interval: 67.5%,78.3%) and, after adjustment, 73.8% (66.2%,79.7%). The Moderna vaccine's effectiveness was numerically higher (unadjusted: 78.2% [68.8%, 84.7%]; adjusted: 78.0% [68.1%, 84.9%]) than the Pfizer vaccine's (unadjusted: 70.8% [62.9%, 76.9%]; adjusted: 73.9% [66.3%,79.8%]). We found a significant difference in adjusted vaccine effectiveness across categories was BMI (p < 0.001)-BMI <25: 66.3% (45.3%,79.2%); BMI 25-29: 71.3% (56.1%, 81.2%); BMI 30-34: 84.5% (71.7%, 91.5%); and BMI ≥35: 72.7% (50.5%, 84.9%). PRINCIPAL CONCLUSIONS: We demonstrated excellent real-world effectiveness of mRNA vaccines in preventing ED presentation for SARS-COV-2 in a diverse U.S. COHORT: Notably, vaccine effectiveness improved with increasing BMI (until class 2 obesity).

Association of care by a non-medical intensive care unit provider team with outcomes of medically critically ill patients.

OBJECTIVE: To determine the association of boarding of critically ill medical patients on non-medical intensive care unit (ICU) provider teams with outcomes. DESIGN: A retrospective cohort study. SETTING: ICUs in a tertiary academic medical center. PATIENTS: Patients with medical critical illness. INTERVENTIONS: None. MEASUREMENT AND MAIN RESULTS: We compared outcomes for critically ill medical patients admitted to a non-medical specialty ICU team (April 1 - August 30, 2020) with those admitted to the medical ICU team (January 1, 2018 - March 31, 2020). The primary outcome was hospital mortality; secondary outcomes were hospital length of stay (LOS) and hospital disposition for survivors. Our cohort consisted of 1241 patients admitted to the medical ICU team and 230 admitted to non-medical ICU teams. Unadjusted hospital mortality (medical ICU, 38.8% vs non-medical ICU, 42.2%, p = 0.33) and hospital LOS (7.4 vs 7.4 days, p = 0.96) were similar between teams. Among survivors, more non-medical ICU team patients were discharged home (72.6% vs 82.0%, p = 0.024). After multivariable adjustment, we found no difference in mortality, LOS, or home discharge between teams. However, among hospital survivors, admission to a non-medical ICU team was associated with a longer LOS (regression coefficient [95% CI] for log-transformed hospital LOS: 0.23 [0.05,0.40], p = 0.022). Certain subgroups-patients aged 50-64 years (odds-ratio [95% CI]: 4.22 [1.84,9.65], p = 0.001), with ≤10 comorbidities (0-5: 2.78 (1.11,6.95], p = 0.029; 6-10: 6.61 [1.38,31.71], p = 0.018), without acute respiratory failure (1.97 [1.20,3.23], p = 0.008)-had higher mortality when admitted to non-medical ICU teams. CONCLUSIONS: We found no association between admission to non-medical ICU team and mortality for medically critically ill patients. However, survivors experienced longer hospital LOS when admitted to non-medical ICU teams. Middle-aged patients, those with low comorbidity burden, and those without respiratory failure had higher mortality when admitted to non-medical ICU teams.

Predictive Value of Sequential Organ Failure Assessment Score across Patients with and without COVID-19 Infection.

Rationale: Sequential organ failure assessment (SOFA) scores are commonly used in crisis standards of care policies to assist in resource allocation. The relative predictive value of SOFA by coronavirus disease (COVID-19) infection status and among racial and ethnic subgroups within patients infected with COVID-19 is unknown. Objectives: To evaluate the accuracy and calibration of SOFA in predicting hospital mortality by COVID-19 infection status and across racial and ethnic subgroups. Methods: We performed a retrospective cohort study of adult admissions to the University of Miami Hospital and Clinics inpatient wards (July 1, 2020-April 1, 2021). We primarily considered maximum SOFA within 48 hours of hospitalization. We assessed accuracy using the area under the receiver operating characteristic curve (AUROC) and created calibration belts. Considered subgroups were defined by COVID-19 infection status (by severe acute respiratory syndrome coronavirus 2 polymerase chain reaction testing) and prevalent racial and ethnic minorities. Comparisons across subgroups were made with DeLong testing for discriminative accuracy and visualization of calibration belts. Results: Our primary cohort consisted of 20,045 hospitalizations, of which 1,894 (9.5%) were COVID-19 positive. SOFA was similarly accurate for COVID-19-positive (AUROC, 0.835) and COVID-19-negative (AUROC, 0.810; P = 0.15) admissions but was slightly better calibrated in patients who were positive for COVID-19. For those with critical illness, maximum SOFA score accuracy at critical illness onset also did not differ by COVID-19 status (AUROC, COVID-19 positive vs. negative: intensive care unit admissions, 0.751 vs. 0.775; P = 0.46; mechanically ventilated, 0.713 vs. 0.792, P = 0.13), and calibration was again better for patients positive for COVID-19. Among patients with COVID-19, SOFA accuracy was similar between the non-Hispanic White population (AUROC, 0.894) and racial and ethnic minorities (Hispanic White population: AUROC, 0.824 [P vs. non-Hispanic White = 0.05]; non-Hispanic Black population: AUROC, 0.800 [P = 0.12]; Hispanic Black population: AUROC, 0.948 [P = 0.31]). This similar accuracy was also found for those without COVID-19 (non-Hispanic White population: AUROC, 0.829; Hispanic White population: AUROC, 0.811 [P = 0.37]; Hispanic Black population: AUROC, 0.828 [P = 0.97]; non-Hispanic Black population: AUROC, 0.867 [P = 0.46]). SOFA was well calibrated for all racial and ethnic groups with COVID-19 but estimated mortality more variably and performed less well across races and ethnicities without COVID-19. Conclusions: SOFA accuracy does not differ by COVID-19 status and is similar among racial and ethnic groups both with and without COVID-19. Calibration is better for COVID-19-infected patients and, among those without COVID-19, varies by race and ethnicity.

Bloodstream Infection Risk, Incidence, and Deaths for Hospitalized Patients during Coronavirus Disease Pandemic.

Hospital-acquired infections are emerging major concurrent conditions during the coronavirus disease (COVID-19) pandemic. We conducted a retrospective review of hospitalizations during March‒October 2020 of adults tested by reverse transcription PCR for severe acute respiratory syndrome coronavirus 2. We evaluated associations of COVID-19 diagnosis with risk for laboratory-confirmed bloodstream infections (LCBIs, primary outcome), time to LCBI, and risk for death by using logistic and competing risks regression with adjustment for relevant covariates. A total of 10,848 patients were included in the analysis: 918 (8.5%) were given a diagnosis of COVID-19, and 232 (2.1%) had LCBIs during their hospitalization. Of these patients, 58 (25%) were classified as having central line‒associated bloodstream infections. After adjusting for covariates, COVID-19‒positive status was associated with higher risk for LCBI and death. Reinforcement of infection control practices should be implemented in COVID-19 wards, and review of superiority and inferiority ranking methods by National Healthcare Safety Network criteria might be needed.

Candida auris Invasive Infections during a COVID-19 Case Surge.

Clinical cases of C. auris noted during a COVID-19 surge led to an epidemiological, clinical, and genomic investigation. Evaluation identified a close genetic relationship but inconclusive epidemiologic link between all cases. Prolonged hospitalization due to critical illness from COVID-19 and use of antimicrobials may have contributed to clinical infections.

Linking prediction models to government ordinances to support hospital operations during the COVID-19 pandemic.

OBJECTIVES: We describe a hospital's implementation of predictive models to optimise emergency response to the COVID-19 pandemic. METHODS: We were tasked to construct and evaluate COVID-19 driven predictive models to identify possible planning and resource utilisation scenarios. We used system dynamics to derive a series of chain susceptible, infected and recovered (SIR) models. We then built a discrete event simulation using the system dynamics output and bootstrapped electronic medical record data to approximate the weekly effect of tuning surgical volume on hospital census. We evaluated performance via a model fit assessment and cross-model comparison. RESULTS: We outlined the design and implementation of predictive models to support management decision making around areas impacted by COVID-19. The fit assessments indicated the models were most useful after 30 days from onset of local cases. We found our subreports were most accurate up to 7 days after model run.DiscusssionOur model allowed us to shape our health system's executive policy response to implement a 'hospital within a hospital'-one for patients with COVID-19 within a hospital able to care for the regular non-COVID-19 population. The surgical scheduleis modified according to models that predict the number of new patients withCovid-19 who require admission. This enabled our hospital to coordinateresources to continue to support the community at large. Challenges includedthe need to frequently adjust or create new models to meet rapidly evolvingrequirements, communication, and adoption, and to coordinate the needs ofmultiple stakeholders. The model we created can be adapted to other health systems,provide a mechanism to predict local peaks in cases and inform hospitalleadership regarding bed allocation, surgical volumes, staffing, and suppliesone for COVID-19 patients within a hospital able to care for the regularnon-COVID-19 population. CONCLUSION: Predictive models are essential tools in supporting decision making when coordinating clinical operations during a pandemic.

Association of Race and Ethnicity with COVID-19 Test Positivity and Hospitalization Is Mediated by Socioeconomic Factors.

Rationale: Black race and Hispanic ethnicity are associated with increased risks for coronavirus disease (COVID-19) infection and severity. It is purported that socioeconomic factors may drive this association, but data supporting this assertion are sparse. Objectives: To evaluate whether socioeconomic factors mediate the association of race/ethnicity with COVID-19 incidence and outcomes. Methods: We conducted a retrospective cohort study of adults tested for (cohort 1) or hospitalized with (cohort 2) COVID-19 between March 1, 2020, and July 23, 2020, at the University of Miami Hospital and Clinics. Our primary exposure was race/ethnicity. We considered socioeconomic factors as potential mediators of our exposure's association with outcomes. We used standard statistics to describe our cohorts and multivariable regression modeling to identify associations of race/ethnicity with our primary outcomes, one for each cohort, of test positivity (cohort 1) and hospital mortality (cohort 2). We performed a mediation analysis to see whether household income, population density, and household size mediated the association of race/ethnicity with outcomes. Results: Our cohorts included 15,473 patients tested (29.0% non-Hispanic White, 48.1% Hispanic White, 15.0% non-Hispanic Black, 1.7% Hispanic Black, and 1.6% other) and 295 patients hospitalized (9.2% non-Hispanic White, 56.9% Hispanic White, 21.4% non-Hispanic Black, 2.4% Hispanic Black, and 10.2% other). Among those tested, 1,256 patients (8.1%) tested positive, and, of the hospitalized patients, 47 (15.9%) died. After adjustment for demographics, race/ethnicity was associated with test positivity-odds-ratio (95% confidence interval [CI]) versus non-Hispanic White for Non-Hispanic Black: 3.21 (2.60-3.96), Hispanic White: 2.72 (2.28-3.26), and Hispanic Black: 3.55 (2.33-5.28). Population density mediated this association (percentage mediated, 17%; 95% CI, 11-31%), as did median income (27%; 95% CI, 18-52%) and household size (20%; 95% CI, 12-45%). There was no association between race/ethnicity and mortality, although this analysis was underpowered. Conclusions: Black race and Hispanic ethnicity are associated with an increased odds of COVID-19 positivity. This association is substantially mediated by socioeconomic factors.