Diversity of respiratory viruses present in nasal swabs under influenza suspicion in respiratory disease cases of weaned pigs.

Respiratory diseases in weaned pigs are a common problem, with a complex etiology involving both viruses and bacteria. In the present study, we investigated the presence of eleven viruses in nasal swabs, collected from nurseries (55 cases) under the suspicion of swine influenza A virus (swIAV) and submitted by swine veterinarians for diagnosis. The other ten viruses included in the study were influenza B (IBV) and D (IDV), Porcine reproductive and respiratory syndrome virus (PRRSV), Porcine respiratory coronavirus (PRCV), Porcine cytomegalovirus (PCMV), Porcine circovirus 2 (PCV2), 3 (PCV3) and 4 (PCV), Porcine parainfluenza 1 (PPIV1) and Swine orthopneumovirus (SOV). Twenty-six swIAV-positive cases and twenty-nine cases of swIAV-negative respiratory disease were primarily established. While IBV, IDV, PCV4 and PPIV1 were not found in any of the cases, PRCV, SOV, and PCMV were more likely to be found in swIAV-positive nurseries with respiratory disease (p < 0.05). Overall, PCV3, PRRSV, and PCMV were the most frequently detected agents at herd level. Taken individually, virus prevalence was: swIAV, 48.6%; PRCV, 48.0%; PRRSV, 31.6%; SOV, 33.8%; PCMV, 48.3%, PCV2, 36.0%; and PCV3, 33.0%. Moreover, low Ct values (<30) were common for all agents, except PCV2 and PCV3. When the correlation between pathogens was individually examined, the presence of PRRSV was negatively correlated with swIAV and PRCV, while was positively associated to PCMV (p < 0.05). Also, PRCV and SOV were positively correlated between them and negatively with PCMV. Besides, the analysis of suckling pig samples, collected in subclinically infected farrowing units under an influenza monitoring program, showed that circulation of PRCV, PCMV, SOV, and PCV3 started during the early weeks of life. Interestingly, in those subclinically infected units, none of the pathogens was found to be correlated to any other. Overall, our data may contribute to a better understanding of the complex etiology and epidemiology of respiratory diseases in weaners. This is the first report of SOV in Spain and shows, for the first time, the dynamics of this pathogen in swine farms.

Influence of social deprivation index on in-hospital outcomes of COVID-19.

While it is known that social deprivation index (SDI) plays an important role on risk for acquiring Coronavirus Disease 2019 (COVID-19), the impact of SDI on in-hospital outcomes such as intubation and mortality are less well-characterized. We analyzed electronic health record data of adults hospitalized with confirmed COVID-19 between March 1, 2020 and February 8, 2021 from the INSIGHT Clinical Research Network (CRN). To compute the SDI (exposure variable), we linked clinical data using patient's residential zip-code with social data at zip-code tabulation area. SDI is a composite of seven socioeconomic characteristics determinants at the zip-code level. For this analysis, we categorized SDI into quintiles. The two outcomes of interest were in-hospital intubation and mortality. For each outcome, we examined logistic regression and random forests to determine incremental value of SDI in predicting outcomes. We studied 30,016 included COVID-19 patients. In a logistic regression model for intubation, a model including demographics, comorbidity, and vitals had an Area under the receiver operating characteristic curve (AUROC) = 0.73 (95% CI 0.70-0.75); the addition of SDI did not improve prediction [AUROC = 0.73 (95% CI 0.71-0.75)]. In a logistic regression model for in-hospital mortality, demographics, comorbidity, and vitals had an AUROC = 0.80 (95% CI 0.79-0.82); the addition of SDI in Model 2 did not improve prediction [AUROC = 0.81 (95% CI 0.79-0.82)]. Random forests revealed similar findings. SDI did not provide incremental improvement in predicting in-hospital intubation or mortality. SDI plays an important role on who acquires COVID-19 and its severity; but once hospitalized, SDI appears less important.

Diversity of respiratory viruses present in nasal swabs under influenza suspicion in respiratory disease cases of weaned pigs

Respiratory diseases in weaned pigs are a common problem, with a complex etiology involving both viruses and bacteria. In the present study, we investigated the presence of eleven viruses in nasal swabs, collected from nurseries (55 cases) under the suspicion of swine influenza A virus (swIAV) and submitted by swine veterinarians for diagnosis. The other ten viruses included in the study were influenza B (IBV) and D (IDV), Porcine reproductive and respiratory syndrome virus (PRRSV), Porcine respiratory coronavirus (PRCV), Porcine cytomegalovirus (PCMV), Porcine circovirus 2 (PCV2), 3 (PCV3) and 4 (PCV), Porcine parainfluenza 1 (PPIV1) and Swine orthopneumovirus (SOV). Twenty-six swIAV-positive cases and twenty-nine cases of swIAV-negative respiratory disease were primarily established. While IBV, IDV, PCV4 and PPIV1 were not found in any of the cases, PRCV, SOV, and PCMV were more likely to be found in swIAV-positive nurseries with respiratory disease (p < 0.05). Overall, PCV3, PRRSV, and PCMV were the most frequently detected agents at herd level. Taken individually, virus prevalence was: swIAV, 48.6%;PRCV, 48.0%;PRRSV, 31.6%;SOV, 33.8%;PCMV, 48.3%, PCV2, 36.0%;and PCV3, 33.0%. Moreover, low Ct values (<30) were common for all agents, except PCV2 and PCV3. When the correlation between pathogens was individually examined, the presence of PRRSV was negatively correlated with swIAV and PRCV, while was positively associated to PCMV (p < 0.05). Also, PRCV and SOV were positively correlated between them and negatively with PCMV. Besides, the analysis of suckling pig samples, collected in subclinically infected farrowing units under an influenza monitoring program, showed that circulation of PRCV, PCMV, SOV, and PCV3 started during the early weeks of life. Interestingly, in those subclinically infected units, none of the pathogens was found to be correlated to any other. Overall, our data may contribute to a better understanding of the complex etiology and epidemiology of respiratory diseases in weaners. This is the first report of SOV in Spain and shows, for the first time, the dynamics of this pathogen in swine farms.

Optimising precision and power by machine learning in randomised trials with ordinal and time‐to‐event outcomes with an application to COVID‐19

The rapid finding of effective therapeutics requires efficient use of available resources in clinical trials. 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. When more than a few baseline covariates are available, a key question for covariate adjustment in randomised studies is how to fit a model relating the outcome and the baseline covariates to maximise 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‐regularisation, Random Forests, XGBoost, and Multivariate Adaptive Regression Splines [MARS]), under the assumption that outcome data are 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. Our simulation is based on resampling longitudinal data from over 1500 patients hospitalised with COVID‐19 at Weill Cornell Medicine New York Presbyterian Hospital. We found that using ℓ1‐regularisation 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‐regularisation 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.

Optimising precision and power by machine learning in randomised trials with ordinal and time-to-event outcomes with an application to COVID-19.

The rapid finding of effective therapeutics requires efficient use of available resources in clinical trials. 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. When more than a few baseline covariates are available, a key question for covariate adjustment in randomised studies is how to fit a model relating the outcome and the baseline covariates to maximise 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., ℓ 1 -regularisation, Random Forests, XGBoost, and Multivariate Adaptive Regression Splines [MARS]), under the assumption that outcome data are 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. Our simulation is based on resampling longitudinal data from over 1500 patients hospitalised with COVID-19 at Weill Cornell Medicine New York Presbyterian Hospital. We found that using ℓ 1 -regularisation 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, ℓ 1 -regularisation 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.

Comparison of a Target Trial Emulation Framework vs Cox Regression to Estimate the Association of Corticosteroids With COVID-19 Mortality.

Importance: Communication and adoption of modern study design and analytical techniques is of high importance for the improvement of clinical research from observational data. Objective: To compare a modern method for statistical inference, including a target trial emulation framework and doubly robust estimation, with approaches common in the clinical literature, such as Cox proportional hazards models. Design, Setting, and Participants: This retrospective cohort study used longitudinal electronic health record data for outcomes at 28-days from time of hospitalization within a multicenter New York, New York, hospital system. Participants included adult patients hospitalized between March 1 and May 15, 2020, with COVID-19 and not receiving corticosteroids for chronic use. Data were analyzed from October 2021 to March 2022. Exposures: Corticosteroid exposure was defined as more than 0.5 mg/kg methylprednisolone equivalent in a 24-hour period. For target trial emulation, exposures were corticosteroids for 6 days if and when a patient met criteria for severe hypoxia vs no corticosteroids. For approaches common in clinical literature, treatment definitions used for variables in Cox regression models varied by study design (no time frame, 1 day, and 5 days from time of severe hypoxia). Main Outcomes and Measures: The main outcome was 28-day mortality from time of hospitalization. The association of corticosteroids with mortality for patients with moderate to severe COVID-19 was assessed using the World Health Organization (WHO) meta-analysis of corticosteroid randomized clinical trials as a benchmark. Results: A total of 3298 patients (median [IQR] age, 65 [53-77] years; 1970 [60%] men) were assessed, including 423 patients who received corticosteroids at any point during hospitalization and 699 patients who died within 28 days of hospitalization. Target trial emulation analysis found corticosteroids were associated with a reduced 28-day mortality rate, from 32.2%; (95% CI, 30.9%-33.5%) to 25.7% (95% CI, 24.5%-26.9%). This estimate is qualitatively identical to the WHO meta-analysis odds ratio of 0.66 (95% CI, 0.53-0.82). Hazard ratios using methods comparable with current corticosteroid research range in size and direction, from 0.50 (95% CI, 0.41-0.62) to 1.08 (95% CI, 0.80-1.47). Conclusions and Relevance: These findings suggest that clinical research based on observational data can be used to estimate findings similar to those from randomized clinical trials; however, the correctness of these estimates requires designing the study and analyzing the data based on principles that are different from the current standard in clinical research.

Characterization and cross-protection of experimental infections with SeCoV and two PEDV variants.

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 diarrhoea 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 diarrhoea 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 faeces.

Risk Stratification Models for Stroke in Patients Hospitalized with COVID-19 Infection.

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.

Assessing the inequalities in access to online delivery services and the way COVID-19 pandemic affects marginalization.

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.

Improving precision and power in randomized trials for COVID-19 treatments using covariate adjustment, for binary, ordinal, and time-to-event outcomes.

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.

Risk of Ischemic Stroke in Patients With Coronavirus Disease 2019 (COVID-19) vs Patients With Influenza.

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.