Effectiveness of casirivimab and imdevimab, and sotrovimab during Delta variant surge: a prospective cohort study and comparative effectiveness randomized trial

IMPORTANCE The effectiveness of monoclonal antibodies (mAbs), casirivimab and imdevimab, and sotrovimab, for patients with mild to moderate Covid-19 from the Delta variant is unknown. OBJECTIVE To evaluate the effectiveness of mAbs for the Delta variant compared to no treatment, and the comparative effectiveness between mAbs. DESIGN, SETTING, AND PARTICIPANTS Two parallel studies among patients who met Emergency Use Authorization criteria for mAbs from July 14, 2021 to September 29, 2021: i.) prospective observational cohort study comparing mAb treatment to no mAb treatment and, ii.) Bayesian adaptive randomized trial comparing the effectiveness of casirivimab-imdevimab versus sotrovimab. In the observational study, we compared eligible patients who received mAb at an outpatient infusion center at UPMC, to nontreated patients with a positive SARS-CoV-2 test. In the comparative effectiveness trial, we randomly allocated casirivimab-imdevimab or sotrovimab to patients presenting to infusion centers and emergency departments, per system therapeutic interchange policy. EXPOSURE Intravenous mAb per their EUA criteria. MAIN OUTCOMES AND MEASURES For the observational study, risk ratio estimates for hospitalization or death by 28 days were compared between mAb treatment to no mAb treatment using propensity matched models. For the comparative effectiveness trial, the primary outcome was hospital-free days (days alive and free of hospital) within 28 days, where patients who died were assigned -1 day) in a Bayesian cumulative logistic model, adjusted for treatment location, age, sex, and time. Inferiority was defined as a 99% posterior probability of an odds ratio <1. Equivalence was defined as a 95% posterior probability that the odds ratio is within a given bound. RESULTS Among 3,558 patients receiving mAb, the mean age was 54 (SD 18 years), 1,511 (43%) were treated in an infusion center, and 450 (13%) were hospitalized or died by day 28. In propensity matched models, mAb treatment was associated with reduced risk of hospitalization or death compared to no treatment (risk ratio (RR)=0.40, 95% CI: 0.28-0.57). Both casirivimab and imdevimab (RR=0.31, 95% CI: 0.20-0.50), and sotrovimab (RR=0.60, 95% CI: 0.37-1.00) reduced hospitalization or death compared to no mAb treatment. Among patients allocated randomly to casirivimab and imdevimab (n=2,454) or sotrovimab (n=1,104), the median hospital-free days were 28 (IQR 28-28) for both groups, 28-day mortality was 0.5% (n=12) and 0.6% (n=7), and hospitalization by day 28 was 12% (n=291) and 12% (n=140), respectively. Compared to casirivimab and imdevimab, the median adjusted odds ratio for hospital-free days was 0.88 (95% credible interval, 0.70-1.11) for sotrovimab. This odds ratio yielded 86% probability of inferiority of sotrovimab versus casirivimab and imdevimab, and 79% probability of equivalence. CONCLUSIONS AND RELEVANCE In non-hospitalized patients with mild to moderate Covid-19 due to the Delta variant, casirivimab and imdevimab and sotrovimab were both associated with a reduced risk of hospitalization or death. The comparative effectiveness of mAbs appeared similar, though prespecified criteria for statistical inferiority or equivalence were not met. TRIAL REGISTRATION ClinicalTrials.gov: NCT04790786

Semi supervised inspection algorithm of automatic packaging curve based on deep learning

The current automatic packaging process is complex, requires high professional knowledge, poor universality, and difficult to apply in multi-objective and complex background. In view of this problem, automatic packaging optimization algorithm has been widely paid attention to. However, the traditional automatic packaging detection accuracy is low, the practicability is poor. Therefore, a semi-supervised detection method of automatic packaging curve based on deep learning and semi-supervised learning is proposed. Deep learning is used to extract features and posterior probability to classify unlabeled data. KDD CUP99 data set was used to verify the accuracy of the algorithm. Experimental results show that this method can effectively improve the performance of automatic packaging curve semi-supervised detection system.

Elevated De Ritis Ratio Is Associated With Poor Prognosis in COVID-19: A Systematic Review and Meta-Analysis

Objective: This meta-analysis aims to assess whether elevated De Ritis ratio is associated with poor prognosis in patients with coronavirus 2019 (COVID-19).Methods: A systematic literature search was performed using PubMed, Embase, and EuropePMC databases up until September 17, 2021. De Ritis ratio is also known as Aspartate aminotransferase/alanine transaminase (AST/ALT) ratio. The main outcome was poor prognosis, a composite of mortality, severity, the need for ICU care, and intubation. The effect measure was odds ratios (ORs) and mean differences. We generated sensitivity and specificity, negative and positive likelihood ratio (NLR and PLR), diagnostic odds ratio (DOR), and area under curve (AUC).Results: There were eight studies with 4,606 patients. De Ritis ratio was elevated in 44% of the patients. Patients with poor prognosis have higher De Ritis ratio [mean difference 0.41 (0.31, 0.50), p &lt; 0.001; I2: 81.0%] and subgroup analysis showed that non-survivors also have higher De Ritis Ratio [mean difference 0.47 (0.46, 0.48), p &lt; 0.001; I2: 0%]. Elevated De Ritis ratio was associated with poor prognosis [OR 3.28 (2.39, 4.52), p &lt; 0.001; I2: 35.8%]. It has a sensitivity of 55% (36–73), specificity of 71% (52–85), PLR 1.9, NLR.63, DOR of 3 (2–4), and AUC of.67 (0.63–0.71). The posterior probability of poor prognosis was 38% if De Ritis is elevated, while 17% if De Ritis is not elevated.Conclusion: Elevated De Ritis ratio is associated with poor prognosis in patients with COVID-19.Systematic Review Registration: PROSPERO ID: CRD42020216634.

Taxonomic and phylogenic appraisal of a novel species and a new record of Stictidaceae from coffee in Yunnan Province, China

Coffee, an important economic crop, is often threatened by fungal infections. During a survey of coffee fungi in Yunnan Province, China, two saprobic Stictidaceae species were collected. Maximum likelihood (ML) and Bayesian posterior probability of combined LSU, ITS and mtSSU genes supported the placement of our fungal collections within Fitzroyomyces and Ostropomyces with high statistical support. A new species, Fitzroyomyces yunnanensis sp. nov. and a new record, Ostropomyces pruinosellus are introduced. These two species were recorded on coffee wood in sexual and asexual states, respectively. Their taxonomic placements were further supported by detailed morphological and phylogenetic comparisons of allied taxa.

Ecological surveillance of bat coronaviruses in Sarawak, Malaysian Borneo

Objective Coronaviruses (CoVs) are natural commensals of bats. Two subgenera, namely Sarbecoviruses and Merbecoviruses have a high zoonotic potential and have been associated with three separate spillover events in the past 2 decades, making surveillance of bat-CoVs crucial for the prevention of the next epidemic. The study was aimed to elucidate the presence of coronavirus in fresh bat guano sampled from Wind Cave Nature Reserve (WCNR) in Sarawak, Malaysian Borneo. Samples collected were placed into viral transport medium, transported on ice within the collection day, and preserved at − 80 °C. Nucleic acid was extracted using the column method and screened using consensus PCR primers targeting the RNA-dependent RNA polymerase (RdRp) gene. Amplicons were sequenced bidirectionally using the Sanger method. Phylogenetic tree with maximum-likelihood bootstrap and Bayesian posterior probability were constructed. Results CoV-RNA was detected in ten specimens (47.6%, n = 21). Six alphacoronavirus and four betacoronaviruses were identified. The bat-CoVs can be phylogenetically grouped into four novel clades which are closely related to Decacovirus-1 and Decacovirus-2, Sarbecovirus, and an unclassified CoV. CoVs lineages unique to the Island of Borneo were discovered in Sarawak, Malaysia, with one of them closely related to Sarbecovirus. All of them are distant from currently known human coronaviruses.

Cardiac Computed Tomography Versus Transesophageal Echocardiography for the Detection of Left Atrial Appendage Thrombus: A Systemic Review and Meta‐Analysis

Background Transesophageal echocardiography (TEE) has been considered the gold standard for left atrial appendage (LAA) thrombus detection. Nevertheless, TEE may sometimes induce discomfort and cause complications. Cardiac computed tomography has been studied extensively for LAA thrombus detection. We performed this systemic review and meta‐analysis to assess the diagnostic accuracy of cardiac computed tomography for LAA thrombus detection compared with TEE. Methods and Results A systemic search was conducted in the PubMed, Embase, and Cochrane Library databases from January 1977 to February 2021. Studies performed for assessment diagnostic accuracy of cardiac computed tomography on LAA thrombus compared with TEE were included. Summary sensitivity, specificity, and posterior probability of LAA thrombus was calculated by using bivariate random‐effects model. The Quality Assessment of Diagnostic Accuracy Studies‐2 tool was used for the quality assessment. A total of 27 studies involving 6960 patients were included in our study. The summary sensitivity of early imaging studies was 0.95 (95% CI, 0.79–0.99), and the specificity was 0.89 (95% CI, 0.85–0.92). The positive posterior probability was 19.11%, and the negative posterior probability was 0.16%. The summary sensitivity of delayed imaging studies was 0.98 (95% CI, 0.92–1.00), and the specificity was 1.00 (95% CI, 0.98–1.00). The positive posterior probability was 95.76%, and the negative posterior probability was 0.12%. The delayed imaging method significantly improved the specificity (1.00 versus 0.89; P <0.05) and positive posterior probability (95.76% versus 19.11%; P <0.05). Conclusions Cardiac computed tomography with a delayed imaging is a reliable alternative to TEE. It may save the patient and health care from an excess TEE. Registration URL: https://www.crd.york.ac.uk/PROSPERO ; Unique identifier: CRD42021236352.

Abstract 10617: Antiarrhythmic Use and the Posterior Probability of Neurologically Intact Survival in a Reanalysis of the Amiodarone, Lidocaine or Placebo Study in Out-of-Hospital Cardiac Arrest

Introduction: There are no randomized controlled trials (RCT) demonstrating improvement in neurologically intact survival from antiarrhythmic therapy given during out-of-hospital cardiac arrest (OHCA) from ventricular fibrillation/tachycardia (VF/VT). The Amiodarone, Lidocaine or Placebo Study in Out-of-Hospital Cardiac Arrest (ALPS) was an RCT of amiodarone, lidocaine or placebo whose primary end-point was survival to hospital discharge. We sought to estimate the posterior probability of the absolute risk difference of neurologically intact survival (modified Rankin Score ≤ 3) from antiarrhythmic use (amiodarone or lidocaine) compared to placebo and from the use amiodarone versus lidocaine. Methods: We performed a Bayesian reanalysis on the per-protocol population of the ALPS trial in order to calculate the posterior probability of neurologically intact survival. We derived prior probabilities from the Amiodarone for Resuscitation after Out-of-Hospital Cardiac Arrest Due to Ventricular Fibrillation (ARREST) and Amiodarone Compared with Lidocaine for Shock-Resistant Ventricular Fibrillation (ALIVE) trials. We considered a clinically meaningful absolute difference to be ≥ 1%. Results: The posterior median probability of the absolute difference in neurologically intact survival between antiarrhythmic therapy and placebo was 2.2% (95% credible interval of -0.15% to 4.7%). There is a 96% chance that antiarrhythmic improves neurologic outcome and 86% chance of a clinically meaningful improvement. The posterior median probability of the absolute difference in neurologically intact survival between amiodarone and lidocaine was 1.5% (95% credible interval -1.6% to 4.5%). Conclusion: The results of this Bayesian analysis of the ALPS trial using likely optimistic prior probabilities derived from the ARREST trial may help inform clinicians of the value of antiarrhythmic therapy in OHCA.

Blood Type Does Not Affect Morbidity or Mortality in COVID-19 Infections, As Assessed By a Clinical Severity Scoring System

Introduction: Coronavirus disease (COVID-19), caused by the novel coronavirus Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), continues to lead to worldwide morbidity and mortality. This study aimed to determine if there was an association between blood type and clinical outcomes measured by a calculated morbidity score and mortality rates in patients infected with SARS-CoV-2 at our institution. The secondary aim was to investigate the association between patient characteristics (specifically age, gender, comorbid conditions, and race) and clinical outcomes and mortality in patients with confirmed SARS-COV-2 infection. Methods: Logistic regression was used to determine what factors were associated with death. A total morbidity score was constructed based on overall patient's COVID-19 clinical course. This score was modeled using Quasi-Poisson regression. Bayesian variable selection was used for the logistic regression to obtain a posterior probability that blood type is important in predicting worsened clinical outcomes and death. Results: Patients with blood type B were more likely to be African American, and patients with blood type AB were less likely to be male. Neither Blood type nor Rh+ status was a significant moderator of death or total morbidity score in regression analyses. Deviance based tests showed that blood type and Rh+ status could be omitted from each regression without a significant decrease in prediction accuracy. Bayesian variable selection showed that the posterior probability that any blood type related covariates were important in predicting death was .10. Increased age (aOR = 3.37, 95% CI = 2.44 - 4.67), male gender (aOR = 1.35, 95% CI = 1.08-1.69), and number of comorbid conditions (aOR = 1.28, 95% CI = 1.01-1.63) were the only covariates that were significantly associated with death. The only significant factors in predicting total morbidity score were age (aOR = 1.45; 95% CI = 1.349-1.555) and gender (aOR = 1.17; 95% CI = 1.109-1.243). Conclusion: In a large cohort of COVID-19 positive patients treated at a tertiary care hospital serving a low income population in New Orleans, there is strong evidence that blood type was not a significant predictor of clinical course or death in patients hospitalized with COVID 19. Older age and male gender led to worse clinical outcomes and higher rates of death; whereas older age, male gender, and comorbidities predicted a worse clinical course and higher morbidity score. Race was not a predictor of clinical course or death. Disclosures No relevant conflicts of interest to declare.

Markov Chain Monte Carlo in a Dynamical System of Information Theoretic Particles

In Bayesian learning, the posterior probability density of a model parameter is estimated from the likelihood function and the prior probability of the parameter. The posterior probability density estimate is refined as more evidence becomes available. However, any non-trivial Bayesian model requires the computation of an intractable integral to obtain the probability density function (PDF) of the evidence. Markov Chain Monte Carlo (MCMC) is a well-known algorithm that solves this problem by directly generating the samples of the posterior distribution without computing this intractable integral. We present a novel perspective of the MCMC algorithm which views the samples of a probability distribution as a dynamical system of Information Theoretic particles in an Information Theoretic field. As our algorithm probes this field with a test particle, it is subjected to Information Forces from other Information Theoretic particles in this field. We use Information Theoretic Learning (ITL) techniques based on Rényi’s α-Entropy function to derive an equation for the gradient of the Information Potential energy of the dynamical system of Information Theoretic particles. Using this equation, we compute the Hamiltonian of the dynamical system from the Information Potential energy and the kinetic energy. The Hamiltonian is used to generate the Markovian state trajectories of the system.