Currently, there is no widely adopted definition of Digital Health. At PHASTAR, we take a broad view and refer to Digital Health as the "use of technology in healthcare", as it encompasses the use of computing platforms, connectivity, software, artificial intelligence, machine learning, and sensors for health care and related uses.
Just 20 years ago, clinical trials were primarily carried out on paper, with queries posted to the sites for resolution. This created significant delays between initial data entry and query resolution. Until 2015, Direct Data Capture (DDC) systems and Electronic Patient Reported Outcomes (ePROs) were rarely utilised in clinical trials.
The ACDM's eDigital Data Management Expert Group (DMEG) recently released the paper titled “Top 15 Considerations for Decentralised Clinical Trials (DCTs).”
Among the co-authors includes PHASTAR's Director of Data Science, Dr Jennifer Bradford.
You can read more about the eDigital data management expert group and access their publications here.
Written by Melanie Tippetts, Principal Statistician, and Professor Jennifer Rogers, VP, Statistical Research and Consultancy PHASTAR on . Posted in Blog
COVID-19 and its subsequent variants have provided challenges in many ways, not the least of which are in the conduct and management of clinical trials. Global quarantines and disruptions in investigational product supply, patient recruitment and sustainability have been major concerns in an era when it’s more important than ever to proceed with drug development. But how can we ensure that clinical trials can continue with quality data? We review how some of these challenges can be overcome now and, in the future, to make sure that quality data can be collected and analyzed.
Now into our second year of facing these challenges, clinical trial sponsors, managers and regulators will have to even more closely collaborate and share learnings that will be critical for the long-term, particularly in the use of different methods and assessments for the collection of end points. Additional data, such as reasons for missed/delayed visits/assessments, will also need to be collected in the clinical database, as it is important to capture all COVID-19-related data in current trials.
The range of disruptions of COVID-19 to clinical trials have been so diverse that no single solution will be appropriate for all trials. Although there are many common issues, each trial must be assessed on a case-by-case basis.
This article will examine some best practices to assure that the trial can continue during the ongoing pandemic – including current vaccine trials – so that the development of treatments and medicines isn’t interrupted.
Written by Jennifer Bradford, PhD, Director of Data Science, PHASTAR and Sheelagh Aird, PhD, Senior Director of Clinical Data Operations, PHASTAR on . Posted in Blog
A great many new andvaried approaches to clinical trial management are being adopted during the COVID-19 pandemic through the help of virtualization tools, strong partnerships, and regulatory guidance. Despite the upheaval this year and last, there appears to be a silver lining largely due to the systemic changes leading to the remarkably quick development in adapting trials to accommodate different environments and the incredible speed at which COVID-19 vaccines have been developed and administered. Regulatory guidance has accommodated this abrupt shift. In this article, we will cover the key factors needed prior to adapting to patient-centric clinical trials.
For starters, there are quite a few differences regarding attaining and disseminating patient-level data in a decentralized or remote trial setting versus the traditional way an in-person study is designed. Telemedicine or remote visits, for instance, have traditionally only been used for patient-physician consultations in the health care setting. However, the value of telemedicine for use in clinical trials has grown ever more promising due to the greater access to research and reduced attrition it can deliver.
Data collection methods tend to be the primary component that changes with decentralized clinical trials in comparison to in-person studies. With remote trials, the engagement with digital technology for data capture provides the potential to receive information at a higher frequency, which means more data is available.
Written by Prof. Jennifer Rogers on . Posted in Blog
Omicron has been a real step change for COVID-19 with reports of it being a highly transmissible variant of the virus, but potentially not as serious. On the 27th November the UK Government published that the first cases of Omicron had been identified in the UK. Following this, we saw a huge increase in cases with the 7-day average peaking on the 2nd January at 183,084. Just to put this figure into perspective, prior to the Omicron wave, the next largest figure that we’d seen for the 7-day cases average was 59,660. The media was full of experts telling us that even though Omicron was suspected to be less serious than other variants, we were still likely to see relatively big increases in hospitalisations and deaths simply due to the huge number of Omicron cases.
Let’s say, for example, that we have a variant A that has a hospitalisation rate of 10% and variant B that has a hospitalisation rate of 5% (so half the hospitalisation rate). If variant A has 59,660 cases, this would give us an expected number of hospitalisations of 5,966. If variant B has 183,084 cases, this would give us an expected number of hospitalisations of 9,154. So theoretically, we could still have a problem on our hands.
Hospital admissions did increase, but these now look to be levelling off and deaths now look to be on a steep upward trajectory. But what makes a COVID-19 hospitalisation a COVID-19 hospitalisation? And what makes a COVID-19 death a COVID-19 death? Looking into this in more detail suggests that things may be more optimistic than the daily reported number of hospital admissions and deaths suggest.
Let’s look at the definitions for COVID-19 hospital admission and COVID-19 death. Hospital admissions includes all admissions with a positive COVID-19 test. So, if you went into hospital with a broken leg or needed to be admitted into hospital with appendicitis, but you happened to test positive for COVID-19 at the time, you would be formally defined as a COVID-19 hospital admission. This article in The Guardian quotes Chris Hopson, Chief Executive of NHS Providers, who said “What our guys are saying is that incidental cases are about 25% to 30% of cases arriving … They are seeing an increase in the number of hospital admissions, but it’s not going up in an exponential way,”
Similarly, COVID-19 deaths are defined as a death where the person tested positive for COVID-19 within the preceding 28 days. I actually tested positive for COVID-19 26 days ago. If I happened to be knocked over by a car whilst out walking my dogs today, I would officially be recorded as a COVID-19 death on the UK Government COVID-19 dashboard. As the number of COVID-19 cases increases, and if these cases are in fact milder, the proportion of COVID-19 hospital admissions and COVID-19 deaths that are “true COVID” will decrease. There will be a higher likelihood of people who happen to have COVID-19 being admitted to hospital for other reasons and a higher likelihood of people dying from an unrelated reason in the 28 days following a positive COVID-19 test result.
But there are more reliable metrics that we can look at. I always view the number of COVID-19 occupied mechanical ventilation beds as a more consistent estimate of the impact of severe COVID-19. We can see from Figure 1 that Omicron has not resulted in an increase in these figures. In fact, these numbers seem to be on a slow and steady downward trajectory. That’s not to say that Omicron has not had an impact on healthcare services. Having to separate COVID-19 positive patients is undoubtably a burden on hospitals. And an increase in Omicron cases has also resulted in an increase in staff absences. But the number of people being admitted into hospital because of COVID-19 may not have increased as suggested by the UK Government COVID-19 dashboard.
Likewise, the Office for National Statistics (ONS) collects the number of deaths where COVID-19 is reported on the death certificate as a cause of death. This information is also presented on the UK Government COVID-19 dashboard but is slower to collect and process. The latest data only goes up until the 31st December (Figure 2), so whilst we should have started to see the effects of Omicron by this point, it could be the next few weeks before we see its full impact. Typically, we assume a 14-day lag between COVID-19 cases and deaths, so we should have started to see the impact of Omicron already and the data looks to be declining in line with the number of COVID-19 occupied mechanical ventilation beds. We will need to wait to see if this trend will continue.
Figure 2: Daily deaths with COVID-19 on the death certificate by date of death
Why do we have so many different death counts? Surely a COVID-19 death is a COVID-19 death. How can we have different deaths? And why are these ONS figures two weeks old? The ONS remains the gold standard as they must be certified by a doctor, registered, and processed. But this also makes them the slowest. The daily counts that we have become accustomed to seeing every day only include deaths in hospital of those who have tested positive for COVID-19 within the last 28 days. This makes them much quicker to collect, but also less accurate. The COVID-19 pandemic has remained a rapidly changing situation and there will always be a balancing act between getting access to “dirty” data quickly and having the most accurate data available but having to wait for it.
The next week will be interesting as we wait to see if the hospitalisation data continues to come down and we keep our eye on the death data. As discussed in this PHASTAR blog, the rules for PCR testing have changed and this could theoretically be influencing case numbers. The coming weeks will be very interesting to see what happens to the data.
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