Digital technologies to support self-management of COPD: Evidence generation plan
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3 Approach to evidence generation
3.1 Evidence gaps and ongoing studies
Table 1 summarises the evidence gaps and ongoing studies that might address them. Information about evidence status is derived from the external assessment group's report. More information on the studies in the table can be found in the supporting documents.
Evidence gap | Active+me REMOTE | Clinitouch | COPDhub | COPDPredict | Lenus | Luscii | myCOPD | SPACE for COPD |
|---|---|---|---|---|---|---|---|---|
Impact of the digital technologies compared with standard self-management | Limited evidence | No evidence | No evidence | Limited evidence Ongoing study | Limited evidence | No evidence | Limited evidence Ongoing study | Limited evidence Ongoing study |
Long-term clinical improvement in COPD using a validated measure | Limited evidence | No evidence | No evidence | No evidence | Limited evidence | No evidence | Limited evidence Ongoing study | Limited evidence Ongoing study |
Resource use | No evidence | Limited evidence | Limited evidence | Limited evidence | Limited evidence | Limited evidence | No evidence | Limited evidence Ongoing study |
Engagement and adherence | Limited evidence | No evidence | No evidence | Limited evidence Ongoing study | Limited evidence | Limited evidence | Limited evidence Ongoing studies | No evidence |
Adverse events | Limited evidence | No evidence | No evidence | Limited evidence | No evidence | No evidence | Limited evidence | No evidence |
Health-related quality of life | Limited evidence | No evidence | No evidence | Limited evidence | Limited evidence | No evidence | Good evidence Ongoing study | Limited evidence |
Effectiveness in different subgroups | No evidence | No evidence | Limited evidence | No evidence | Limited evidence | No evidence | No evidence Ongoing study | Limited evidence Ongoing study |
Where the technologies are used in the care pathway | Limited evidence | No evidence | No evidence | No evidence | Limited evidence | No evidence | Limited evidence Ongoing study | Limited evidence Ongoing study |
3.2 Data sources
There are several data collections that could potentially support evidence generation. NICE's real-world evidence framework provides detailed guidance on assessing the suitability of a real-world data source to answer a specific research question.
The National Respiratory Audit Programme (NRAP) is a clinical audit dataset for people with respiratory disease (including COPD). It collects information about people admitted to hospital with an exacerbation of COPD. It includes much of the data needed to address the evidence gaps, such as standard care in the NHS, hospital admissions and exacerbations because of COPD. NRAP can be linked to other datasets such as the Hospital Episode Statistics dataset, and this combined dataset can be used to estimate resource use. The dataset can be amended to support additional data collection where necessary. Some people with COPD who have exacerbations may only have treatment in primary care or at home, so data about these people is not recorded in NRAP. A potential approach to addressing this gap in the data could be to link to primary care datasets such as Clinical Practice Research Datalink (CPRD) and The Health Improvement Network (THIN).
The quality and coverage of real-world data collections are of key importance when used in generating evidence. Active monitoring and follow up through a central coordinating point is an effective and viable approach of ensuring good-quality data with broad coverage.
3.3 Evidence collection plan
Real-world prospective cohort studies
Prospective controlled cohort studies are the proposed approach to address the evidence gaps. The studies should enrol a representative population, that is, people who would be offered standard care, including self-management of COPD, without digital technologies. This may include face-to-face appointments and monitoring. The studies should compare people with COPD using digital technologies for self-management with a similar group having standard care. Eligibility for inclusion, and the point of starting follow up should be clearly defined and consistent across comparison groups to avoid selection bias.
Data should be collected in all groups from the point at which a person would become eligible for standard care. The data from both the intervention and comparison groups should be collected at appropriate time intervals and up to a minimum of 12 months. Data from people in different centres, with comparable standard care and patient population, but no access to digital technologies for self-management, should form the comparison group. Ideally, the studies should be run across multiple centres, aiming to recruit centres that represent the variety of care pathways in the NHS.
To reduce the variability in repeated measurements and therefore the effects of regressions to the mean, people who are selected into the studies should ideally provide multiple baseline measurements.
Despite consistent eligibility criteria, non-random assignment to interventions can lead to confounding bias, complicating interpretation of the treatment effect. So, approaches should be used that balance confounding factors across comparison groups, for example, using propensity score methods. To achieve this robustly, data collection will need to include prognostic factors related both to the intervention delivered and patient outcomes. These should be defined with input from clinical specialists. Also, subgroup analysis should also be done for important covariates. For example, stratification according to the severity of COPD would be useful when generating evidence to help inform practice.
Data could be collected using a combination of primary data collection, suitable real-world data sources, and data collected through the technologies themselves (for example, engagement data).
Data collection should follow a predefined protocol and quality assurance processes should be put in place to ensure the integrity and consistency of data collection. See NICE's real-world evidence framework, which provides guidance on the planning, conduct and reporting of real-world cohort studies to assess comparative treatment effects.
3.4 Data to be collected
Study criteria
At recruitment, eligibility criteria for suitability of using the digital technologies and inclusion in the real-world study should be reported, and include:
a clinical diagnosis of COPD
position of the technology in the clinical pathway
the point that follow-up starts.
Detailed description of the standard care offered.
Detailed description of the technologies including features offered in the technologies, and the specific versions.
Baseline information and outcomes
Respiratory function using the COPD Assessment Test score at baseline and over follow up (for a minimum of 12 months).
COPD severity using the Global Initiative for Chronic Obstructive Lung Disease (GOLD) classification at baseline and over follow up (for a minimum of 12 months).
Changes in COPD symptoms, including exacerbation rates, at baseline and over follow up (for a minimum of 12 months).
Information on healthcare resource use and exacerbation-related hospitalisation costs related to COPD, including:
primary care visits
emergency department visits
hospital visit and admissions, and length of stay.
Costs of digital technologies for supporting self-management of COPD, including:
licence fees
healthcare professional staff time and training costs to support the service
integration with NHS systems.
implementation costs
implementation costs
other technology costs.
EQ-5D-3L at baseline and over follow up (for a minimum of 12 months).
Access and uptake including the number and proportion of eligible people who were able to, or accepted an offer to, access digital technologies to support self-management of COPD.
Engagement with, and information about, stopping using digital technologies for supporting self-management of COPD, including reasons for stopping. This should include:
the number of people starting to use digital technologies for supporting self-management of COPD
engagement over time (for example, use of exercises, symptom tracking, or other features in the specific technology)
reasons for stopping (for example, because of improvements in symptoms, adverse effects, or other reasons)
people who were offered and have refused the support of digital technologies for self-management, and reason for refusal.
Information about individual characteristics at baseline, for example, sex, age, ethnicity, clinical diagnosis and when the diagnosis was done (for example, new compared with established COPD), urban or rural location, and whether inclusion was within 4 weeks of a COPD exacerbation. Other important covariates should be chosen with input from clinical specialists.
Safety monitoring outcomes
Any adverse events arising from using digital technologies to support self-management of COPD.
3.5 Evidence generation period
The evidence generation period should be 3 years (during which a minimum of 1 year of follow-up data will be collected). This will be enough time to implement the evidence generation study, collect the necessary information and analyse the collected data.
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