1477The landscape of COVID-19 trials in Australia

Abstract Background The coronavirus disease 2019 (COVID-19) pandemic has seen a large number of clinical trials launched at unprecedented speed. We aimed to explore the landscape of COVID-19 trials in Australia, and to what extent Australian researchers have responded to global need for coordination and collaboration. Methods We systematically searched the Australian New Zealand Clinical Trials Registry (ANZCTR) and ClinicalTrials.gov from 1st January to 16th November 2020. We included all interventional studies addressing prevention, diagnosis or treatment of COVID-19, recruiting in Australia. We analysed the number and size of trials, additional recruitment countries, funding, trial purpose, study design, data sharing plans, and collection of COVID-19 core outcomes. Results We identified 56 COVID-19 trials, targeting 33,757 participants. They evaluated drugs (n = 34, 61%), vaccines (n = 10, 18%) or other interventions (n = 12, 21%), e.g. ventilators, digital health. Median target sample size was 150 (Q1-Q3=33-395). Only two trials utilised adaptive methods (Bayesian designs), and of the 34 COVID-19 treatment trials, only one included all core outcomes. Most (80%) indicated they were not planning to share data. Conclusions There has been impressive research scale-up and innovation in drug development and digital health, but fast-track procedures may have impacted scientific rigor and research prioritisation. Trials often lacked innovative study designs, were underpowered for clinical outcomes, collected limited core outcomes and did not intend to share data, precluding future evidence synthesis. Key messages The research response in Australia has been rapid, but better coordination is required. Infrastructure for innovation would support coordination of research efforts, and reduce research waste.

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Attribution of non-ClinicalTrials.gov registries among WHO International Clinical Trials Registry Platform-registered trials from 2014 to 2018: A protocol for a meta-epidemiological study

10.7287/peerj.preprints.27298v1 ◽

2018 ◽

Author(s):

Masahiro Banno ◽

Yasushi Tsujimoto ◽

Yuki Kataoka

Keyword(s):

Clinical Trials ◽

Epidemiological Study ◽

Medical Information ◽

University Hospital ◽

World Health ◽

Study Phase ◽

Target Sample Size ◽

Registration Number ◽

Health Organization ◽

Clinical Trials Registry

Background. The attribution of non-ClinicalTrials.gov registries among registered trials of the World Health Organization (WHO) International Clinical Trials Registry Platform (ICTRP) had increased until 2013. However, the attribution after 2013 is unknown. Moreover, no study has investigated the usage of non-ClinicalTrials.gov registries after 2015 or compared the characteristics of trials under non-ClinicalTrials.gov and ClinicalTrials.gov registries. Methods. This will be a meta-epidemiological study. It will include all trials registered on the ICTRP from January 1, 2014, to December 31, 2018. First, we will describe the total attribution of non-ClinicalTrials.gov registries among the ICTRP-registered trials for each year and each registry worldwide. Second, we will compare the recruitment status, target sample size, study type, study design, countries, prospective registration, funding, and study phase of the trials on ClinicalTrials.gov and other registries from 2014 to 2018. Third, we will report on the distribution of primary registries of trials from the top five countries in order of the quantity of registered trials on the ICTRP. Ethics & Dissemination. Ethics approval is not required for this study. This protocol has been registered in the University Hospital Medical Information Network Clinical Trials Registry (UMIN-CTR). The findings will be published in a peer-reviewed journal and may be presented at conferences. Trial Registration Number. UMIN000034401

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Indian Clinical Trials on COVID-19: A Review of Clinical Trials Registry of India (CTRI)

SSRN Electronic Journal ◽

10.2139/ssrn.3674093 ◽

2020 ◽

Author(s):

N Vasantha Raju ◽

N.S. Harinarayana

Keyword(s):

Clinical Trials ◽

Clinical Trials Registry

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Analysis of the Market, Regulatory Landscape, and Current State of Clinical Trials Pertaining to Digital Health

Technology Transfer and Entrepreneurship ◽

10.2174/2213809905666180816115016 ◽

2018 ◽

Vol 5 (1) ◽

pp. 21-34

Author(s):

Cheyenne E. Allenby ◽

Eric S. Babiash ◽

Patrick N. Blank ◽

Marco D. Carpenter ◽

Isabelle G. Lee ◽

...

Keyword(s):

Clinical Trials ◽

Digital Health ◽

Current State ◽

Regulatory Landscape

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Clinical trial registries as Scientometric data: A novel solution for linking and deduplicating clinical trials from multiple registries

Scientometrics ◽

10.1007/s11192-021-04111-w ◽

2021 ◽

Author(s):

Christian Thiele ◽

Gerrit Hirschfeld ◽

Ruth von Brachel

Keyword(s):

Clinical Trials ◽

Random Forest ◽

Random Forest Model ◽

Scientometric Analysis ◽

Data Set ◽

The Public ◽

Forest Model ◽

Clinical Trial Registries ◽

Multiple Primary ◽

Clinical Trials Registry

AbstractRegistries of clinical trials are a potential source for scientometric analysis of medical research and serve important functions for the research community and the public at large. Clinical trials that recruit patients in Germany are usually registered in the German Clinical Trials Register (DRKS) or in international registries such as ClinicalTrials.gov. Furthermore, the International Clinical Trials Registry Platform (ICTRP) aggregates trials from multiple primary registries. We queried the DRKS, ClinicalTrials.gov, and the ICTRP for trials with a recruiting location in Germany. Trials that were registered in multiple registries were linked using the primary and secondary identifiers and a Random Forest model based on various similarity metrics. We identified 35,912 trials that were conducted in Germany. The majority of the trials was registered in multiple databases. 32,106 trials were linked using primary IDs, 26 were linked using a Random Forest model, and 10,537 internal duplicates on ICTRP were identified using the Random Forest model after finding pairs with matching primary or secondary IDs. In cross-validation, the Random Forest increased the F1-score from 96.4% to 97.1% compared to a linkage based solely on secondary IDs on a manually labelled data set. 28% of all trials were registered in the German DRKS. 54% of the trials on ClinicalTrials.gov, 43% of the trials on the DRKS and 56% of the trials on the ICTRP were pre-registered. The ratio of pre-registered studies and the ratio of studies that are registered in the DRKS increased over time.

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A Roadmap to Inform Development, Validation and Approval of Digital Mobility Outcomes: The Mobilise-D Approach

Digital Biomarkers ◽

10.1159/000512513 ◽

2020 ◽

Vol 4 (1) ◽

pp. 13-27 ◽

Cited By ~ 1

Author(s):

Lynn Rochester ◽

Claudia Mazzà ◽

Arne Mueller ◽

Brian Caulfield ◽

Marie McCarthy ◽

...

Keyword(s):

Health Care ◽

Clinical Trials ◽

Health Care Professionals ◽

Digital Health ◽

Disease Status ◽

Proximal Femoral Fracture ◽

Chronic Obstructive ◽

Obstructive Pulmonary Disease ◽

Physical Mobility ◽

High Level

Health care has had to adapt rapidly to COVID-19, and this in turn has highlighted a pressing need for tools to facilitate remote visits and monitoring. Digital health technology, including body-worn devices, offers a solution using digital outcomes to measure and monitor disease status and provide outcomes meaningful to both patients and health care professionals. Remote monitoring of physical mobility is a prime example, because mobility is among the most advanced modalities that can be assessed digitally and remotely. Loss of mobility is also an important feature of many health conditions, providing a read-out of health as well as a target for intervention. Real-world, continuous digital measures of mobility (digital mobility outcomes or DMOs) provide an opportunity for novel insights into health care conditions complementing existing mobility measures. Accepted and approved DMOs are not yet widely available. The need for large collaborative efforts to tackle the critical steps to adoption is widely recognised. Mobilise-D is an example. It is a multidisciplinary consortium of 34 institutions from academia and industry funded through the European Innovative Medicines Initiative 2 Joint Undertaking. Members of Mobilise-D are collaborating to address the critical steps for DMOs to be adopted in clinical trials and ultimately health care. To achieve this, the consortium has developed a roadmap to inform the development, validation and approval of DMOs in Parkinson’s disease, multiple sclerosis, chronic obstructive pulmonary disease and recovery from proximal femoral fracture. Here we aim to describe the proposed approach and provide a high-level view of the ongoing and planned work of the Mobilise-D consortium. Ultimately, Mobilise-D aims to stimulate widespread adoption of DMOs through the provision of device agnostic software, standards and robust validation in order to bring digital outcomes from concept to use in clinical trials and health care.

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Digital health interventions in palliative care: a systematic meta-review

npj Digital Medicine ◽

10.1038/s41746-021-00430-7 ◽

2021 ◽

Vol 4 (1) ◽

Author(s):

Anne M. Finucane ◽

Hannah O’Donnell ◽

Jean Lugton ◽

Tilly Gibson-Watt ◽

Connie Swenson ◽

...

Keyword(s):

Decision Making ◽

Palliative Care ◽

Systematic Reviews ◽

Psychological Symptoms ◽

Digital Health ◽

Evidence Synthesis ◽

Information Provision ◽

Health Interventions ◽

Meta Review

AbstractDigital health interventions (DHIs) have the potential to improve the accessibility and effectiveness of palliative care but heterogeneity amongst existing systematic reviews presents a challenge for evidence synthesis. This meta-review applied a structured search of ten databases from 2006 to 2020, revealing 21 relevant systematic reviews, encompassing 332 publications. Interventions delivered via videoconferencing (17%), electronic healthcare records (16%) and phone (13%) were most frequently described in studies within reviews. DHIs were typically used in palliative care for education (20%), symptom management (15%), decision-making (13%), information provision or management (13%) and communication (9%). Across all reviews, mostly positive impacts were reported on education, information sharing, decision-making, communication and costs. Impacts on quality of life and physical and psychological symptoms were inconclusive. Applying AMSTAR 2 criteria, most reviews were judged as low quality as they lacked a protocol or did not consider risk of bias, so findings need to be interpreted with caution.

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