Adherence to long-term anticoagulation treatment, what is known and what the future might hold

John K. Abdou1,2, Vivian Auyeung1, Jignesh P. Patel1,2, Roopen Arya2

1 Institute of Pharmaceutical Science, King’s College London, 150 Stamford Street, London, SE1 9NH

2 King’s Thrombosis Centre, Department of Haematological Medicine, King’s College Hospital Foundation NHS Trust, Denmark Hill, London, SE5 9RS

Author for correspondence:Mr John K. AbdouInstitute of Pharmaceutical SciencesKing’s College London150 Stamford StreetLondonSE1 9NHEmail: john.abdou@kcl.ac.ukTel: 01689 863766Fax: 01689 864278

Keywords: Medication Adherence, Vitamin-K Antagonists, Direct Oral Anticoagulants, Venous Thromboembolism, Atrial Fibrillation,

Accepted and published in British Journal of Haematology Volume 174, Issue 1, pages 30–42, July 2016

DOI: 10.1111/bjh.14134

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Medication non-adherence is an area of growing concern within healthcare particularly in the management of chronic diseases. Research suggests up to 50% of patients do not adhere to their treatment, as prescribed, (World Health Organisation, 2003), having significant consequences for patients in terms of healthcare outcomes and financially for healthcare systems. With a growing co-morbid, ageing population, prescribed multiple medications, the problem of medication non-adherence is likely to increase (Banerjee, 2014).

The aim of this review is to describe what is known about adherence to anticoagulant therapy, how the problem has been addressed in the past and the possible strategies that can be utilised in the future given the range of newer treatments now available. Theoretical models from the field of psychology will be introduced to better understand variations in medicine-taking behaviour and insights from studies that have targeted non-adherence will be explored. The subject of medication adherence is relevant across all chronic diseases and although this review will focus on anticoagulant therapy, many of the principles and lessons will be applicable to other haematological treatments and disciplines such as sickle cell disease (Abraham & MacDonald, 2012) and chronic myeloid leukaemia (Walsh et al, 2014).

Understanding medication non-adherence

A frequently quoted model of medication adherence is the perceptions and practicalities model describing two forms of non-adherence: intentional and non-intentional (Horne et al, 2005). Non-intentional non-adherence is due to lack of internal resources (e.g. low health literacy or an inability to open a medicine bottle) or due to a lack of external resources (e.g. poor access to healthcare). Intentional non-adherence is due to a decision made by the patient themselves, based on beliefs such as a perceived lack of need for treatment, e.g. an asymptomatic hypertensive patient (DiMatteo, 2004, Krueger et al, 2005). More recently, the COM-B model (Figure 1) has been proposed as a better means of explaining both medication adherence and the modifiable patient behaviours influencing medication non-adherence. The model suggests that a person’s Capability, Opportunity and Motivation are factors that influence a behavioural output (Michie et al, 2011). Understanding and mapping factors influencing non-adherence to COM-B processes allows for the development of interventions targeting modifiable risk factors for non-adherence. It is important to remember, that whilst clinical, social and demographic factors may influence medication adherence, they are non-modifiable and cannot be targeted for intervention (Jackson et al, 2014).

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Measuring non-adherence

The published literature frequently uses the terms adherence and persistence interchangeably, and in doing so can be misleading. Persistence is a measure of how long a patient is continually prescribed a drug without a treatment break. Treatment breaks are a continuous period without access to or claiming treatment (Vrijens et al, 2012) and typically defined as a 30 or 60 day period. For example, a patient is considered to be non-persistent if they have a gap in treatment greater than 30 days.

Adherence reflects the manner in which patients take their medications relative to the agreed regimen with the prescriber (NICE, 2009). A patient is considered to be adherent if they take their medication as prescribed more than 80% of the time (World Health Organisation, 2003). A variety of indirect and direct methods can be used to measure adherence (Table 1). Each method has strengths

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and limitations, and so a combination of techniques used simultaneously provides the most reliable measure of adherence (Vrijens & Heidbuchel, 2015, Osterberg & Blaschke, 2005).

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What is Known About Adherence to VKAsCurrent clinical guidelines recommend that patients diagnosed with atrial fibrillation (AF) with a CHA2DS2VASc score ≥2 or those who have suffered a venous thromboembolism (VTE) at high risk of recurrence should be considered for chronic anticoagulation, providing bleeding risk does not outweigh the risk of thrombosis (NICE, 2012, Camm et al, 2012). This represents an interesting scenario for patients, as during long-term treatment the anticoagulation treatment, is prescribed to prevent thrombosis, rather than for acute treatment or to provide symptomatic relief. This situation is similar to other cardiovascular diseases where medication non-adherence is well reported, e.g. hypertension (Naderi et al, 2012). Until recently, the mainstay of oral anticoagulation worldwide has been with vitamin-K antagonists (VKAs) such as warfarin.

Adherence to VKAs

The RE-COVER and RE-MEDY trials reported that over 97% of patients were adherent to VKAs, defined as having an MPR 0.8 (Schulman et al, 2009, Schulman et al, 2013). Outside of landmark clinical trials, the literature is inconsistent with regards to adherence to VKAs. Similar to RE-COVER and RE-MEDY, Wang’s study of AF and VTE outpatients reported 92% of participants were adherent according to prescription claims over a 3 month period. However, the study period was short and although the mean duration of therapy was 7.4 years, the standard deviation was 7.5 years. Thus, it is likely that a significant number of patients were newly initiated on therapy, and were undergoing more intensive monitoring with greater contact with health-professionals (Wang et al, 2014). In contrast, using prescription claims data, quite the opposite was found amongst VTE patients prescribed chronic VKA therapy, where only 23% were reported to be adherent (Chen et al, 2013).

Orensky used both prescription claims and self-reporting in an AF and VTE cohort. Although pharmacy data showed that only 10.5% of participants were non-adherent, self-reporting revealed that 41.3% of the cohort was occasionally non-adherent, with the average time in therapeutic range (TTR) for the cohort was only 44.8% (Orensky & Holdford, 2005). Elsewhere similar numbers of patients have been found non-adherent to VKAs through self-reporting (Castellucci et al, 2015).

MEMS devices provide a good opportunity to record real time use of medication and were used extensively in the IN-RANGE investigations (Kimmel et al, 2007). Forty percent of patients in this study were non-adherent and 36% failed to open the device for 20% of their doses, doubling the odds of under-anticoagulation. Under-adherence was 6 times more likely than over-adherence, with patients taking the wrong dose 22% of the time. Clinicians were found to be consistently poor at identifying non-adherence: 80% of patients who took the wrong dose more than half the time were mistakenly classified as adherent by their clinician (Parker et al, 2007). Despite this, patients were adherent 79% of the time, very close to the 80% threshold of optimal adherence (Platt AB et al, 2008).

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Persistence with VKAs

Average persistence with VKAs has been found to vary between 4 and 35 months (Gomes et al, 2012, Deitelzweig et al, 2013). When interpreting these results it is important to account for the duration of individual studies, many only following up over a 1 year period.

The ARISTOTLE trial reported that 27.5% of participants receiving warfarin discontinued therapy early, while 17% of VKA patients in RE-LY discontinued by 2 years (Granger et al, 2011, Connolly et al, 2009). In routine practice this is not the case. Chen’s study of VTE patients with high risk of recurrence, where it was unlikely for patients to be instructed by their clinician to discontinue therapy, reported 51.5% of patients stopped VKA therapy within 1 year. Those that did were almost 3 times more likely to suffer a thrombotic event than those who persisted with therapy (Chen et al, 2013), demonstrating the benefits of continued treatment.

High discontinuation rates are reported throughout the world. In Canada 61% of patients discontinued VKA therapy at 5 years and 74% had discontinued within 3 years in Australia (Gomes et al, 2012, Simons et al, 2013). At 1 year, 36% of AF patients in the U.K. discontinued VKAs (Martinez et al, 2015). Research data also demonstrates that over half of patients studied had treatment gaps indicative of discontinuation, with over a third restarting therapy at least once (Deitelzweig et al, 2013). Furthermore, Simons reports that in Australia, as many as 15% of those sampled did not claim their first VKA prescription. There is evidence to the contrary, in patients admitted to hospital for heart failure that were either already prescribed warfarin on admission or started during their admission. One year after discharge, only 7% had discontinued VKAs (Eapen et al, 2014).

What we know is that adherence rates found in major clinical trials do not translate into real life, and clinicians are often unable to identify non-adherent patients. Although it is difficult to determine the duration of therapy many patients will have, there is a large proportion of patients who discontinue VKA therapy in the early stages of chronic treatment, placing themselves at high long-term risk of thrombosis.

Limitations of Adherence Measures with VKA StudiesPrescription and claims databases are valuable tools for assessing persistence/ discontinuation and adherence on a population level. The information they offer with respect to VKAs must be considered in the following contexts: single doses can be made up from different combinations of tablet strengths, patients may be instructed to omit doses in response to high International Normalised Ratio (INR) results and the potential for unnecessary claims in the absence of prescription fees. A common limitation of studies is that they are often unable to determine whether cessation has been instigated by the patient or their physician..

Although probably the most accurate measure, direct sampling is both costly and inconvenient for patients. INR is effective with VKAs both short term and long term. A single reading will indicate whether or not VKAs have been consumed in the recent past. Longer term, TTR can be used as a proxy for assessing adherence. However, this has its limitations as there may be other factors that contribute to low TTR (Kimmel et al, 2007, Rose et al, 2013). TTR is also the most routinely available tool for clinicians to gauge a patient’s adherence. This is a potentially unique aspect of VKA therapy. TTR provides HCPs with the opportunity to assess an individual patient’s adherence, discuss potential adherence barriers they are facing, provide educational assistance where required and support them in their long-term anticoagulant use. INR monitoring also provides patients themselves

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with objective feedback of their medicines taking behaviour, giving them the opportunity to reflect upon it and alter their behaviour accordingly, if counselled correctly on how INR readings are interpreted. The literature suggests that some patients find this beneficial.

Barriers and Predictors of Non-Adherence to VKAsCompared to many other drug therapies, VKAs place unique burdens on both patients and healthcare providers. Their narrow therapeutic index, extensive drug-drug interactions and drug-food interactions, leads to significant inter-patient variability in response, making safe and effective dosing of VKAs challenging. Therapeutic doses between patients can vary by up to a factor of 10 to achieve the same INR (Hirsh et al, 2001). Many patients are unable to consistently maintain optimal INR control with TTR >65% considered to be good (Camm et al, 2012).

Clinical & socio-demographic demographic factors

Younger patients are less likely to persist and adhere with VKA therapy compared to those over the age of 75 or retired (Platt AB et al, 2008, Gomes et al, 2012, Deitelzweig et al, 2013, Zalesak et al, 2013). Patients under the age of 45 with AF or VTE were more likely to be under-anticoagulated, which may be associated with sub-optimal adherence (Macedo et al, 2015). Although some studies have determined men were more likely to discontinue early (Gomes et al, 2012, Arnsten et al, 1997), others have found no gender difference (Chen et al, 2013). Gumbinger reports that women were more than twice as likely as men to be non-adherent; however, following multivariate analysis there were other factors that accounted for this finding and gender was no longer found to be significant (Gumbinger et al, 2015). In VTE, a retrospective analysis reports that men were more likely to be over-anticoagulated and women under-anticoagulated, no gender gap was found in AF (Macedo et al, 2015). Low-socioeconomic status has been found to be a predictor of poor persistence but this is not a consistent theme in the literature (Platt AB et al, 2008, Macedo et al, 2015).

There are conflicting reports on the effect that co-morbidities have on adherence to VKAs. Whilst some report that an increasing number of co-morbidities improves adherence (Deitelzweig et al, 2013), others have found the reverse to be true (Zalesak et al, 2013). An increasing CHADS2 score, in itself an accumulation of co-morbidities (Gage et al, 2004), was predictive of greater persistence with VKAs in some studies (Gomes et al, 2012, Deitelzweig et al, 2013), but not in all (Zalesak et al, 2013, Gumbinger et al, 2015).

Applying the COM-B model, barriers and predictors of adherence will be presented with respect to VKA therapy according to the categories specified in the model, to illustrate what interventions might be implemented in the clinic environment, when applicable to support medication adherence.

Capability

Dementia and low mental functioning are overwhelming risk factors for non-adherence (Platt AB et al, 2008, Gumbinger et al, 2015). While many patients rely on multi-dose devices to manage their medication, VKAs are not usually permitted within such devices as unpredictable dose changes can easily lead to administration of incorrect doses. This may potentially generate confusion where patients already have a high pill burden through polypharmacy; the separation of VKAs from the

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remainder of their medication raises the risk of missed doses. On realisation of a dose being missed, patients may be unaware about what action to take, whether they should take the dose or omit it until their next scheduled dose.

Opportunity

Complexity of dosing is a significant issue with VKAs. Alternate daily dosing and the use of split tablets were identified by patients as being contributors to anticoagulation related harm. The National Patient Safety Agency (NPSA) in the U.K. discouraged such regimens in their 2007 alert and suggested fixed dosing, which is considered to improve both morbidity and adherence (National Patient Safety Agency, 2007) and is supported by the literature (Orensky & Holdford, 2005, Ryan et al, 2014). Alternate daily dosing is potentially problematic for those with low health literacy or poor memory (Platt AB et al, 2008) and illustrates the potential interaction between capability and opportunity factors. However, the evidence that poor health literacy is predictive of non-adherence is not strong (Fang et al, 2006). Furthermore, patients relying on carers to administer their warfarin who may have a different carer from day to day may not be aware what dose was taken the previous day and therefore may potentially administer the incorrect dose.

Taking additional medicines has been associated with better adherence to anticoagulant therapy and may be related to the success of established medicines taking routines (Castellucci et al, 2015). Therefore although a reduction in pill burden is not always possible, a reduction in scheduling burden is a possible intervention.

Frequency of blood testing, the lack of availability of blood test centres and the delay patients face when waiting for their blood tests are barriers for both physicians and patients. Testing may be inconvenient particularly for those in jobs that have time restraints and may not wish to inform their employer about their health status and testing requirements (Arnsten et al, 1997, Gumbinger et al, 2015, Ingelgard et al, 2006, Borg Xuereb et al, 2012). Patients with poor mobility may require home visits for sampling. If this service is not available or there is limited access to it, it may be a barrier for testing and therefore safe anticoagulation management. Research suggests that patients with their own means of transport to the anticoagulation clinic had better adherence rates (Orensky & Holdford, 2005). Surprisingly, living in a nursing home was also found to be a predictor of early discontinuation in AF patients with a history of stroke (Gumbinger et al, 2015).

Motivation

Overcoming perceptions about harm related to VKAs is a major motivational barrier for both patients and clinicians. A qualitative study showed that negative associations VKAs have, such as being ‘rat poison’ or knowing people who have suffered haemorrhage has dissuaded patients from starting VKAs. With regards to clinicians, those in primary care had greater concerns than their secondary care colleagues: clinicians in secondary care reportedly did not feel the same long-term responsibility about haemorrhagic risk and were less hesitant to prescribe VKAs (Borg Xuereb et al, 2012). Whilst some practitioners may be motivated to prescribe anticoagulation, their motivation is not always in line with their capability. A survey of cardiologists, haematologists and internal medicine physicians in the U.S. showing many of them were confident in prescribing anti-thrombotic therapies in AF and VTE, however there was significantly low knowledge of current anti-thrombotic guidelines (Arepally et al, 2010).

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In summary, younger patients and men are more likely to discontinue therapy and be non-adherent to VKA therapy, whilst patients that are at the highest thrombotic risk potentially persist with therapy for longer and are more adherent. Based on the COM-B model there is scope for services to address barriers in each domain in order to enhance adherence to VKAs from optimising opportunity by simplifying dosing regimens, to raising motivation by dispelling myths surrounding VKAs.

Will Adherence Improve With The Availability of Direct Oral Anticoagulants? Since 2010, direct oral anticoagulants (DOACs) have been licensed for use in stroke prevention in AF (SPAF) and for the acute treatment and secondary prevention of VTE. With fixed dosing and predictable pharmacokinetic profiles, they are an attractive alternative to VKAs providing less day to day burdens to patients and potentially lower costs to healthcare systems. DOACs have a lower propensity for drug-drug interactions and have no dietary interactions. Accordingly, they do not require routine monitoring of anticoagulant activity (Heidbuchel et al, 2015). Their use is increasing steadily (Martinez et al, 2015, Hanemaaijer et al, 2015), with large clinical trials all showing that they are at least non-inferior to warfarin (Amin & Marrs, 2015). With the lower burden they have on both patients and healthcare systems this increase in use is likely to continue.

In the U.K., the National Institute for Health and Care Excellence (NICE) has recommended that those with a TTR <65% should be considered for DOAC therapy (NICE, 2014). While DOACs may remove certain barriers to adherence that exist with VKAs, within the existing paradigm of anticoagulation this presents new challenges. The unique monitoring aspect of VKAs is lost meaning that healthcare providers will no longer have an objective measure of the adherence of their patients, and thus not be able to objectively identify when patients are at high thromboembolic or bleeding risk.

Although the efficacy of the DOACs is not in doubt, if patients do not take the medication regularly they will be put at high risk of thromboembolism, incidences of which will only become apparent in years to come. This risk is even higher with the relatively short half-life of these agents in comparison to VKAs (Amin & Marrs, 2015). NICE have deemed DOACs to be cost effective in comparison to VKAs, however there was little consideration of non-adherence in their analysis (NICE, 2014). Despite being relatively new to the market, research is rapidly emerging on adherence and persistence to DOACs.

Adherence to DOACs

High adherence rates are frequently reported in the literature with respect to DOACs. Smaller studies have reported that greater than 85% of patients were adherent to dabigatran (Schulman et al, 2013, Hu et al, 2015), with Schulman’s study showing a median adherence rate of 99.7% as calculated by medicines possession ratio (MPR). Perhaps the most significant finding of Hu’s study, which is supported by research by Suryanarayan and colleagues, was that the majority of those who were identified as being non-adherent by questionnaire and interviewing had high prescription filling rates (Suryanarayan et al, 2015). This highlights the importance of utilising multiple methods to determine adherence.

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Database analyses in the U.S. show average adherence rates according to proportion of days covered (PDC) of >80% for rivaroxaban, dabigatran and apixaban; where rivaroxaban showed the greatest level of adherence (Shore et al, 2014, Crivera et al, 2015). However, what was of concern, was more than a quarter of patients were found to be non-adherent (Shore et al, 2014). Further cause for concern can be found with Zhou’s study reporting 41.3% of patients were non-adherent to dabigatran at 6 months, increasing to 49.2% at 12 months. Mean MPR was 0.73 and 0.65 at 6 and 12 months respectively (Zhou et al, 2015). Analyses from western European populations show similar trends where more than three quarters of patients prescribed DOACs were non-adherent according to PDC, while in a separate study only a third of patients had a PDC of 100% (Hanemaaijer et al, 2015, Gorst-Rasmussen et al, 2015). In Canada, 42.9% of patients reported to be non-adherent to DOACs, which was equivalent to non-adherence rates with VKAs (Castellucci et al, 2015).

Persistence with DOACs

RE-LY reports 21% of participants discontinued dabigatran at 2 years, compared to 17% of warfarin patients (Connolly et al, 2009). In ARISTOTLE, 25.3% of patients discontinued apixaban compared to 27.5% stopping warfarin (p=0.001) (Granger et al, 2011).

Elsewhere, Thorne reports 30% of AF or VTE patients discontinued dabigatran between 3 and 12 months of initiation (Thorne et al, 2014). Prescribing data from New Zealand shows that as many as 45% of patients discontinued dabigatran within 12 months of starting therapy (Harper et al, 2015), while in the U.S., 31.5% of patients were non-persistent with dabigatran at 6 months, increasing to nearly half of patients by 1 year (Zhou et al, 2015).

The DRESDEN registry for rivaroxaban in AF reported 18.5% of patients discontinued therapy early. Median treatment duration was 544 days over the 3.5 year (approximately 1200 days) observation period and the highest risk period for discontinuation was the first 6months (Beyer-Westendorf et al, 2014). Direct comparisons show rivaroxaban had significantly higher persistence at 6 months compared to VKAs. However the mean observation period in the rivaroxaban cohort was 83 days, less than 3 months (Laliberte et al, 2014). In the U.K., DOAC discontinuation at 1 year was nearly half that of VKAs. Investigators in the U.K. and U.S. have found that currently persistence with rivaroxaban is greater than with dabigatran (Martinez et al, 2015, Nelson et al, 2015).

In summary, the available data on DOACs generally shows similar and in some cases, improved rates of adherence relative to VKAs. However, there are warning signs with similar levels of early cessation of therapy and studies using self-reporting tools report lower adherence rates, similar to VKAs, compared to the larger claims database studies.

Barriers and predictors of non-adherence to DOACs

To date, factors identified to be related to adherence to DOACs have been non-modifiable, i.e. being predominantly clinical and socio-demographic in nature. As such there is limited scope for applying the COM-B model.

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As with VKAs, men have been found to be more likely to discontinue or be non-adherent to therapy (Castellucci et al, 2015, Zalesak et al, 2013, Harper et al, 2015) as have younger patients (Shore et al, 2014, Zhou et al, 2015, Harper et al, 2015, Nelson et al, 2015). Taking multiple medications has been associated with higher adherence rates (Castellucci et al, 2015, Nelson et al, 2015), while patients prescribed dabigatran with a history of warfarin use were more adherent than anticoagulation naïve patients (Shore et al, 2014).

Co-morbidities associated with worsening adherence to DOACs include congestive heart failure, COPD, diabetes, cardiovascular disease, depression and alcohol abuse (Zalesak et al, 2013, Shore et al, 2014, Beyer-Westendorf et al, 2014). Many of these diseases are risk factors for stroke, however increasing stroke risk in AF patients according to CHADS2 score is associated with improved DOAC adherence to both dabigatran and rivaroxaban (Martinez et al, 2015, Zalesak et al, 2013, Shore et al, 2014, Zhou et al, 2015, Nelson et al, 2015), demonstrating the conflict in the messages the literature currently gives.

Thorne showed that most patients who discontinued dabigatran did so due to gastro-intestinal (GI) side effects and lack of a direct reversal agent. The latter potentially demonstrating low motivation in some patients to persist with dabigatran when there was a reversible alternative available. Clinicians stopped dabigatran due to worsening renal function, myocardial infarction (MI) and haemorrhage. Others have also found that haemorrhage is major reason for dabigatran and rivaroxaban discontinuation (Zalesak et al, 2013, Thorne et al, 2014, Beyer-Westendorf et al, 2014). The DRESDEN registry identified that 9.9% of patients discontinued rivaroxaban as they had reverted to sinus rhythm, a reason that is rarely reported. A claims database analysis between 2010- 2012, showed that over a 1 year period 10.5% of dabigatran patients changed therapy, primarily to warfarin. It must be noted that other DOACs were in the process of being licensed, becoming available partway through the study period. The mean time to switch was 126.4 days (SD=114.6 days), with such a large standard deviation it must be assumed that a significant number of patients switched early in their therapy (Zhou et al, 2015). The reasons behind switching were not reported. Concerns surrounding reversal of direct anticoagulants will be allayed with the emergence of idarucizumab and andexenat alfa, the former being recently licensed in Europe and the U.S. (Boehringer Ingelheim Limited, 2015, Siegal et al, 2015).

Risk factors for discontinuation of DOAC therapy appear to be similar to those with VKAs. There is clearly a gap in the literature for investigations examining the modifiable risk factors for non-adherence to DOACs. Such studies will provide opportunities for evidence based interventions to support long term adherence to DOACs. While some studies are emerging, there is opportunity to learn from previous research with VKAs.

What Can Be Done to Improve Adherence to AnticoagulationHow healthcare providers can improve adherence and respond to the new paradigm posed by the use of DOACs is a question that has been raised by many (Ten Cate, 2013, Di Minno et al, 2014, Nerini et al, 2013). Various stakeholders around the world are in agreement that monitoring and follow-up is vital for patients prescribed DOACs. This includes the assessment of adherence and the reinforcement of the importance of adherence in maintaining therapeutic anticoagulation (Heidbuchel et al, 2015, Gladstone et al, 2015).

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Studies have also investigated the effect of health beliefs on adherence. Targeting and modifying known beliefs also provides scope for intervention. In other diseases, including stroke and cardiovascular disease, health beliefs have been predictive of adherence to medication (O'Carroll et al, 2011, Sjolander et al, 2013, Ross et al, 2004). We present relevant interventions and studies targeting both VKA and DOAC patients and the risk factors they address according to the COM-B model.

Targeting Capability

Educational interventions are highly popular interventions, particularly as they feed into the prevalent model of patient centred care (Epstein & Street, 2011). They intend to enhance a patient’s capability to understand and engage in therapy.

A trial involving AF patients prescribed VKAs found that those who received decision aids related to anticoagulation therapy had improved INR control at 3 months compared to those receiving usual care. However, at 12 months control reverted to baseline levels (McAlister et al, 2005). Elsewhere, despite improving knowledge, decision aids did not improve persistence (Man-Son-Hing et al, 1999).

The TREAT trial delivered an educational programme to AF patients improving TTR at 6 months. However at 12 months, the intervention and normal care groups had comparable TTRs (Clarkesmith et al, 2013b). The ongoing AEGEAN study has reported that intensive education on the initiation of apixaban for AF did not improve adherence in comparison to the normal stream of care over the study period (Montalescot, 2015). TREAT has demonstrated how negative illness perceptions and medication beliefs can be changed over time to beliefs associated with better adherence; unfortunately, this did not translate into improved TTR. Although not significant in the final pooled analysis, a Cochrane review found that educational interventions alone had a trend to improve anticoagulation control with VKAs (Clarkesmith et al, 2013a). Increasing capability through better self-management reduced mortality in patients taking VKAs (Ryan et al, 2014).

Targeting Opportunity

Adherence patterns of newly diagnosed AF patients has been assessed, showing patients were more likely to both adhere and persist to a once daily rather than twice daily co-prescriptions of antihypertensive or diabetes treatments (Laliberte et al, 2012). In this study, patients were ineligible for entry to the study, if they were on medicines which comprised both once or twice daily frequencies, and the study authors have not clearly indicated whether these patients were also prescribed anticoagulation. Therefore, these results should be viewed with caution when attempting to predict adherence to once or twice daily anticoagulation regimens. There are suggestions patients are likely to be more adherent to the once daily regimens of rivaroxaban and edoxaban in comparison to apixaban and dabigatran which are prescribed twice daily (Rubboli, 2015). While there may be some merit in this, it is important to consider the anticoagulation regimen in the context of all the patient’s prescribed medication. An established routine with other twice daily medication reduces the likelihood non-adherence with twice daily anticoagulants (Nelson et al, 2015). Also by the same logic, adherence to VKAs should also be superior to apixaban and dabigatran.

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Greater support, through monitoring and follow-up, for patients prescribed DOACs is an emerging theme in the literature. This is important as patients may be unaware that they take their medication incorrectly. Twenty-three percent of patients prescribed rivaroxaban admitted taking it on an empty stomach leading to sub-optimal therapy. Of the dabigatran patients contacted, 14% were storing it incorrectly (Simon et al, 2015). These issues can be addressed with more involvement from specialist services and prolonged follow-up. Shore determined that longer monitoring periods with enhanced pharmacist involvement was associated with improved adherence to dabigatran. Appropriate selection of patients by prior detailed assessment of medical histories, adherence to warfarin and other twice daily medicines improved dabigatran adherence (Shore et al, 2015).

Targeting Motivation

With regards to beliefs, AF patients commonly attribute their disease to psychological causes like stress or worry rather than lifestyle factors. This has been found to be positively linked with depression and dejection in patients with symptomatic AF, as has a strong belief in the negative consequences of AF to their health. Symptomatic AF patients who believed AF took a cyclical course experienced greater tension and anxiety (McCabe PJ et al, 2011, McCabe PJ & Barnason SA, 2012). Although such beliefs may lower motivation to adhere, in stroke sufferers, these perceptions were not found to be related to medication adherence (O'Carroll et al, 2011, Sjolander et al, 2013).

Patients with a previous history of VTE had stronger sense of risk and believed that VTE had more severe consequences than those who were at high risk of VTE but had not experienced any thrombotic event. The more symptoms these patients attributed to their illness, the greater their risk perception (Lane DA et al, 2009). These beliefs could be indicative of greater motivation in the symptomatic VTE patient to adhere to treatment.

A small study of AF patients from ethnic minorities in the U.K. reported that Indo-Asian patients had an externalised perception of control such as ‘God’ whereas Afro-Caribbean and White patients believed control lay either with themselves or with their doctor. In this cohort, 61% of patients declared they did not think AF was serious but 50% of patients reported warfarin concerned them (Lip et al, 2002). Motivation to adhere may be lower in those who believe that their course of illness is controlled by an entity other than themselves. Similarly believing that AF does not have serious consequences suggests motivation to adhere will be low in such patients.

Strong concerns about treatment are likely to reduce motivation. High levels of concern about a specific medication was associated with poor adherence in stroke patients, although not all of these patients received anticoagulation. Stroke patients who did not recognise the necessity of their medication had poorer adherence in O’Carroll’s study (O'Carroll et al, 2011); however, Sjölander found no association between adherence and necessity in a similar cohort. Patients who believed that medication in general had a high potential to cause harm and that medicines were overused were non-adherent with their secondary prevention of stroke medicines (Sjolander et al, 2013).

The TREAT trial reports that AF patients had relatively high concerns about VKAs and believed medicines were overused by health professionals. Beliefs that medication in general can cause harm

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at 1 month were inversely related to TTR at 6 months while having a strong sense of needing anticoagulation was positively associated with TTR at 1 year (Clarkesmith et al, 2013b). In Korea, high satisfaction scores were predictive of good adherence and concerns decreased with increasing duration of therapy (Wang et al, 2014).

Incentives have been used in an attempt to increase patient motivation and improve adherence. In Kimmel’s lottery trial, patients had the opportunity to receive a financial reward if they opened their MEMs devices appropriately. However, compared to those with no potential financial gain, this incentive did not improve adherence to VKAs or TTR (Kimmel et al, 2012). The small financial reward on offer not increasing motivation sufficiently to effect adherence.

Powerful interactions between capability, opportunity and motivation on behaviour

Targeting a single domain of the COM-B model has not always reaped rewards in adherence interventions in anticoagulation. Whilst each domain of the model directly influences behaviour, there is also an interaction between domains that may make it necessary to target more than one domain to bring about behavioural change.

Patients receiving an educational intervention followed by anticoagulation testing and management from specialist services had increased dissatisfaction and distress compared to baseline. Increasing capability but not opportunity, leading to a decrease in motivation for patients. In contrast, those who were self-monitoring with or without self-management showed improved treatment satisfaction and self-efficacy i.e. the perception of one’s ability to effectively manage their illness (Gadisseur et al, 2004). Here both capability and opportunity were enhanced resulting in increased motivation. Compared to the usual stream of care, educational interventions alone did not improve TTR. However, education plus self-monitoring with and without self-management did improve (Gadisseur et al, 2003).

With regards to patient safety targeting capability and opportunity with self-monitoring and self-management with VKAs lowers incidence of adverse events (Ryan et al, 2014). Interventions that involved patient self-monitoring in combination with education displayed an improved trend towards better anticoagulation control although this was not significant in the final Cochrane analysis (Clarkesmith et al, 2013a).

ConclusionWhilst the literature presents an inconsistent picture concerning both the determinants and levels of non-adherence to VKAs and DOACs, moving forward there are clear messages for anticoagulation service providers. Early indications suggest that DOACs appear to have favourable persistence and adherence rates in comparison to VKAs, however more studies are needed using multiple measures of adherence to enable us to truly understand whether or not adherence is significantly improved with direct agents, particularly longer-term. This is of particular importance with the new paradigm anticoagulation is facing where patients are likely to be switched to a DOAC with minimal follow up. If modifiable risk factors for sub-optimal adherence are not addressed, one might predict worsened clinical outcomes in the long-term.

[12]

The challenge is to develop the means to identify such patients and modify medicines taking behaviour. Through utilising the COM-B model of adherence, we are presented with modifiable risk factors for non-adherence. Many of which do not require significant resources or service developments to address, but can be easily accounted for on a patient by patient basis in a normal clinical setting.

The importance of adherence to patient health cannot be under-estimated. As we are famously reminded by former U.S. surgeon general, Dr Koop; “Drugs don't work in patients who don't take them” (Osterberg & Blaschke, 2005).

Disclosure of Interests

Professor Arya has received honoraria for lectures and travel Bayer, Boehringer Ingelheim and Pfizer, and awards for investigator sponsored research from Bayer and Covidien. Dr Auyeung and Dr Patel have received investigator initiated research funding from Bayer. John Abdou has no disclosures.

[13]

[14]

Figure 1: Capability, Opportunity, Motivation (COM)-B Model of Adherence. Adapted from Jackson et.al. 2014

Behaviour(e.g. Adherence)

CapabilityPsychologicalAbility to engage in the required thought processesCognitive functioningAbility to calculate appropriate combination of tablets

PhysicalAbility to visually differentiate various tablet strengthsAbility to open medication packagingBeing able to travel to clinic

MotivationReflectiveEvaluation and planningMedication beliefsConcern of side effects and bleedingSense of neccessity for anticoagulation

AutomaticEmotions generated from previous experience and personal behavioursExperience of thrombosis or haemorrhage Illess beliefs

OpportunityPhysicalAvailability and provision of healthcare servicesSelf-monitoringRegimen complexityClinical follow up

SocialCultural norms and beliefs that influence behaviourDesire to not disclose illness to employers, friends or family

[15]

Table 1: Description of common methods used to measure adherence, and their strengths and limitations

Method Description Strengths LimitationsDirect Sampling

Plasma levels of drug or relevant biochemical marker

Accurate measure of drug level

Infrequent sampling Lack of availability of

reliable reproducible assay Expensive

Surrogate measure of clinical response

Clinically relevant measure of treatment efficacy

No guarantee of drug consumption

High resource burden with clinic time

ExpensivePill Count Patient presents

medication for countingSimple to implement and inexpensive

Reliant on patient presenting all medication

Self Reporting Use of questionnaire Readily available validated tools can be used

Subject to patient reporting bias

Validation of tools is in specific patient groups

Reporting number of missed tablets in given timeframe

Inexpensive Subject to patient reporting bias, often leading to over reporting.

Database Calculation of quantity of prescriptions claimed over defined period Medicines possession

ratio (MPR) Proportion of days

covered (PDC)

Insight into population trends of persistence and adherence

Analyses prescription claim events, not actual drug consumption

Dependent on correct coding of databases

Does not capture out of pocket payments of drugs (insurance databases)

Electronic Monitoring

Electronic devices measuring cap openings Medication Event

Monitoring Systems (MEMS)

Real time observation of medication use

Gold standard for clinical trials

Measures cap openings and not drug consumption

Expensive Subject to observation bias

Ingestible sensors embedded within drug formulation

Real time observation of medication use

Detects drug consumption

Expensive Reliant on patient wearing

patch to receive signal confirming consumption

Licensed in U.S. only

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