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1.
International Journal of Infectious Diseases ; 2022.
Article in English | ScienceDirect | ID: covidwho-1804273

ABSTRACT

Background : The emergence of SARS-CoV-2 variants of concern has led to significant phenotypical changes in transmissibility, virulence, and public health measures. Our study used clinical data to compare characteristics between a Delta variant wave and a pre-Delta variant wave of hospitalized patients. Methods : This single-center retrospective study defined a wave as an increasing number of COVID-19 hospitalizations, which peaked and later decreased. Data from the United States Department of Health and Human Services was used to identify the waves’ primary variant. Wave 1 (08/08/20-04/01/21) was characterized by heterogeneous variants, while Wave 2 (06/26/21-10/18/21) was predominantly Delta variant. Descriptive statistics, regression techniques, and machine learning approaches supported the comparisons between waves. Results : From the cohort(n=1318), Wave 2 patients(n=665) were more likely to be younger, have fewer comorbidities, require more ICU care, and show an inflammatory profile with higher C-reactive protein, lactate dehydrogenase, ferritin, fibrinogen, prothrombin time, activated thromboplastin time, and INR compared to Wave 1. The gradient boosting model showed an area under the ROC curve of 0.854(sensitivity 86.4%;specificity 61.5%;positive predictive value 73.8%;negative predictive value 78.3%). Conclusions : Clinical and laboratory characteristics can be used to estimate the COVID-19 variant regardless of genomic testing availability. This finding has implications for variant-driven treatment protocols and further research.

2.
J Intern Med ; 2022 Feb 23.
Article in English | MEDLINE | ID: covidwho-1759213

ABSTRACT

BACKGROUND: While COVID-19 immunization programs attempted to reach targeted rates, cases rose significantly since the emergence of the delta variant. This retrospective cohort study describes the correlation between antispike antibodies and outcomes of hospitalized, breakthrough cases during the delta variant surge. METHODS: All patients with positive SARS-CoV-2 polymerase chain reaction hospitalized at Mayo Clinic Florida from 19 June 2021 to 11 November 2021 were considered for analysis. Cases were analyzed by vaccination status. Breakthrough cases were then analyzed by low and high antibody titers against SARS-CoV-2 spike protein, with a cut-off value of ≥132 U/ml. Outcomes included hospital length of stay (LOS), need for intensive care unit (ICU), mechanical ventilation, and mortality. We used 1:1 nearest neighbor propensity score matching without replacement to assess for confounders. RESULTS: Among 627 hospitalized patients with COVID-19, vaccine breakthrough cases were older with more comorbidities compared to unvaccinated. After propensity score matching, the unvaccinated patients had higher mortality (27 [28.4%] vs. 12 [12.6%], p = 0.002) and LOS (7 [1.0-57.0] vs. 5 [1.0-31.0] days, p = 0.011). In breakthrough cases, low-titer patients were more likely to be solid organ transplant recipients (16 [34.0%] vs. 9 [12.3%], p = 0.006), with higher need for ICU care (24 [51.1%] vs. 22 [11.0%], p = 0.034), longer hospital LOS (median 6 vs. 5 days, p = 0.013), and higher mortality (10 [21.3%] vs. 5 [6.8%], p = 0.025) than high-titer patients. CONCLUSIONS: Hospitalized breakthrough cases were more likely to have underlying risk factors than unvaccinated patients. Low-spike antibody titers may serve as an indicator for poor prognosis in breakthrough cases admitted to the hospital.

3.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-322109

ABSTRACT

Most COVID-19 predictive modeling efforts use statistical or mathematical models to predict national- and state-level COVID-19 cases or deaths in the future. These approaches assume parameters such as reproduction time, test positivity rate, hospitalization rate, and social intervention effectiveness (masking, distancing, and mobility) are constant. However, the one certainty with the COVID-19 pandemic is that these parameters change over time, as well as vary across counties and states. In fact, the rate of spread over region, hospitalization rate, hospital length of stay and mortality rate, the proportion of the population that is susceptible, test positivity rate, and social behaviors can all change significantly over time. Thus, the quantification of uncertainty becomes critical in making meaningful and accurate forecasts of the future. Bayesian approaches are a natural way to fully represent this uncertainty in mathematical models and have become particularly popular in physics and engineering models. The explicit integration time varying parameters and uncertainty quantification into a hierarchical Bayesian forecast model differentiates the Mayo COVID-19 model from other forecasting models. By accounting for all sources of uncertainty in both parameter estimation as well as future trends with a Bayesian approach, the Mayo COVID-19 model accurately forecasts future cases and hospitalizations, as well as the degree of uncertainty. This approach has been remarkably accurate and a linchpin in Mayo Clinic's response to managing the COVID-19 pandemic. The model accurately predicted timing and extent of the summer and fall surges at Mayo Clinic sites, allowing hospital leadership to manage resources effectively to provide a successful pandemic response. This model has also proven to be very useful to the state of Minnesota to help guide difficult policy decisions.

4.
Clin Infect Dis ; 2021 Nov 02.
Article in English | MEDLINE | ID: covidwho-1493775

ABSTRACT

We characterized COVID-19 breakthrough cases admitted to a single center in Florida. With the emergence of delta variant, an increased number of hospitalizations was seen due to breakthrough infections. These patients were older and more likely to have comorbidities. Preventive measures should be maintained even after vaccination.

5.
Mayo Clin Proc ; 96(7): 1890-1895, 2021 07.
Article in English | MEDLINE | ID: covidwho-1202099

ABSTRACT

Predictive models have played a critical role in local, national, and international response to the COVID-19 pandemic. In the United States, health care systems and governmental agencies have relied on several models, such as the Institute for Health Metrics and Evaluation, Youyang Gu (YYG), Massachusetts Institute of Technology, and Centers for Disease Control and Prevention ensemble, to predict short- and long-term trends in disease activity. The Mayo Clinic Bayesian SIR model, recently made publicly available, has informed Mayo Clinic practice leadership at all sites across the United States and has been shared with Minnesota governmental leadership to help inform critical decisions during the past year. One key to the accuracy of the Mayo Clinic model is its ability to adapt to the constantly changing dynamics of the pandemic and uncertainties of human behavior, such as changes in the rate of contact among the population over time and by geographic location and now new virus variants. The Mayo Clinic model can also be used to forecast COVID-19 trends in different hypothetical worlds in which no vaccine is available, vaccinations are no longer being accepted from this point forward, and 75% of the population is already vaccinated. Surveys indicate that half of American adults are hesitant to receive a COVID-19 vaccine, and lack of understanding of the benefits of vaccination is an important barrier to use. The focus of this paper is to illustrate the stark contrast between these 3 scenarios and to demonstrate, mathematically, the benefit of high vaccine uptake on the future course of the pandemic.


Subject(s)
COVID-19 Vaccines , COVID-19/prevention & control , COVID-19/epidemiology , Forecasting , Hospitalization/statistics & numerical data , Hospitalization/trends , Humans , United States/epidemiology
6.
Infect Control Hosp Epidemiol ; 42(12): 1479-1485, 2021 12.
Article in English | MEDLINE | ID: covidwho-1169333

ABSTRACT

OBJECTIVE: We evaluated the risk of patients contracting coronavirus disease 2019 (COVID-19) during their hospital stay to inform the safety of hospitalization for a non-COVID-19 indication during this pandemic. METHODS: A case series of adult patients hospitalized for 2 or more nights from May 15 to June 15, 2020 at large tertiary-care hospital in the midwestern United States was reviewed. All patients were screened at admission with the severe acute respiratory coronavirus virus 2 (SARS-CoV-2) polymerase chain reaction (PCR) test. Selected adult patients were also tested by IgG serology. After dismissal, patients with negative serology and PCR at admission were asked to undergo repeat serologic testing at 14-21 days after discharge. The primary outcome was healthcare-associated COVID-19 defined as a new positive SARS-CoV-2 PCR test on or after day 4 of hospital stay or within 7 days of hospital dismissal, or seroconversion in patients previously established as seronegative. RESULTS: Of the 2,068 eligible adult patients, 1,778 (86.0%) completed admission PCR testing, while 1,339 (64.7%) also completed admission serology testing. Of the 1,310 (97.8%) who were both PCR and seronegative, 445 (34.0%) repeated postdischarge serology testing. No healthcare-associated COVID-19 cases were detected during the study period. Of 1,310 eligible PCR and seronegative adults, no patients tested PCR positive during hospital admission (95% confidence interval [CI], 0.0%-0.3%). Of the 445 (34.0%) who completed postdischarge serology testing, no patients seroconverted (0.0%; 95% CI, 0.0%-0.9%). CONCLUSION: We found low likelihood of hospital-associated COVID-19 with strict adherence to universal masking, physical distancing, and hand hygiene along with limited visitors and screening of admissions with PCR.


Subject(s)
COVID-19 , Adult , Aftercare , Hospitals , Humans , Patient Discharge , SARS-CoV-2
7.
Nat Commun ; 12(1): 1951, 2021 03 29.
Article in English | MEDLINE | ID: covidwho-1157905

ABSTRACT

Serological detection of antibodies to SARS-CoV-2 is essential for establishing rates of seroconversion in populations, and for seeking evidence for a level of antibody that may be protective against COVID-19 disease. Several high-performance commercial tests have been described, but these require centralised laboratory facilities that are comparatively expensive, and therefore not available universally. Red cell agglutination tests do not require special equipment, are read by eye, have short development times, low cost and can be applied at the Point of Care. Here we describe a quantitative Haemagglutination test (HAT) for the detection of antibodies to the receptor binding domain of the SARS-CoV-2 spike protein. The HAT has a sensitivity of 90% and specificity of 99% for detection of antibodies after a PCR diagnosed infection. We will supply aliquots of the test reagent sufficient for ten thousand test wells free of charge to qualified research groups anywhere in the world.


Subject(s)
Antibodies, Viral/analysis , COVID-19 Testing/methods , COVID-19/diagnosis , Hemagglutination Tests/methods , SARS-CoV-2/isolation & purification , Spike Glycoprotein, Coronavirus/immunology , Agglutination Tests/methods , Antibodies, Monoclonal/immunology , Antibodies, Viral/blood , Antibodies, Viral/immunology , COVID-19/blood , COVID-19/immunology , COVID-19/virology , Enzyme-Linked Immunosorbent Assay/methods , Humans , Point-of-Care Systems , Polymerase Chain Reaction , SARS-CoV-2/immunology , Sensitivity and Specificity , Seroconversion
8.
Mayo Clin Proc ; 96(3): 690-698, 2021 03.
Article in English | MEDLINE | ID: covidwho-1002862

ABSTRACT

In March 2020, our institution developed an interdisciplinary predictive analytics task force to provide coronavirus disease 2019 (COVID-19) hospital census forecasting to help clinical leaders understand the potential impacts on hospital operations. As the situation unfolded into a pandemic, our task force provided predictive insights through a structured set of visualizations and key messages that have helped the practice to anticipate and react to changing operational needs and opportunities. The framework shared here for the deployment of a COVID-19 predictive analytics task force could be adapted for effective implementation at other institutions to provide evidence-based messaging for operational decision-making. For hospitals without such a structure, immediate consideration may be warranted in light of the devastating COVID-19 third-wave which has arrived for winter 2020-2021.


Subject(s)
COVID-19/therapy , Decision Making , Disease Management , Hospitals/statistics & numerical data , Intensive Care Units/statistics & numerical data , Pandemics , SARS-CoV-2 , COVID-19/epidemiology , Forecasting , Humans
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