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1.
Transbound Emerg Dis ; 2022 Aug 02.
Article in English | MEDLINE | ID: covidwho-1973746

ABSTRACT

The aim of this study was to characterize the infection of weaned pigs with swine enteric coronavirus (SeCoV) -a chimeric virus most likely originated from a recombination event between porcine epidemic diarrhea virus (PEDV) and transmissible gastroenteritis virus, or its mutant porcine respiratory coronavirus-, and two PEDV G1b variants, including a recently described recombinant PEDV-SeCoV (rPEDV-SeCoV), as well as to determine the degree of cross-protection achieved against the rPEDV-SeCoV. For this purpose, forty-eight 4-week-old weaned pigs were randomly allocated into four groups of 12 animals; piglets within each group were primary inoculated with one of the investigated viral strains (B: PEDV; C: SeCoV and D: rPEDV-SeCoV) or mock-inoculated (A), and exposed to rPEDV-SeCOV at day 20 post-infection; thus, group A was primary challenged (-/rPEDV-SeCoV), groups B and C were subjected to a heterologous re-challenge (PEDV/rPEDV-SeCoV and SeCoV/rPEDV-SeCoV, respectively), and group D to a homologous re-challenge (rPEDV-SeCoV/rPEDV-SeCoV), Clinical signs, viral shedding, microscopic lesions and specific humoral and cellular immune responses (IgG, IgA, neutralizing antibodies and IgA and IFN-γ-secreting cells) were monitored. After primo-infection all three viral strains induced an undistinguishable mild-to-moderate clinical disease with diarrhea as the main sign and villus shortening lesions in the small intestine. In homologous re-challenged pigs, no clinical signs or lesions were observed, and viral shedding was only detected in a single animal. This fact may be explained by the significant high level of rPEDV-SeCoV-specific neutralizing antibodies found in these pigs before the challenge. In contrast, prior exposure to a different PEDV G1b variant or SeCoV only provided partial cross-protection, allowing rPEDV-SeCoV replication and shedding in feces. This article is protected by copyright. All rights reserved.

2.
J Stroke Cerebrovasc Dis ; 31(8): 106589, 2022 Aug.
Article in English | MEDLINE | ID: covidwho-1945834

ABSTRACT

OBJECTIVES: To derive models that identify patients with COVID-19 at high risk for stroke. MATERIALS AND METHODS: We used data from the AHA's Get With The Guidelines® COVID-19 Cardiovascular Disease Registry to generate models for predicting stroke risk among adults hospitalized with COVID-19 at 122 centers from March 2020-March 2021. To build our models, we used data on demographics, comorbidities, medications, and vital sign and laboratory values at admission. The outcome was a cerebrovascular event (stroke, TIA, or cerebral vein thrombosis). First, we used Cox regression with cross validation techniques to identify factors associated with the outcome in both univariable and multivariable analyses. Then, we assigned points for each variable based on corresponding coefficients to create a prediction score. Second, we used machine learning techniques to create risk estimators using all available covariates. RESULTS: Among 21,420 patients hospitalized with COVID-19, 312 (1.5%) had a cerebrovascular event. Using traditional Cox regression, we created/validated a COVID-19 stroke risk score with a C-statistic of 0.66 (95% CI, 0.60-0.72). The CANDLE score assigns 1 point each for prior cerebrovascular disease, afebrile temperature, no prior pulmonary disease, history of hypertension, leukocytosis, and elevated systolic blood pressure. CANDLE stratified risk of an acute cerebrovascular event according to low- (0-1: 0.2% risk), medium- (2-3: 1.1% risk), and high-risk (4-6: 2.1-3.0% risk) groups. Machine learning estimators had similar discriminatory performance as CANDLE: C-statistics, 0.63-0.69. CONCLUSIONS: We developed a practical clinical score, with similar performance to machine learning estimators, to help stratify stroke risk among patients hospitalized with COVID-19.


Subject(s)
COVID-19 , Stroke , Adult , COVID-19/complications , COVID-19/diagnosis , Hospitalization , Humans , Risk Assessment/methods , Risk Factors , Stroke/diagnosis , Stroke/epidemiology , Stroke/therapy
3.
EuropePMC; 2022.
Preprint in English | EuropePMC | ID: ppcovidwho-337699

ABSTRACT

Background: Observational research provides a unique opportunity to learn causal effects when randomized trials are not available, but obtaining the correct estimates hinges on a multitude of design and analysis choices. We illustrate the advantages of modern causal inference methods and compare to standard research practice to estimate the effect of corticosteroids on mortality in hospitalized COVID-19 patients in an observational dataset. We use several large RCTs to benchmark our results. Methods: Our retrospective data source consists of 3,293 COVID-19 patients hospitalized at New York Presbyterian March 1-May 15, 2020. We design our study using the Target Trial Emulation framework. We estimate the effect of an intervention consisting of 6 days of corticosteroids administered at the time of severe hypoxia and contrast with an intervention consisting of no corticosteroids administration. The dataset includes dozens of time-varying confounders. We estimate the causal effects using a doubly robust estimator where the probabilities of treatment, outcome, and censoring are estimated using flexible regressions via super learning. We compare these analyses to standard practice in clinical research, consisting of two main methods: (i) Cox models for an exposure of corticosteroids receipt within various time windows of hypoxia, and (ii) a Cox time-varying model where the exposure is daily administration of corticosteroids starting at the time of hospitalization. Results: The effect in our target trial emulation is qualitatively identical to an RCT benchmark, estimated to reduce 28-day mortality from 32% (95% confidence interval: 31-34) to 23% (21-24). The estimated effect from meta-analyses of RCTs for corticosteroids is an odds ratio of 0.66 (0.53-0.82)(1). Hazard ratios from the Cox models range in size and direction from 0.50 (0.41-0.62) to 1.08 (0.80-1.47) and all study designs suffer from various forms of bias. Conclusion: We demonstrate in a case study that clinical research based on observational data can unveil true causal relations. However, the correctness of these effect estimates requires designing and analyzing the data based on principles which are different from the current standard in clinical research. The widespread communication and adoption of these design and analytical techniques is of high importance for the improvement of clinical research based on observational data.

4.
EuropePMC; 2022.
Preprint in English | EuropePMC | ID: ppcovidwho-336068

ABSTRACT

Global emergence and re-emergence of Porcine epidemic diarrhea virus (PEDV), an Alphacoronavirus which causes a highly contagious enteric disease, have led to several studies addressing its variability. The aim of this study was to characterize the infection of weaned pigs with Swine enteric coronavirus (SeCoV) -a chimeric virus most likely originated from a recombination event between PEDV and Transmissible gastroenteritis virus, or its mutant Porcine respiratory coronavirus- , and two PEDV G1b variants, including a recently described recombinant PEDV-SeCoV (rPEDV-SeCoV), as well as to determine the degree of cross-protection achieved against the rPEDV-SeCoV. For this purpose, forty-eight 4-week-old weaned pigs were randomly allocated into four groups of 12 animals;piglets in groups B, C and D were orally inoculated with a PEDV variant (B and D) or SeCoV (C), while piglets in group A were mock inoculated and maintained as controls. At day 20 post-infection all groups were exposed to rPEDV-SeCoV;thus, group D was subjected to a homologous re-challenge, groups B and C to a heterologous re-challenge (PEDV/rPEDV-SeCoV and SeCoV/rPEDV-SeCoV, respectively) and group A was primary challenged (-/rPEDV-SeCoV). Clinical signs, viral shedding, microscopic lesions and specific humoral and cellular immune responses (IgG, IgA, neutralizing antibodies and IgA and IFN-γ-secreting cells) were monitored. After primo-infection all three viral strains induced an undistinguishable mild-to-moderate clinical disease with diarrhea as the main sign and villus shortening lesions in the small intestine. In homologous re-challenged pigs, no clinical signs or lesions were observed, and viral shedding was only detected in a single animal. This fact may be explained by the significant high level of rPEDV-SeCoV-specific neutralizing antibodies found in these pigs before the challenge. In contrast, prior exposition to a different PEDV G1b variant or SeCoV only provided partial cross-protection, allowing rPEDV-SeCoV replication and shedding in feces.

5.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-306662

ABSTRACT

Combating the SARS-CoV2 pandemic will require the fast development of effective preventive vaccines. Regulatory agencies may open accelerated approval pathways for vaccines if an immunological marker can be established as a mediator of a vaccine's protection. A rich source of information for identifying such correlates are large-scale efficacy trials of COVID-19 vaccines, where immune responses are measured subject to a case-cohort sampling design. We propose two approaches to estimation of mediation parameters in the context of case-cohort sampling designs. We establish the theoretical large-sample efficiency of our proposed estimators and evaluate them in a realistic simulation to understand whether they can be employed in the analysis of COVID-19 vaccine efficacy trials.

6.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-324006

ABSTRACT

The rapid finding of effective therapeutics requires the efficient use of available resources in clinical trials. The use of covariate adjustment can yield statistical estimates with improved precision, resulting in a reduction in the number of participants required to draw futility or efficacy conclusions. We focus on time-to-event and ordinal outcomes. A key question for covariate adjustment in randomized studies is how to fit a model relating the outcome and the baseline covariates to maximize precision. We present a novel theoretical result establishing conditions for asymptotic normality of a variety of covariate-adjusted estimators that rely on machine learning (e.g., l1-regularization, Random Forests, XGBoost, and Multivariate Adaptive Regression Splines), under the assumption that outcome data is missing completely at random. We further present a consistent estimator of the asymptotic variance. Importantly, the conditions do not require the machine learning methods to converge to the true outcome distribution conditional on baseline variables, as long as they converge to some (possibly incorrect) limit. We conducted a simulation study to evaluate the performance of the aforementioned prediction methods in COVID-19 trials using longitudinal data from over 1,500 patients hospitalized with COVID-19 at Weill Cornell Medicine New York Presbyterian Hospital. We found that using l1-regularization led to estimators and corresponding hypothesis tests that control type 1 error and are more precise than an unadjusted estimator across all sample sizes tested. We also show that when covariates are not prognostic of the outcome, l1-regularization remains as precise as the unadjusted estimator, even at small sample sizes (n = 100). We give an R package adjrct that performs model-robust covariate adjustment for ordinal and time-to-event outcomes.

7.
EuropePMC;
Preprint in Portuguese | EuropePMC | ID: ppcovidwho-328168

ABSTRACT

A raíz de la pandemia del SARS-CoV-2, se viene prestando mayor interés a una línea de estudio que demuestra cómo la medición de la satisfacción en la educación virtual tiene impactos positivos en la formación universitaria. En ese sentido, esta investigación presenta dos propósitos: (a) describir la conceptualización y tipología de la satisfacción en la educación virtual;y (b) identificar los beneficios y condiciones de estas en la instrucción. El método llevado a cabo, ha utilizado la declaración PRISMA de revisión sistemática y las pesquisas se recopilaron de fuentes de datos como Scopus, ERIC y EBSCO Discovery Service. En concordancia con los resultados, se ha seleccionado un total de 50 estudios internacionales, cuyo contenido aporta a la literatura científica en la sistematización de criterios conceptuales para categorizar tipos de satisfacción y también los factores condicionantes, limitantes y predominantes para la misma, como son los roles del estudiante y del profesor, el curso virtual, la conectividad, la tecnología y la gestión institucional. Además, se observan, identifican, reconocen e incorporan los beneficios obtenidos en los aspectos personales, profesionales y sociales del estudiantado. Finalmente, se expone la necesidad de desarrollar pesquisas que ahonden en cada una de las condiciones para alcanzar la satisfacción.

9.
Transport Policy ; 2021.
Article in English | ScienceDirect | ID: covidwho-1230801

ABSTRACT

This paper discusses the importance of incorporating online home delivery services (OHDS) into the concept of accessibility and marginalization. The authors propose a method to quantify access to OHDS and assess levels of inequalities in access to OHDS using data from OHDS providers in the pharmaceutical and food sectors, as well as from transport operators delivering parcels. The Västra Götaland Region in the West coast of Sweden is used as a case study. The results show significant inequalities in access to OHDS. Moreover, there are segments of population under a compound marginalization during the COVID-19 pandemic due to (i) limited accessibility to OHDS services, (ii) high incidence of COVID-19 cases in their area that makes physical visits to a store a risk activity, and (iii) high vulnerability (e.g., high share of individuals older than 65). These results reveal a need for the public sector to prioritize innovations in services that target specific clusters of the population that are vulnerable and marginalized, but also shows the imminent risk for some of these segments during the pandemic.

10.
Biometrics ; 77(4): 1467-1481, 2021 12.
Article in English | MEDLINE | ID: covidwho-796092

ABSTRACT

Time is of the essence in evaluating potential drugs and biologics for the treatment and prevention of COVID-19. There are currently 876 randomized clinical trials (phase 2 and 3) of treatments for COVID-19 registered on clinicaltrials.gov. Covariate adjustment is a statistical analysis method with potential to improve precision and reduce the required sample size for a substantial number of these trials. Though covariate adjustment is recommended by the U.S. Food and Drug Administration and the European Medicines Agency, it is underutilized, especially for the types of outcomes (binary, ordinal, and time-to-event) that are common in COVID-19 trials. To demonstrate the potential value added by covariate adjustment in this context, we simulated two-arm, randomized trials comparing a hypothetical COVID-19 treatment versus standard of care, where the primary outcome is binary, ordinal, or time-to-event. Our simulated distributions are derived from two sources: longitudinal data on over 500 patients hospitalized at Weill Cornell Medicine New York Presbyterian Hospital and a Centers for Disease Control and Prevention preliminary description of 2449 cases. In simulated trials with sample sizes ranging from 100 to 1000 participants, we found substantial precision gains from using covariate adjustment-equivalent to 4-18% reductions in the required sample size to achieve a desired power. This was the case for a variety of estimands (targets of inference). From these simulations, we conclude that covariate adjustment is a low-risk, high-reward approach to streamlining COVID-19 treatment trials. We provide an R package and practical recommendations for implementation.


Subject(s)
COVID-19 , COVID-19/drug therapy , Hospitalization , Humans , Randomized Controlled Trials as Topic , SARS-CoV-2 , Treatment Outcome , United States
11.
JAMA Neurol ; 2020 Jul 02.
Article in English | MEDLINE | ID: covidwho-627768

ABSTRACT

IMPORTANCE: It is uncertain whether coronavirus disease 2019 (COVID-19) is associated with a higher risk of ischemic stroke than would be expected from a viral respiratory infection. OBJECTIVE: To compare the rate of ischemic stroke between patients with COVID-19 and patients with influenza, a respiratory viral illness previously associated with stroke. DESIGN, SETTING, AND PARTICIPANTS: This retrospective cohort study was conducted at 2 academic hospitals in New York City, New York, and included adult patients with emergency department visits or hospitalizations with COVID-19 from March 4, 2020, through May 2, 2020. The comparison cohort included adults with emergency department visits or hospitalizations with influenza A/B from January 1, 2016, through May 31, 2018 (spanning moderate and severe influenza seasons). EXPOSURES: COVID-19 infection confirmed by evidence of severe acute respiratory syndrome coronavirus 2 in the nasopharynx by polymerase chain reaction and laboratory-confirmed influenza A/B. MAIN OUTCOMES AND MEASURES: A panel of neurologists adjudicated the primary outcome of acute ischemic stroke and its clinical characteristics, mechanisms, and outcomes. We used logistic regression to compare the proportion of patients with COVID-19 with ischemic stroke vs the proportion among patients with influenza. RESULTS: Among 1916 patients with emergency department visits or hospitalizations with COVID-19, 31 (1.6%; 95% CI, 1.1%-2.3%) had an acute ischemic stroke. The median age of patients with stroke was 69 years (interquartile range, 66-78 years); 18 (58%) were men. Stroke was the reason for hospital presentation in 8 cases (26%). In comparison, 3 of 1486 patients with influenza (0.2%; 95% CI, 0.0%-0.6%) had an acute ischemic stroke. After adjustment for age, sex, and race, the likelihood of stroke was higher with COVID-19 infection than with influenza infection (odds ratio, 7.6; 95% CI, 2.3-25.2). The association persisted across sensitivity analyses adjusting for vascular risk factors, viral symptomatology, and intensive care unit admission. CONCLUSIONS AND RELEVANCE: In this retrospective cohort study from 2 New York City academic hospitals, approximately 1.6% of adults with COVID-19 who visited the emergency department or were hospitalized experienced ischemic stroke, a higher rate of stroke compared with a cohort of patients with influenza. Additional studies are needed to confirm these findings and to investigate possible thrombotic mechanisms associated with COVID-19.

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