Interferon 1beta-1a ring prophylaxis to reduce household transmission of SARS-CoV-2

Introduction/Aim: Accumulating evidence indicates that an early, robust type 1 interferon (IFN) response to SARS-CoV-2 is critical for COVID-19 outcomes. Our objective was to examine the prophylactic potential of IFN treatment to limit viral transmission Methods: A cluster-randomised clinical trial was undertaken to determine effects of IFNbeta-1a treatment on SARS-CoV-2 household transmission (clinicaltrials.gov: NCT04552379). Index cases were identified from confirmed SARS-CoV-2 cases in Santiago, Chile, with 341 index cases and 831 household contacts enrolled. Households received 125 mug subcutaneous pegylated-IFNbeta-1a on days 1, 6, & 11 (172 households, 607 participants), or standard care (169 households, 565 participants). Primary outcomes included: (1) duration of viral shedding in infected cases (IC-INF), (2) transmission to treatment-eligible household contacts (EHC-ITT) at day 11. Result(s): Of 1172 individuals randomised, 53 individuals withdrew from the study (IFNbeta-1a = 35, SOC = 18). Eighty-two households (IFNbeta-1a = 36, SOC = 46) where the index case was SARS-CoV-2 negative on days 1 & 6, or with no SARS-CoV-2 negative contacts at recruitment, were excluded from exploratory analyses. Treatment with IFNbeta-1a: had no effect on duration of viral shedding in the IC-INF population (primary outcome 1), had no effect on transmission of SARS-CoV-2 at day 11 in the EHC-ITT population (primary outcome 2) but an effect was observed in a sensitivity analysis at day 6 (EHC-ITT: OR = 0.493, 95% CI = 0.256-0.949), reduced duration of hospitalisation in the IC-INF population and incidence of hospitalisation in per-protocol index cases (secondary outcome 3). In exploratory frequentist analysis, a significant effect of IFNbeta-1a treatment on SARS-CoV-2 transmission by day 11 (OR = 0.55, 95% CI = 0.36-0.99), and a Bayesian analysis identified a significant reduction in the odds of transmission (OR = -0.85, 95% credible interval = -1.59--0.16). Conclusion(s): Ring prophylaxis with IFNbeta-1a had no effect on duration of viral shedding but reduces the probability of SARS-CoV-2 transmission to uninfected, post-exposure contacts within a household.

Optimizing the Interdisciplinary Design of Adaptive Platform Trials

Adaptive platform trials (APTs) are often complex clinical trials that, ideally, are well suited to answer the motivating clinical questions effectively and efficiently, with the motivating clinical questions and associated treatment arms expected to evolve over time as evidence accumulates. Recently, APTs have played a pivotal role in informing public health policy by efficiently generating compelling evidence regarding the effectiveness of therapies for COVID-19. For APTs to be maximally effective in informing future public health policy, they must be carefully tailored to address the right clinical questions, with the right balance of size, scope, rigor, and flexibility. The design process requires input from clinical and statistical domain experts and often includes input from trial implementation personnel, ethicists, and patient representatives. The design process is inherently iterative, with proposed designs evaluated through trial simulation, the identification of strengths and weaknesses of the proposed design, and revision by the team to address weaknesses. This iterative design process requires effective communication and collaboration between the statistical and clinical domain experts. This session is intended to present a current best practice in facilitating and enhancing the collaborative design process for APTs, including how best to present simulation-based trial performance to the design team and ensure effective interdisciplinary communication. The speakers have extensive experience in leading the design of APTs across multiple therapeutic areas, in both academic and industry settings. The session will begin with a brief presentation by Dr. Lewis on the basic structure of an APT and the tasks and challenges associated with the multidisciplinary design process. The subsequent discussion will be organized by the following themes: (1) considerations in the selection of the study population and primary outcome metric;(2) selecting treatment domains and factors to be compared;(3) trial simulation and communication of performance metrics to both statistical and non-statistical team members;and (4) defining and calibrating interim decision rules. Each of the 4 panel members will outline a recommended approach to facilitating 1 of the 4 design tasks, with examples drawn from their experience. The remaining time (15 min) will be available for a panel question-and-answer period. At the end of the session, the audience will have an understanding of the general organization of, and a process for facilitating, the design process for an adaptive platform trial. Panel Members Roger J Lewis, MD, PhD, is a Senior Physician in the Los Angeles County Department of Health Services, Professor of Emergency Medicine at the David Geffen School of Medicine at UCLA, and the Senior Medical Scientist at Berry Consultants, LLC, a group that specializes in innovative clinical trial design. He is also the former Chair of the Department of Emergency Medicine at Harbor-UCLA Medical Center. Dr. Lewis' expertise centers on adaptive and Bayesian clinical trials, including platform trials;translational, clinical, health services and outcomes research methodology;data and safety monitoring boards, and the oversight of clinical trials. Dr. Lewis was elected to membership in the National Academy of Medicine in 2009. He has authored or coauthored over 270 original research publications, reviews, editorials, and chapters. Dr. Lewis is a Past President of the Society for Academic Emergency Medicine (SAEM) and served on the Board of Directors for the Society for Clinical Trials. He is a fellow of the American College of Emergency Physicians, the American Statistical Association, and the Society for Clinical Trials. Juliana Tolles, MD, MHS, is an Assistant Professor of Emergency Medicine at the Harbor-UCLA Medical Center and the David Geffen School of Medicine at UCLA, and a Medical and Statistical Scientist at Berry Consultants, LLC. Her academic research interests include emergency medical services, resuscitation medicine, and trau a care. She has authored several reviews for Journal of the American Medical Association (JAMA) on statistical methodology and has lectured nationally on research methodology for the Society for Academic Emergency Medicine Advanced Research Methodology Evaluation and Design (ARMED) course. She is also a co-investigator for the Strategies to Innovate Emergency Clinical Care Trials (SIREN) network Southern California site. Kert Viele, PhD, is a Director and Senior Statistical Scientist with Berry Consultants, where he leads Berry Consultants' research enterprise. He is a leader in clinical trial implementation of Bayesian hierarchical modeling, with expertise in platform and basket trials as well as clinical trials incorporating the use of historical information. Prior to joining Berry Consultants in 2010, he was a faculty member at the University of Kentucky, where he received the Provost's Award for Outstanding Teaching and was an investigator for NSF and NIH-funded research. He has developed over 100 custom Bayesian adaptive clinical trials for clients in industry, government, and academia, and currently serves on several data safety monitoring boards for randomized clinical trials. A former editor of the journal Bayesian Analysis, Dr. Viele is also an author of FACTS (Fixed and Adaptive Clinical Trial Simulator), clinical trial simulation software currently licensed to multiple pharmaceutical, academic, and government organizations. William Meurer, MD, MS, is an Associate Professor of Emergency Medicine and Neurology at the University of Michigan Health System. In addition, he serves as a Medical and Statistical Scientist for Berry Consultants, LLC. He works to improve the care of patients with acute neurological disease both through his work on the acute stroke team and as a researcher. His work in the field focuses on the design of clinical trials with adaptive and flexible components. In addition, he is a principal investigator of the National Institutes of Neurological Disorders and Stroke (NINDS) Clinical Trials Methodology Course (http:// neurotrials.training) and a co-investigator in the clinical coordinating center of the Strategies to Innovate Emergency Care Clinical Trials (SIREN) network- also funded by NIH). He was a co-investigator on the Adaptive Designs Accelerating Promising Treatments into Trials (ADAPT-IT) project, as part of the FDA Advancing Regulatory Science initiative with NIH.

[Early alert of COVID-19 outbreaks to plan the response in the IMSS]

The Instituto Mexicano del Seguro Social (IMSS) developed and implemented epidemic monitoring and modeling tools to support the organization and planning of an adequate and timely response to the COVID-19 health emergency. The aim of this article is to describe the methodology and results of the early outbreak detection tool called COVID-19 Alert. An early warning traffic light was developed that uses time series analysis and a Bayesian method of early detection of outbreaks from electronic records on COVID-19 for suspected cases, confirmed cases, disabilities, hospitalizations, and deaths. Through Alerta COVID-19, the beginning of the fifth wave of COVID-19 in the IMSS was detected in a timely manner, three weeks before the official declaration. The proposed method is aimed at generating early warnings before the start of a new wave of COVID-19, monitoring the serious phase of the epidemic, and supporting decision-making within the institution;unlike other tools that have an approach aimed at communicating risks to the community. We can conclude that the Alerta COVID-19 is an agile tool that incorporates robust methods for the early detection of outbreaks. Copyright © 2023 Revista Medica del Instituto Mexicano del Seguro Social.

Estimation of Lockdowns' Impact on Well-Being in Selected Countries: An Application of Novel Bayesian Methods and Google Search Queries Data.

Lockdowns introduced in connection with the COVID-19 pandemic have had a significant impact on societies from an economic, psychological, and health perspective. This paper presents estimations of their impact on well-being, understood both from the perspective of mental health and considering economic security and similar factors. This is not an easy task because well-being is influenced by numerous factors and the changes happen dynamically. Moreover, there are some obstacles when using the control group. However, other studies show that in certain cases it is possible to approximate selected phenomena with Google search queries data. Secondly, the econometric issues related to the suitable modeling of such a problem can be solved, for example, by using Bayesian methods. In particular, herein the recently gaining in popularity Bayesian structural time series and Bayesian dynamic mixture models are used. Indeed, these methods have not been used in social sciences extensively. However, in the fields where they have been used, they have been very efficient. Especially, they are useful when short time series are analyzed and when there are many variables that potentially have a significant explanatory impact on the response variable. Finally, 15 culturally different and geographically widely scattered countries are analyzed (i.e., Belgium, Brazil, Canada, Chile, Colombia, Denmark, France, Germany, Italy, Japan, Mexico, the Netherlands, Spain, Sweden, and the United Kingdom). Little evidence of any substantial changes in the Internet search intensity on terms connected with negative aspects of well-being and mental health issues is found. For example, in Mexico, some evidence of a decrease in well-being after lockdown was found. However, in Italy, there was weak evidence of an increase in well-being. Nevertheless, the Bayesian structural time series method has been found to fit the data most accurately. Indeed, it was found to be a superior method for causal analysis over the commonly used difference-in-differences method or Bayesian dynamic mixture models.

Outbreak Prediction of COVID-19 3rd Wave in Asia

Coronavirus is a pandemic for whole world and infected more than 200 countries of the world. Spreading of coronavirus started from china at the end of December and within three months, it infected whole world. Coronavirus is belonging to beta coronavirus family. Common symptoms of coronavirus are fever, dry cough, fatigue, and respiratory-related problem. This paper tries to study the infection rate of coronavirus in Asian countries, rest of world countries, and overall world countries. Asia is largest continent of the world which contains approximate 50 countries and highest contributes in GDP. Population of Asian countries 446.27 crore, that is 60 percentage of whole world population and covers 30 percent geographical area of world. China and India are the most populated countries in Asia. Total confirmed cases 2,056,051, recovered cases 502,045, and death cases 134,177 in the world. Scholar also divides the Asia continent into six regions such East, South, Central, North, Southeast and Western Asia for better understanding infection of coronavirus. This paper analyzes the confirmed cases, recovered cases, and death cases in Asia and understands the infection pattern date wise and countries wise. Machine learning algorithm is used for prediction infection in the world and also predicts future infection rate and death rate in Asian countries and world. Prophet is used for future prediction of confirmed and death cases in the Asian countries. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Validation of RT-qPCR test for SARS-CoV-2 in saliva specimens.

BACKGROUND: Saliva samples may be an easier, faster, safer, and cost-saving alternative to NPS samples, and can be self-collected by the patient. Whether SARS-CoV-2 RT-qPCR in saliva is more accurate than in nasopharyngeal swaps (NPS) is uncertain. We evaluated the accuracy of the RT-qPCR in both types of samples, assuming both approaches were imperfect. METHODS: We assessed the limit of detection (LoD) of RT-qPCR in each type of sample. We collected paired NPS and saliva samples and tested them using the Berlin Protocol to detect SARS-CoV-2 envelope protein (E). We used a Bayesian latent class analysis (BLCA) to estimate the sensitivity and specificity of each test, while accounting for their conditional dependence. RESULTS: The LoD were 10 copies/mL in saliva and 100 copies/mL in NPS. Paired samples of saliva and NPS were collected in 412 participants. Out of 68 infected cases, 14 were positive only in saliva. RT-qPCR sensitivity ranged from 82.7 % (95 % CrI: 54.8, 94.8) in NPS to 84.5 % (50.9, 96.5) in saliva. Corresponding specificities were 99.1 % (95 % CrI: 95.3, 99.8) and 98.4 %(95 % CrI: 92.8, 99.7). CONCLUSIONS: SARS-CoV-2 RT-qPCR test in saliva specimens has a similar or better accuracy than RT-qPCR test in NPS. Saliva specimens may be ideal for surveillance in general population, particularly in children, and in healthcare or other personnel in need of serial testing.

Prevalence assessment adjusted for laboratory test performance using an example of the COVID-19 serological tests

Assessment of the prevalence of the disease or condition should consider the accuracy of the diagnostic tests. In the context of the new coronavirus infection (COVID-19) pandemic, laboratory testing has been one of the most important components of the overall strategy for the control and prevention of this infection. Seroprevalence studies have been used to assess and monitor the level of population immunity to the virus. In this paper we provide detailed description of the methods to calculate and interpret the accuracy of laboratory tests as well as their sensitivity, specificity, positive-and negative prognostic values of laboratory tests using seroprevalence of COVID-19 studies as an example for better understanding of the methodological issues. The use of the laboratory tests accuracy in prevalence studies has been demonstrated. A sample syntax to calculate confidence intervals for the prevalence estimates using the bootstrap procedure with known absolute values of true positive and true negative results, false positive and false negative results for R software is also provided. Presentation of the prevalence estimates adjusted for test performance indicators with confidence intervals improves comparability of the findings obtained using different serological tests. The article is intended for undergraduate-, postgraduate-, and doctoral students in health sciences working with the assessment of the prevalence (seroprevalence) of diseases or conditions through population-based serological surveys. © 2022, Northern State Medical University. All rights reserved.

LIVING SYSTEMATIC REVIEWS (LSR) AND PROSPECTIVE META ANALYSIS (PMA): A CALL-OFDUTY FOR BAYESIAN ANALYSIS

Background The recent Covid-19 pandemic has accelerated the use of LSRs and PMAs, viewed as the 'next generation systematic reviews and meta-analyses'. LSRs and PMAs are prospective designs that can reduce the problems of traditional retrospective meta-analyses (MA) such as selective outcome reporting and publication bias, missing data, etc., and thus offer a better option for incorporating and generating new evidence. Objectives We propose the Bayesian approach as a method for analysing LSRs and PMAs. Bayesian Meta Analysis (BMA) is particularly appealing - actually, natural - for these designs as it clearly reflects the process of learning, defined as new evidence coming to update the previous knowledge, that is intrinsic to LSRs and PMAs. Methods Results pooled in the previous update of the LSR, or derived from the studies already known in the PMA, can be used to provide an objective/historical prior distribution. The combination of this information with the accumulated results (conditioning on these) provides the posterior probability distribution that can be used as the prior in the next iteration of the LSR/PMA (yesterday's posterior becomes tomorrow's prior). Results We will show an example of BMA on a LSR of the association between Covid-19 and asthmatic patients and give practical suggestions for its use. Discussion Without relying on asymptomatic normality assumptions, BMA is suitable as it is a coherent and flexible framework that, in comparison with frequentist MAs, allows a better assessment of the between-study variance and overcomes some common issues as dealing with missing data and publication bias.

OFFICE BASED TRANSPERINEAL LASER ABLATION FOR BENIGN PROSTATIC HYPERPLASIA: 3 & 6 MONTHS OUTCOMES

INTRODUCTION AND OBJECTIVE: BPH affects tens of millions of men across the world. Most procedures require either general or regional anesthesia or a transurethral approach. Herein, we present the 3 & 6 months results of NCT04760483 is a phase I prospective, single center, interventional pilot study evaluating transperineal laser ablation (TPLA) of BPH tissues, carried in Office setting under local anesthesia. A detailed step by step video depiction of this procedure is available at the AUA video library. The objectives call for safety, feasibility, and impact in pertinent outcomes measures, such as Uroflowmetry, IPSS, Hematuria, Erectile function, and ejaculation METHODS: The study contemplated accrual of 20 men between 50 and 80 years with prostate volumes between 30 and 120 cc, IPSS scores >9, peak flows between 5 and 15 cc/s and void residuals under <250 ml. Any patient neurological conditions, history of any surgical intervention or urinary retention were excluded. IPSS assessments, Flow studies and prostate volume measures were conducted at 3 months. Herein we present the results. Bayesian analysis for continuous measurements were performed and non-parametric differences were evaluated using chi2 tests. RESULTS: Patients enrolled between December 2020 and February of 2021. The median (IQR) for age and BMI was 68 (58,73) and 29 (27,31), respectively. These parameters for room time, ablation time, watts and total joules were 29 (23,32), 9 minutes (7,12), 6 (5,7) watts and 3,400 (2,600, 3600) joules, respectively. 8(40%) were discharged with a Foley due to elevated residuals. 16 patients had erections and ejaculations before and 3 months after TPLA. 17/20 (85%) had significant improvement in their urinary profile after TPLA (See TABLE for details). One of the initial responders suffered from COVID- 19 infection and developed a CVA that hindered his urinary function. CONCLUSIONS: TPLA in the office setting is feasible and safe. Three month outcomes showed subjective and objective sustained improvement in over 80% of patients for at least 6 months. Furthermore, erections or ejaculations were not affected. This novel and promising approach demands further evaluation in phase II-III trials. (Figure Presented).

Different clinical trial analyses for different stakeholders

The predominant reporting of clinical trial results today is what we might call the ''regulator's analysis.'' This analysis meets requirements that regulators have set for their work. Shouldn't there also be an analysis for other stakeholders, for example, physician's analysis, a thirdparty payor's analysis, a patient's analysis, and other analyses? Different analyses for different stakeholders appearing in different journals would allow Bayesian analyses, simulation results, effectiveness data, realworld evidence, more sophisticated safety analyses, and so on to also be published. The need for analyses for different stakeholders has been heightened by the various stakeholders involved in the analysis of COVID-19 trials and the current attention to estimands and concern about the proportional hazards assumption in survival analysis. This session will present the case that ''persuasive evidence'' is defined differently by different clinical trial stakeholders and provide some examples of innovative clinical trials analyses. Chair and Org: Greg Ball, Merck [greg.ball@merck .com] Title: Is There Only One Analysis? Jay Herson, Johns Hopkins Bloomberg School of Public Health [jay.herson@ earthlink.net]. Summary: The main output of clinical trials is to yield persuasive evidence. However, our notion of persuasive evidence has changed over time. The intent-to-treat frequentist analysis preferred by regulatory agencies may no longer be considered persuasive to other stakeholders. This talk will motivate the need for and provide examples of additional analyses that are useful for other stakeholders such as physicians, patients, and insurers. Title: Safety Analysis: A Physician's Perspective Barbara Hendrickson, AbbVie [barbara.hendrickson@ abbvie.com] Summary: Clinical trial safety data presentations are typically confined to rates of different categories of adverse events (e.g. common, fatal, serious, and those leading to discontinuation). However, other types of questions are important to healthcare providers and patients. Specifically, information about time to onset, reversibility with and without intervention, and additional measures of severity by functional impairment or treatments required is frequently asked questions. Also, more meaningful risk factor analyses as well as information about the likelihood for key adverse reactions in different patient subpopulations (e.g. by age and renal impairment) are needed. Title: Efficacy Analysis: Beyond the Tyranny of the p-value Michael Gaffney, Pfizer [michael.gaffney@pfizer. com]. Summary: Clinical trials are usually large, expensive and not repeated. The adherence to statistical rules often impedes the interpretation and dissemination of the strength of clinical trial results. These rules serve the legitimate objectives of regulators and medical journal editors. However, the emergence of personalized and targeted therapy;the relatively new concept of estimands;the increasing use of adaptive designs;and real-world evidence all provide an opportunity for a new paradigm which should lead to a fuller and integrated understanding of clinical trial results. This new paradigm requires reducing the role of hypothesis testing/ decision-making and increasing the role of estimation and uncertainty. Discussant: Elizabeth Garrett-Mayer, American Society of Clinical Oncology [Liz.Garrett-Mayer@asco.org].

Epidemiological Identification of A Novel Pathogen in Real Time: Analysis of the Atypical Pneumonia Outbreak in Wuhan, China, 2019-2020.

Virological tests have now shown conclusively that a novel coronavirus is causing the 2019-2020 atypical pneumonia outbreak in Wuhan, China. We demonstrate that non-virological descriptive characteristics could have determined that the outbreak is caused by a novel pathogen in advance of virological testing. Characteristics of the ongoing outbreak were collected in real time from two medical social media sites. These were compared against characteristics of eleven pathogens that have previously caused cases of atypical pneumonia. The probability that the current outbreak is due to "Disease X" (i.e., previously unknown etiology) as opposed to one of the known pathogens was inferred, and this estimate was updated as the outbreak continued. The probability (expressed as a percentage) that Disease X is driving the outbreak was assessed as over 29% on 31 December 2019, one week before virus identification. After some specific pathogens were ruled out by laboratory tests on 5 January 2020, the inferred probability of Disease X was over 49%. We showed quantitatively that the emerging outbreak of atypical pneumonia cases is consistent with causation by a novel pathogen. The proposed approach, which uses only routinely observed non-virological data, can aid ongoing risk assessments in advance of virological test results becoming available.