Lessons learned from the 2014 Ebola outbreak and how they can be transferred to the COVID-19 pandemic

During a pandemic, data collection is of the upmost importance to be able to control and stop the spread of disease. It is essential to have adaptable, available data systems with clear, simplified forms that can be completed off-line and these systems must be operable both on and off network to enable field workers to complete the fields while working in remote locations and then later upload the data to a repository when there is network access. Overall, a solid IT system is necessary for quick capture and relay of information.

Another challenge during an outbreak response is creating and disseminating effective and timely information to the public. This requires a full understanding of the knowledge, attitudes and beliefs of the target audience. Target communication needs to account for the local level of understanding and literacy while being culturally appropriate. There needs to also be a way of monitoring if the communication is effective, again this needs to be done in real time. It is vital that communication is simple, consistent and reliable.

The current COVID-19 pandemic has seen a large increase of data systems and other forms of communication. Furthermore, there are a huge number of clinical studies ongoing or in the pipeline to address this crisis. Moving forward, a central repository housing essential information (e.g. trial protocols and analysis plans, interim findings, and real-time reporting of primary results) on completed and ongoing clinical trials will be needed, in order to encourage collaboration and information sharing, as well as to avoid any redundancy in research efforts.

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Managing COVID-19 issues in Clinical Trials

In the later stages of 2019 and the beginning of 2020, the Coronavirus pandemic began spreading across the world and it became apparent that it would affect so many areas of how we live our lives, from children attending school, working in our offices and being able to shop freely.  Since before the lockdown began in the UK on the 23rd March, there have been restrictions put in place at clinical trial sites to protect their patients including restricting unnecessary visitors (such as CRAs) to site. 

Guidance documents on how to conduct clinical trials during the COVID-19 crisis have been published by regulators, providing guidance on (among other areas) what considerations should be made regarding the collection of any data relating to COVID-19 interruptions, the impact of missing data and data collected outside the protocolled time window. 

The guidance says to capture specific information in the case report form that will explain the basis of the missing data, including the relationship to COVID-19, for missing, protocol-specified information, e.g. from missed study visits or study discontinuations due to COVID-19. This information, summarised in the clinical study report will be helpful to the sponsor and FDA.   

Taking the guidance in to consideration, we quickly developed a CRF page to capture the COVID-19 interruptions on the data including accounting for missing visits, missing assessments, delayed visits and assessments and other impacts on the data due to COVID-19. The guidance is that COVID-19 interruptions should be detailed in the clinical study report (CSR), and so we have developed a data collection tool to capture this dataThis CRF page was available to show our clients within days of the guidance being issued. 

We have also helped a client develop an alternative process, without the use of this eCRF, through using queries to identify missing data affected by COVID-19 and subsequently those query results will be used to identify protocol deviations caused by COVID-19. Collecting the COVID-19 interruptions as protocol deviations seems to be the solution of choice for Sponsors of clinical trials which are already well established. There are slightly increased risks with this solution compared to using the eCRF page however, these risks have been addressed and a process put in place.  

Following a communication released by ICH M1 Points to Consider Working Group and MedDRA MSSO on Coronavirus (here), we created some text to be added to our coding document so that our individual study guidelines cover the coding of the coronavirus related medical terms at that point COVID-19 specific terms were not available in the MedDRA version 23.0 regular release which took place in March 2020. The MSSO subsequently re-released MedDRA v23.0 in April which included new COVID-19 related terms and revisions - this is a replacement of the regular March release.  Where appropriate, MedDRA has been up-versioned to V23.0 in our studies. Our coding documents have been updated to reflect any up-versioning of MedDRA and instruction on coding of any COVID-19 related terms. 

We are closely following Coronavirus updates from WHODrug. Currently there are no plans to re-release the March 2020 version of WHODrug Global but if those plans change, we are ready to implement in the same way that we have done for MedDRA. Currently novel COVID-19-related medicinal products (for example, new molecular entities and new vaccines) are being included in the dictionary ready for the next release in September 2020.   

We have been working closely with our clients to support and advise on discussions regarding changes to data collection e.g. remote visits, options regarding using ePRO and changes to our processes to support data cleaning whilst CRAs cannot attend site. Risk-based monitoring options can also be used increasingly so that we can continue to ensure a high standard of data collection and reporting.   

At PHASTAR, our data management team control and implement the database design on our projects and therefore we have been able to provide a fast turn-around of database amendments to support adjustments to data collection e.g. applying the new CRF page for capturing COVID-19 interruptions, adding information to individual forms regarding reasons for non-collection or delay.  We continue to work with our clients to help them manage the changes for their specific trials using the information we have gained from the various industry guidance and discussions currently taking place. If you would like more information, please don’t hesitate to get in touch. 

Making sense of the ONS COVID-19 death count

The Office for National Statistics (ONS) has just published their latest release for deaths registered weekly in England and Wales (provisional) for the week ending 3rd April. The main point from this release is that the provisional number of deaths registered in England and Wales in the week ending 3rd April 2020 was 16,387, the highest number of deaths since official weekly statistics began 15 years ago. This period in question doesn’t even cover the last week, which has seen the highest number of deaths being published by the Department for Health and Social Care. What should we take away from these figures?

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Clearing the fog surrounding LOINC

Beginning 15th March 2020, submissions of studies for New Drug Applications (NDAs), Abbreviated New Drug Applications (ANDAs), and certain Biologic License Applications (BLAs) will be required to include Logical Observation Identifiers Names and Codes (LOINC®) as specified by the Food and Drug Administration (FDA) Data Standards Catalog with some INDs (Investigational New Drug) having the same requirement from 15th  March 2021. These standards will allow for succinct interoperability between clinical data systems. 

While seemingly problematic from  the outset, LOINC provides a rich database for clinicians and researchers to draw from. This ensures that once standards are applied to their data, local or proprietary terms can be easily transferred to other institutions, allowing for a swift exchange of data.

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Technology, AI and real world evidence

Technology, AI and real world evidence

Real world evidence (RWE) in medicine is the clinical evidence regarding the use and potential benefits or risks of a medical product derived from the analysis of real-world data (RWD). RWD are effectively data collected from outside of a clinical trial and that relate data to the patient health status and/or the delivery of health care. RWD is routinely collected through different digital health sources for example electronic health records (EHRs), product/disease registries, patient-generated data, medical claims/billing databases, mobile devices etc.

Increasing volumes of RWD are being produced following the development of specialist devices and sophisticated data collection techniques.  Together with technological advancements including computing power and storage, there is an opportunity for powerful artificial intelligence (AI) approaches to be applied to these data to process and provide valuable insights for patient benefit. In the context of drug development, the application of AI to RWD and subsequent generation of RWE has huge potential with examples including analysis of patient treatment pathways, risk of disease development for patients, tracking patient behaviour’s and adherence.

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