Atrial fibrillation (AF) is predominantly a disease of older age, with a doubling
of risk for each decade lived.[1] As well as experiencing more AF, older adults are more likely to suffer the consequences
of AF, most notably stroke, and they are more likely to be left dead or disabled following
an AF-related stroke.[1] Despite this age-associated ‘treble whammy’, the majority of evidence around treatment
of AF has focused on middle-aged adults. This apparent disconnect between evidence
and practice may be one reason why there is marked variation in clinical practice
around treating AF in older adults.[2]
Of course age is just a number and even within the oldest old, there will be a spectrum
of health from robust, community-dwelling through to frail, multi-morbid older adults.
It is in this latter group that clinical trial evidence is especially lacking. AF
is often associated with frailty and co-morbidity.[3] Yet, traditional, large cardiovascular randomised controlled trials (RCTs) have
generally excluded such patients. Certainly, including older adults living with frailty
in AF-based RCTs poses challenges, for example, there will be issues with capacity,
attrition, test burden and competing risk.[4] Encouragingly, there are emerging examples of trials that focus on frailty and also
examples of trials that describe frailty syndromes as study outcomes.[4]
A potential solution to the perceived lack of generalisability of RCTs is to use existing
clinical data from large registries and databases. Many such studies have been published,
that have examined treatment patterns and outcomes.[5]
[6]
[7] Secondary analysis of big data from real-world settings offers several advantages,
not least that clinical hypotheses can be tested at scale for a fraction of the cost,
time and manpower required for a prospective study.[8] In this issue of Thrombosis & Haemostasis, there are several papers where real-world data are used to improve our understanding
of AF treatment in older adults. These papers are exemplars of what can be achieved
through analysis of routine data, but they also highlight some of the limitations
of the big data approach.
Hohmann et al[9] used information held in a German healthcare claims database to look at risk profiles
of various anticoagulation treatment approaches in older adults. The dataset included
70,501 patients newly started on anticoagulation for AF (n = 42,562 [60.4%] with non-vitamin K antagonist [VKA] oral anticoagulant [NOAC]).
The population were categorised by age, frailty, co-morbidity and polypharmacy. Across
all these categories, the risk of stroke and systemic embolism was higher than had
been described in the seminal RCTs of NOACs.[1] Comparing OAC treatments (NOAC vs.phenprocoumon), there was no signal of differential
treatment efficacy between NOAC and a traditional VKA. However, when comparing safety
profiles, in this case defined by major bleeding, there was a clear difference between
groups in favour of NOAC prescribing.
Using a similar approach of interrogating a large German healthcare claims database,
Hohnloser et al[10] describe rates of OAC discontinuation and switching (VKA to NOAC, NOAC to VKA and
NOAC to NOAC) for a cohort of older adults newly prescribed anticoagulation for AF.
Again the cohort was impressively large, with 51,606 patients (30,138 [58.4%] prescribed
NOAC). We often think of anticoagulation for AF as a life-long treatment, however,
in this cohort around one-third had discontinued anticoagulation at 1year. Rates of
discontinuation were similar across group, NOAC (29.5%) and VKA (29.9%). Older adults
were more likely to persist with OAC; a reassuring finding given that this group are
at higher risk of AF-related stroke. In total, 5,149 (10%) patients switched from
their initial anticoagulant to another agent, with those initially prescribed VKA
more likely to switch than those prescribed NOAC (2,298 [11%] vs. 2,851 [9.5%]). Change
of OAC was associated with clinical events, both stroke and bleeding. There was also
a temporal prescribing change with more conversion to NOAC over time, perhaps as physicians
become more familiar with these new medications.
Finally, Field et al[11] focused their attention on one of the outcomes of greatest concern to older adults,
namely dementia. Using the U.K. Clinical Practice Research Database, they created
matched cohorts with and without AF, totalling 91,372 (15,276 [16.7%] with AF). By
linking the primary care dataset to U.K.National Health Service secondary care registries,
they examined incident dementia over an average of 2 years of follow-up. They report
an increased risk between AF and incident dementia (adjusted hazard ratio:1.31 [95%
confidence interval:1.15–1.49]), albeit with a smaller effect than demonstrated in
previous studies.[12] By performing sub-group analyses looking at anticoagulation prescription and incident
stroke, we can speculate on potential mechanisms underlying this association. Prescription
of anticoagulation seemed to ameliorate the risk of dementia, suggesting that cardioembolism
is likely to be a factor in any cognitive decline. However, the risk of dementia remained
significant even after removing all those with stroke or transient ischaemic attack.
This suggests that it is not only stroke that is driving cognitive change, or at least
not clinically apparent stroke. A disheartening observation in this contemporary cohort
was that around one-third of older adults with AF received no anticoagulation treatment.
So, what messages can we take from these three complementary analyses? Clearly, routinely
collected data represent a powerful resource for research in older adults. The numbers
included in these analyses would be almost impossible to replicate in a bespoke, prospective
study. However, ‘big data’ are not necessarily better data and if the data have inherent
limitations, the large sample size will only serve to make a biased estimate more
precise. Of particular concern, are those patients who are not captured in the databases,
for example, the two German studies only included AF patients newlyinitiating OAC.
Yet, other data tell us that often the highest risk AF groups are not prescribed evidence-based
therapies and so will not feature in these prescribing-based registers.[2]
The studies described looked at syndromes of older age through the lens of what is
recorded and available in large databases. In this regard, co-morbidity and polypharmacy
are relevant. However, for many older adults, the outcomes of interest may not be
routinely recorded and clinical register-based research may struggle to assess factors
such as falls, disability and admission to care homes. Even where data are available,
the validity is not guaranteed. Taking dementia as an example, recording of dementia
in clinical registers tends to be specific but insensitive and may miss many living
with the disease.[13] VKA medications are problematic for big data approaches, as the interpretation requires
additional information on dosing and international normalised ratio, which is often
lacking. In this regard, big data researchers will be glad of the increasing use of
NOACs which are analytically easier to handle.
Even with the most sophisticated analyses, matching and adjustment, we must be cautious
about how we interpret these observational data. Retrospective analyses can miss the
nuance and clinical rationale for prescribing decisions and the preference for a particular
anticoagulant strategy will not be made at random. This is likely to be a particular
issue in older adults where the evidence to guide treatment decisions may be best
practice recommendations rather than from RCTs.[14]
For all these reasons, big data approaches are best regarded as complementing other
study methods including mechanistic pre-clinical experiments, bespoke cohorts and
large-scale clinical trials (see [Fig. 1]). Using routinely recoded data can generate new hypotheses to be tested or can validate
results generated using other research methods. From the data presented in these three
papers, we can be reasonably confident about prescribing NOACs in older adults but
whether these drugs will reduce frailty syndromes such as dementia will require testing
in large-scale trials.[12]
Fig. 1 Incorporating big data into all aspects of drug development. This illustrates a hypothetical
scenario of repurposing an existing drug for a new indication.
