Clinical Trial Data Sharing for COVID-19-Related Research

Now Is the Time to Strengthen Government-Academic Data Infrastructures to Jump-Start Future Public Health Crisis Response

During public health crises, the significance of rapid data sharing cannot be overstated. In attempts to accelerate COVID-19 pandemic responses, discussions within society and scholarly research have focused on data sharing among health care providers, across government departments at different levels, and on an international scale. A lesser-addressed yet equally important approach to sharing data during the COVID-19 pandemic and other crises involves cross-sector collaboration between government entities and academic researchers. Specifically, this refers to dedicated projects in which a government entity shares public health data with an academic research team for data analysis to receive data insights to inform policy. In this viewpoint, we identify and outline documented data sharing challenges in the context of COVID-19 and other public health crises, as well as broader crisis scenarios encompassing natural disasters and humanitarian emergencies. We then argue that government-academic data collaborations have the potential to alleviate these challenges, which should place them at the forefront of future research attention. In particular, for researchers, data collaborations with government entities should be considered part of the social infrastructure that bolsters their research efforts toward public health crisis response. Looking ahead, we propose a shift from ad hoc, intermittent collaborations to cultivating robust and enduring partnerships. Thus, we need to move beyond viewing government-academic data interactions as 1-time sharing events. Additionally, given the scarcity of scholarly exploration in this domain, we advocate for further investigation into the real-world practices and experiences related to sharing data from government sources with researchers during public health crises.

A Scalable Pseudonymization Tool for Rapid Deployment in Large Biomedical Research Networks: Development and Evaluation Study

Background

The SARS-CoV-2 pandemic has demonstrated once again that rapid collaborative research is essential for the future of biomedicine. Large research networks are needed to collect, share, and reuse data and biosamples to generate collaborative evidence. However, setting up such networks is often complex and time-consuming, as common tools and policies are needed to ensure interoperability and the required flows of data and samples, especially for handling personal data and the associated data protection issues. In biomedical research, pseudonymization detaches directly identifying details from biomedical data and biosamples and connects them using secure identifiers, the so-called pseudonyms. This protects privacy by design but allows the necessary linkage and reidentification.

Objective

Although pseudonymization is used in almost every biomedical study, there are currently no pseudonymization tools that can be rapidly deployed across many institutions. Moreover, using centralized services is often not possible, for example, when data are reused and consent for this type of data processing is lacking. We present the ORCHESTRA Pseudonymization Tool (OPT), developed under the umbrella of the ORCHESTRA consortium, which faced exactly these challenges when it came to rapidly establishing a large-scale research network in the context of the rapid pandemic response in Europe.

Methods

To overcome challenges caused by the heterogeneity of IT infrastructures across institutions, the OPT was developed based on programmable runtime environments available at practically every institution: office suites. The software is highly configurable and provides many features, from subject and biosample registration to record linkage and the printing of machine-readable codes for labeling biosample tubes. Special care has been taken to ensure that the algorithms implemented are efficient so that the OPT can be used to pseudonymize large data sets, which we demonstrate through a comprehensive evaluation.

Results

The OPT is available for Microsoft Office and LibreOffice, so it can be deployed on Windows, Linux, and MacOS. It provides multiuser support and is configurable to meet the needs of different types of research projects. Within the ORCHESTRA research network, the OPT has been successfully deployed at 13 institutions in 11 countries in Europe and beyond. As of June 2023, the software manages data about more than 30,000 subjects and 15,000 biosamples. Over 10,000 labels have been printed. The results of our experimental evaluation show that the OPT offers practical response times for all major functionalities, pseudonymizing 100,000 subjects in 10 seconds using Microsoft Excel and in 54 seconds using LibreOffice.

Conclusions

Innovative solutions are needed to make the process of establishing large research networks more efficient. The OPT, which leverages the runtime environment of common office suites, can be used to rapidly deploy pseudonymization and biosample management capabilities across research networks. The tool is highly configurable and available as open-source software.

ISO/TS 21564:2019- based Evaluation of a Semantic Map between Variables in the ISARIC Freestanding Follow Up Survey and ORCHESTRA Studies

The COVID-19 pandemic has led to tremendous investment in clinical studies to generate much-needed knowledge on the prevention, diagnosis, treatment and long-term effects of the disease. Case report forms, comprised of questions and answers (variables), are commonly used to collect data in clinical trials. Maximizing the value of study data depends on data quality and on the ability to easily pool and share data from several sources. ISARIC, in collaboration with the WHO, has created a case report form that is available for use by the scientific community to collect COVID-19 trial data. One of such research initiatives collecting and analyzing multi-country and multi-cohort COVID-19 study data is the Horizon 2020 project ORCHESTRA. Following the ISO/TS 21564:2019 standard, a mapping between five ORCHESTRA studies' variables and the ISARIC Freestanding Follow-Up Survey elements was created. Measures of correspondence of shared semantic domain of 0 (perfect match), 1 (fully inclusive match), 2 (partial match), 4 (transformation required) or 4* (not present in ORCHESTRA) as compared to the target code system, ORCHESTRA study variables, were assigned to each of the elements in the ISARIC FUP case report form (CRF) which was considered the source code system. Of the ISARIC FUP CRF's variables, around 34% were found to show an exact match with corresponding variables in ORCHESTRA studies and about 33% showed a non-inclusive overlap. Matching variables provided information on patient demographics, COVID-19 testing, hospital admission and symptoms. More in-depth details are covered in ORCHESTRA variables with regards to treatment and comorbidities. ORCHESTRA's Long-Term Sequelae and Fragile population studies' CRFs include 32 and 27 variables respectively which were evaluated as a perfect match to variables in the ISARIC FUP CRF. Our study serves as an example of the kind of maps between case report form variables from different research projects needed to link ongoing COVID-19 research efforts and facilitate collaboration and data sharing. To enable data aggregation across two data systems, the information they contain needs to be connected through a map to determine compatibility and transformation needs. Combining data from various clinical studies can increase the power of analytical insights.

Industry Involvement and Transparency in the Most Cited Clinical Trials, 2019-2022

Importance

Industry involvement is prominent in influential clinical trials, and commitments to transparency of trials are highly variable.

Objective

To evaluate the modes of industry involvement and the transparency features of the most cited recent clinical trials across medicine.

Design, Setting, and Participants

This cross-sectional study was a meta-research assessment including randomized and nonrandomized clinical trials published in 2019 or later. The 600 trials of any type of disease or setting that attracted highest number of citations in Scopus as of December 2022 were selected for analysis. Data were analyzed from March to September 2023.

Main Outcomes and Measures

Outcomes of interest were industry involvement (sponsor, author, and analyst) and transparency (protocols, statistical analysis plans, and data and code availability).

Results

Among 600 trials with a median (IQR) sample size of 415 (124-1046) participants assessed, 409 (68.2%) had industry funding and 303 (50.5%) were exclusively industry-funded. A total of 354 trials (59.0%) had industry authors, with 280 trials (46.6%) involving industry analysts and 125 trials (20.8%) analyzed exclusively by industry analysts. Among industry-funded trials, 364 (89.0%) reached conclusions favoring the sponsor. Most trials (478 trials [79.7%]) provided a data availability statement, and most indicated intention to share the data, but only 16 trials (2.7%) had data already readily available to others. More than three-quarters of trials had full protocols (482 trials [82.0%]) or statistical analysis plans (446 trials [74.3%]) available, but only 27 trials (4.5%) explicitly mentioned sharing analysis code (8 readily available; 19 on request). Randomized trials were more likely than nonrandomized studies to involve only industry analysts (107 trials [22.9%] vs 18 trials [13.6%]; P = .02) and to have full protocols (405 studies [86.5%] vs 87 studies [65.9%]; P < .001) and statistical analysis plans (373 studies [79.7%] vs 73 studies [55.3%]; P < .001) available. Almost all nonrandomized industry-funded studies (90 of 92 studies [97.8%]) favored the sponsor. Among industry-funded trials, exclusive industry funding (odds ratio, 2.9; 95% CI, 1.5-5.4) and industry-affiliated authors (odds ratio, 2.9; 95% CI, 1.5-5.6) were associated with favorable conclusions for the sponsor.

Conclusions and Relevance

This cross-sectional study illustrates how industry involvement in the most influential clinical trials was prominent not only for funding, but also authorship and provision of analysts and was associated with conclusions favoring the sponsor. While most influential trials reported that they planned to share data and make both protocols and statistical analysis plans available, raw data and code were rarely readily available.

Totality of evidence of the effectiveness of repurposed therapies for COVID-19: Can we use real-world studies alongside randomized controlled trials?

Rapid and robust strategies to evaluate the efficacy and effectiveness of novel and existing pharmacotherapeutic interventions (repurposed treatments) in future pandemics are required. Observational "real-world studies" (RWS) can report more quickly than randomized controlled trials (RCTs) and would have value were they to yield reliable results. Both RCTs and RWS were deployed during the coronavirus disease 2019 (COVID-19) pandemic. Comparing results between them offers a unique opportunity to determine the potential value and contribution of each. A learning review of these parallel evidence channels in COVID-19, based on quantitative modeling, can help improve speed and reliability in the evaluation of repurposed therapeutics in a future pandemic. Analysis of all-cause mortality data from 249 observational RWS and RCTs across eight treatment regimens for COVID-19 showed that RWS yield more heterogeneous results, and generally overestimate the effect size subsequently seen in RCTs. This is explained in part by a few study factors: the presence of RWS that are imbalanced for age, gender, and disease severity, and those reporting mortality at 2 weeks or less. Smaller studies of either type contributed negligibly. Analysis of evidence generated sequentially during the pandemic indicated that larger RCTs drive our ability to make conclusive decisions regarding clinical benefit of each treatment, with limited inference drawn from RWS. These results suggest that when evaluating therapies in future pandemics, (1) large RCTs, especially platform studies, be deployed early; (2) any RWS should be large and should have adequate matching of known confounders and long follow-up; (3) reporting standards and data standards for primary endpoints, explanatory factors, and key subgroups should be improved; in addition, (4) appropriate incentives should be in place to enable access to patient-level data; and (5) an overall aggregate view of all available results should be available at any given time.

Global Trend on Machine Learning in Helicobacter within One Decade: A Scientometric Study

Purpose

This study aims to create a science map, provide structural analysis, investigate evolution, and identify new trends in Helicobacter pylori (H. pylori) research articles.

Methods

All Helicobacter publications were gathered from the Web of Science (WoS) database from August 2010 to 2021. The data were required for bibliometric analysis. The bibliometric analysis was performed with Bibliometrix R Tool. Bibliometric data were analyzed using the Bibliometrix Biblioshiny R-package software.

Results

A total of 17,413 articles were reviewed and analyzed, with descriptive characteristics of the H. pylori literature included. In journals, 21,102 keywords plus and 20,490 author keywords were reported. These articles were also written by 56,106 different authors, with 262 being single-author articles. Most authors' abstracts, titles, and keywords included "Helicobacter-pylori." Since 2010, the total number of H. pylori-related publications has been decreasing. Gut, PLOS ONE, and Gastroenterology are the most influential H. pylori journals, according to source impact. China, the United States, and Japan are the countries with most affiliations and subjects. In addition, Seoul National University has published the most articles about H. pylori. According to the cloud word plot, the authors' most frequently used keywords are gastric cancer (GC), H. pylori, gastritis, eradication, and inflammation. "Helicobacter pylori" and "infection" have the steepest slopes in terms of the upward trend of words used in articles from 2010 to 2021. Subjects such as GC, intestinal metaplasia, epidemiology, peptic ulcer, eradication, and clarithromycin are included in the diagram's motor theme section, according to strategic diagrams. According to the thematic evolution map, topics such as Helicobacter pylori infection, B-cell lymphoma, CagA, Helicobacter pylori, and infection were largely discussed between 2010 and 2015. From 2016 to 2021, the top topics covered included Helicobacter pylori, H. pylori infection, and infection.

Data capture and sharing in the COVID-19 pandemic: a cause for concern

Routine health care and research have been profoundly influenced by digital-health technologies. These technologies range from primary data collection in electronic health records (EHRs) and administrative claims to web-based artificial-intelligence-driven analyses. There has been increased use of such health technologies during the COVID-19 pandemic, driven in part by the availability of these data. In some cases, this has resulted in profound and potentially long-lasting positive effects on medical research and routine health-care delivery. In other cases, high profile shortcomings have been evident, potentially attenuating the effect of-or representing a decreased appetite for-digital-health transformation. In this Series paper, we provide an overview of how facets of health technologies in routinely collected medical data (including EHRs and digital data sharing) have been used for COVID-19 research and tracking, and how these technologies might influence future pandemics and health-care research. We explore the strengths and weaknesses of digital-health research during the COVID-19 pandemic and discuss how learnings from COVID-19 might translate into new approaches in a post-pandemic era.

Investigating Linkages Between Spatiotemporal Patterns of the COVID-19 Delta Variant and Public Health Interventions in Southeast Asia: Prospective Space-Time Scan Statistical Analysis Method

Background

The COVID-19 Delta variant has presented an unprecedented challenge to countries in Southeast Asia (SEA). Its transmission has shown spatial heterogeneity in SEA after countries have adopted different public health interventions during the process. Hence, it is crucial for public health authorities to discover potential linkages between epidemic progression and corresponding interventions such that collective and coordinated control measurements can be designed to increase their effectiveness at reducing transmission in SEA.

Objective

The purpose of this study is to explore potential linkages between the spatiotemporal progression of the COVID-19 Delta variant and nonpharmaceutical intervention (NPI) measures in SEA. We detected the space-time clusters of outbreaks of COVID-19 and analyzed how the NPI measures relate to the propagation of COVID-19.

Methods

We collected district-level daily new cases of COVID-19 from June 1 to October 31, 2021, and district-level population data in SEA. We adopted prospective space-time scan statistics to identify the space-time clusters. Using cumulative prospective space-time scan statistics, we further identified variations of relative risk (RR) across each district at a half-month interval and their potential public health intervention linkages.

Results

We found 7 high-risk clusters (clusters 1-7) of COVID-19 transmission in Malaysia, the Philippines, Thailand, Vietnam, and Indonesia between June and August, 2021, with an RR of 5.45 (P<.001), 3.50 (P<.001), 2.30 (P<.001), 1.36 (P<.001), 5.62 (P<.001), 2.38 (P<.001), 3.45 (P<.001), respectively. There were 34 provinces in Indonesia that have successfully mitigated the risk of COVID-19, with a decreasing range between –0.05 and –1.46 due to the assistance of continuous restrictions. However, 58.6% of districts in Malaysia, Singapore, Thailand, and the Philippines saw an increase in the infection risk, which is aligned with their loosened restrictions. Continuous strict interventions were effective in mitigating COVID-19, while relaxing restrictions may exacerbate the propagation risk of this epidemic.

Conclusions

The analyses of space-time clusters and RRs of districts benefit public health authorities with continuous surveillance of COVID-19 dynamics using real-time data. International coordination with more synchronized interventions amidst all SEA countries may play a key role in mitigating the progression of COVID-19.

Smartphone-Enabled versus Conventional Otoscopy in Detecting Middle Ear Disease: A Meta-Analysis

Traditional otoscopy has some limitations, including poor visualization and inadequate time for evaluation in suboptimal environments. Smartphone-enabled otoscopy may improve examination quality and serve as a potential diagnostic tool for middle ear diseases using a telemedicine approach. The main objectives are to compare the correctness of smartphone-enabled otoscopy and traditional otoscopy and to evaluate the diagnostic confidence of the examiner via meta-analysis. From inception through 20 January 2022, the Cochrane Library, PubMed, EMBASE, Web of Science, and Scopus databases were searched. Studies comparing smartphone-enabled otoscopy with traditional otoscopy regarding the outcome of interest were eligible. The relative risk (RR) for the rate of correctness in diagnosing ear conditions and the standardized mean difference (SMD) in diagnostic confidence were extracted. Sensitivity analysis and trial sequential analyses (TSAs) were conducted to further examine the pooled results. Study quality was evaluated by using the revised Cochrane risk of bias tool 2. Consequently, a total of 1840 examinees were divided into the smartphone-enabled otoscopy group and the traditional otoscopy group. Overall, the pooled result showed that smartphone-enabled otoscopy was associated with higher correctness than traditional otoscopy (RR, 1.26; 95% CI, 1.06 to 1.51; p = 0.01; I² = 70.0%). Consistently significant associations were also observed in the analysis after excluding the simulation study (RR, 1.10; 95% CI, 1.00 to 1.21; p = 0.04; I² = 0%) and normal ear conditions (RR, 1.18; 95% CI, 1.01 to 1.40; p = 0.04; I² = 65.0%). For the confidence of examiners using both otoscopy methods, the pooled result was nonsignificant between the smartphone-enabled otoscopy and traditional otoscopy groups (SMD, 0.08; 95% CI, -0.24 to 0.40; p = 0.61; I² = 16.3%). In conclusion, smartphone-enabled otoscopy was associated with a higher rate of correctness in the detection of middle ear diseases, and in patients with otologic complaints, the use of smartphone-enabled otoscopy may be considered. More large-scale studies should be performed to consolidate the results.

Data flow within global clinical trials: a scoping review

Objective

To document clinical trial data flow in global clinical trials published in major journals between 2013 and 2021 from Global South to Global North.

Design

Scoping analysis

Methods

We performed a search in Cochrane Central Register of Controlled Trials (CENTRAL) to retrieve randomised clinical trials published between 2013 and 2021 from The BMJ, BMJ Global Health, the Journal of the American Medical Association, the Lancet, Lancet Global Health and the New England Journal of Medicine. Studies were included if they involved recruitment and author affiliation across different country income groupings using World Bank definitions. The direction of data flow was extracted with a data collection tool using sites of trial recruitment as the starting point and the location of authors conducting statistical analysis as the ending point.

Results

Of 1993 records initially retrieved, 517 studies underwent abstract screening, 348 studies underwent full-text screening and 305 studies were included. Funders from high-income countries were the sole funders of the majority (82%) of clinical trials that recruited across income groupings. In 224 (73.4%) of all assessable studies, data flowed exclusively to authors affiliated with high-income countries or to a majority of authors affiliated with high-income countries for statistical analysis. Only six (3.2%) studies demonstrated data flow to lower middle-income countries and upper middle-income countries for analysis, with only one with data flow to a lower middle-income country.

Conclusions

Global clinical trial data flow demonstrates a Global South to Global North trajectory. Policies should be re-examined to assess how data sharing across country income groupings can move towards a more equitable model.

Transparency of COVID-19-Related Research in Dental Journals

Objective

We aimed to assess the adherence to transparency practices (data availability, code availability, statements of protocol registration and conflicts of interest and funding disclosures) and FAIRness (Findable, Accessible, Interoperable, and Reusable) of shared data from open access COVID-19-related articles published in dental journals available from the Europe PubMed Central (PMC) database.

Methods

We searched and exported all COVID-19-related open-access articles from PubMed-indexed dental journals available in the Europe PMC database in 2020 and 2021. We detected transparency indicators with a validated and automated tool developed to extract the indicators from the downloaded articles. Basic journal- and article-related information was retrieved from the PMC database. Then, from those which had shared data, we assessed their accordance with FAIR data principles using the F-UJI online tool (f-uji.net).

Results

Of 650 available articles published in 59 dental journals, 74% provided conflicts of interest disclosure and 40% funding disclosure and 4% were preregistered. One study shared raw data (0.15%) and no study shared code. Transparent practices were more common in articles published in journals with higher impact factors, and in 2020 than in 2021. Adherence to the FAIR principles in the only paper that shared data was moderate.

Conclusion

While the majority of the papers had a COI disclosure, the prevalence of the other transparency practices was far from the acceptable level. A much stronger commitment to open science practices, particularly to preregistration, data and code sharing, is needed from all stakeholders.

A Comprehensive COVID-19 Daily News and Medical Literature Briefing to Inform Health Care and Policy in New Mexico: Implementation Study

BACKGROUND

On March 11, 2020, the New Mexico Governor declared a public health emergency in response to the COVID-19 pandemic. The New Mexico medical advisory team contacted University of New Mexico (UNM) faculty to form a team to consolidate growing information on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its disease to facilitate New Mexico's pandemic management. Thus, faculty, physicians, staff, graduate students, and medical students created the "UNM Global Health COVID-19 Intelligence Briefing."

OBJECTIVE

In this paper, we sought to (1) share how to create an informative briefing to guide public policy and medical practice and manage information overload with rapidly evolving scientific evidence; (2) determine the qualitative usefulness of the briefing to its readers; and (3) determine the qualitative effect this project has had on virtual medical education.

METHODS

Microsoft Teams was used for manual and automated capture of COVID-19 articles and composition of briefings. Multilevel triaging saved impactful articles to be reviewed, and priority was placed on randomized controlled studies, meta-analyses, systematic reviews, practice guidelines, and information on health care and policy response to COVID-19. The finalized briefing was disseminated by email, a listserv, and posted on the UNM digital repository. A survey was sent to readers to determine briefing usefulness and whether it led to policy or medical practice changes. Medical students, unable to partake in direct patient care, proposed to the School of Medicine that involvement in the briefing should count as course credit, which was approved. The maintenance of medical student involvement in the briefings as well as this publication was led by medical students.

RESULTS

An average of 456 articles were assessed daily. The briefings reached approximately 1000 people by email and listserv directly, with an unknown amount of forwarding. Digital repository tracking showed 5047 downloads across 116 countries as of July 5, 2020. The survey found 108 (95%) of 114 participants gained relevant knowledge, 90 (79%) believed it decreased misinformation, 27 (24%) used the briefing as their primary source of information, and 90 (79%) forwarded it to colleagues. Specific and impactful public policy decisions were informed based on the briefing. Medical students reported that the project allowed them to improve on their scientific literature assessment, stay current on the pandemic, and serve their community.

CONCLUSIONS

The COVID-19 briefings succeeded in informing and guiding New Mexico policy and clinical practice. The project received positive feedback from the community and was shown to decrease information burden and misinformation. The virtual platforms allowed for the continuation of medical education. Variability in subject matter expertise was addressed with training, standardized article selection criteria, and collaborative editing led by faculty.

Barreras y facilitadores para la investigación en ciencias de la salud durante la crisis del COVID-19: una revisión de alcance

Objetivo:

La pandemia ocasionada por el Covid-19 ha significado un gran desafío para la investigación en salud por la necesidad de dar una respuesta oportuna y efectiva a esta situación de crisis. Es importante proveer una visión panorámica sobre las principales barreras y facilitadores encontrados en la conducción de estudios en ciencias de la salud durante la crisis del Covid-19, así como también de las iniciativas en investigación sugeridas por autoridades en salud de investigación a nivel global, regional o local.

Materiales y métodos:

Se desarrolló una revisión sistemática de alcance. Se hizo una búsqueda de la literatura en Medline, Cochrane library, Lilacs y Google Scholar. Se incluyeron estudios de investigación originales, artículos de revisión, de opinión y editoriales disponibles en texto completo, publicados entre enero de 2020 y mayo de 2021 en español, inglés o portugués. Se hizo selección de los documentos y extracción de los datos por dos autores de manera independiente. Las barreras y facilitadores identificados fueron descritos y organizados en cuatro categorías a partir de la literatura: socioculturales, administrativos, organizacionales y metodológicos. Asimismo, se incluyeron documentos y comunicados oficiales de autoridades en salud e investigación a nivel global, regional y local. Los resultados se presentan de manera narrativa y en tablas.

Resultados:

Se seleccionaron 26 documentos para el análisis y síntesis de la información. Las barreras mencionadas más frecuentemente en la literatura incluyen las dificultades en cuanto al acceso a los participantes, a los trámites asociados a los comités de ética; así como el riesgo biológico para los investigadores y la falta de coordinación inter e intrainstitucional. Por su parte, los facilitadores identificados incluyen la adopción de soluciones virtuales, el trabajo cooperativo entre los actores de la investigación y la flexibilidad en el proceso de obtención del consentimiento informado. Frente a las iniciativas difundidas por las autoridades en salud e investigación, se identificaron cuatro estrategias relacionadas con la priorización de preguntas de investigación, el fomento de la cooperación y la inclusión en la investigación, la lucha contra la infodemia y el fortalecimiento de la calidad metodológica de los estudios.

Conclusiones:

Para la investigación en el contexto de la pandemia representa un desafío continuar con la cooperación e interoperabilidad entre las instituciones, los países y las disciplinas, con el fin de facilitar los procesos investigativos en el futuro; del mismo modo, cobra importancia mantener la ciencia abierta y la financiación de estudios cooperativos cuando surjan otras prioridades. Asimismo, es evidente la necesidad de desarrollar y sostener mecanismos que gestionen la información de manera eficiente para la toma de decisiones. Se requiere evaluar de manera continua los efectos que ha dejado esta pandemia en la práctica de la investigación en ciencias de la salud para comprender de manera integral lo que debemos aprender como sociedad a partir de las crisis.

Identification and Development of Therapeutics for COVID-19

After emerging in China in late 2019, the novel coronavirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spread worldwide, and as of mid-2021, it remains a significant threat globally. Only a few coronaviruses are known to infect humans, and only two cause infections similar in severity to SARS-CoV-2: Severe acute respiratory syndrome-related coronavirus, a species closely related to SARS-CoV-2 that emerged in 2002, and Middle East respiratory syndrome-related coronavirus, which emerged in 2012. Unlike the current pandemic, previous epidemics were controlled rapidly through public health measures, but the body of research investigating severe acute respiratory syndrome and Middle East respiratory syndrome has proven valuable for identifying approaches to treating and preventing novel coronavirus disease 2019 (COVID-19). Building on this research, the medical and scientific communities have responded rapidly to the COVID-19 crisis and identified many candidate therapeutics. The approaches used to identify candidates fall into four main categories: adaptation of clinical approaches to diseases with related pathologies, adaptation based on virological properties, adaptation based on host response, and data-driven identification (ID) of candidates based on physical properties or on pharmacological compendia. To date, a small number of therapeutics have already been authorized by regulatory agencies such as the Food and Drug Administration (FDA), while most remain under investigation. The scale of the COVID-19 crisis offers a rare opportunity to collect data on the effects of candidate therapeutics. This information provides insight not only into the management of coronavirus diseases but also into the relative success of different approaches to identifying candidate therapeutics against an emerging disease. IMPORTANCE The COVID-19 pandemic is a rapidly evolving crisis. With the worldwide scientific community shifting focus onto the SARS-CoV-2 virus and COVID-19, a large number of possible pharmaceutical approaches for treatment and prevention have been proposed. What was known about each of these potential interventions evolved rapidly throughout 2020 and 2021. This fast-paced area of research provides important insight into how the ongoing pandemic can be managed and also demonstrates the power of interdisciplinary collaboration to rapidly understand a virus and match its characteristics with existing or novel pharmaceuticals. As illustrated by the continued threat of viral epidemics during the current millennium, a rapid and strategic response to emerging viral threats can save lives. In this review, we explore how different modes of identifying candidate therapeutics have borne out during COVID-19.

Data Collection during Public Health Emergencies: Design Tenets and Usability of an Electronic Data Capture Tool (Preprint)

Background

The Discovery Critical Care Research Network Program for Resilience and Emergency Preparedness (Discovery PREP) partnered with a third-party technology vendor to design and implement an electronic data capture tool that addressed multisite data collection challenges during public health emergencies (PHE) in the United States. The basis of the work was to design an electronic data capture tool and to prospectively gather data on usability from bedside clinicians during national health system stress queries and influenza observational studies.

Objective

The aim of this paper is to describe the lessons learned in the design and implementation of a novel electronic data capture tool with the goal of significantly increasing the nation’s capability to manage real-time data collection and analysis during PHE.

Methods

A multiyear and multiphase design approach was taken to create an electronic data capture tool, which was used to pilot rapid data capture during a simulated PHE. Following the pilot, the study team retrospectively assessed the feasibility of automating the data captured by the electronic data capture tool directly from the electronic health record. In addition to user feedback during semistructured interviews, the System Usability Scale (SUS) questionnaire was used as a basis to evaluate the usability and performance of the electronic data capture tool.

Results

Participants included Discovery PREP physicians, their local administrators, and data collectors from tertiary-level academic medical centers at 5 different institutions. User feedback indicated that the designed system had an intuitive user interface and could be used to automate study communication tasks making for more efficient management of multisite studies. SUS questionnaire results classified the system as highly usable (SUS score 82.5/100). Automation of 17 (61%) of the 28 variables in the influenza observational study was deemed feasible during the exploration of automated versus manual data abstraction. The creation and use of the Project Meridian electronic data capture tool identified 6 key design requirements for multisite data collection, including the need for the following: (1) scalability irrespective of the type of participant; (2) a common data set across sites; (3) automated back end administrative capability (eg, reminders and a self-service status board); (4) multimedia communication pathways (eg, email and SMS text messaging); (5) interoperability and integration with local site information technology infrastructure; and (6) natural language processing to extract nondiscrete data elements.

Conclusions

The use of the electronic data capture tool in multiple multisite Discovery PREP clinical studies proved the feasibility of using the novel, cloud-based platform in practice. The lessons learned from this effort can be used to inform the improvement of ongoing global multisite data collection efforts during the COVID-19 pandemic and transform current manual data abstraction approaches into reliable, real time, and automated information exchange. Future research is needed to expand the ability to perform automated multisite data extraction during a PHE and beyond.

Implementation of platform trials in the COVID-19 pandemic: A rapid review

Motivation

Platform designs - master protocols that allow for new treatment arms to be added over time - have gained considerable attention in recent years. Between 2001 and 2019, 16 platform trials were initiated globally. The COVID-19 pandemic seems to have provided a new motivation for these designs. We conducted a rapid review to quantify and describe platform trials used in COVID-19.

Methods

We cross-referenced PubMed, ClinicalTrials.gov, and the Cytel COVID-19 Clinical Trials Tracker to identify platform trials, defined by their stated ability to add future arms.

Results

We identified 58 COVID-19 platform trials globally registered between January 2020 and May 2021. According to trial registries, 16 trials have added new therapies (median 3, IQR 4) and 11 have dropped arms (median 3, IQR 2.5). About 50% of trials publicly share their protocol, and 31 trials (53%) intend to share trial data. Forty-nine trials (84%) explicitly report adaptive features, and 21 trials (36%) state Bayesian methods.

Conclusions

During the pandemic, there has been a surge in the number of platform trials compared to historical use. While transparency in statistical methods and clarity of data sharing policies needs improvement, platform trials appear particularly well-suited for rapid evidence generation. Trials secured funding quickly and many succeeded in adding new therapies in a short time period, thus demonstrating the potential for these trial designs to be implemented beyond the pandemic. The evidence gathered here may provide ample insight to further inform operational, statistical, and regulatory aspects of future platform trial conduct.