Discussion

Consistent with the high potential for cognitive decline and vascular dementia7 in a typical cohort of 260 older individuals hospitalised with chronic AF, we found that a high proportion of our study cohort (approximately two-thirds) displayed concurrent MCI. Even with a more stringent definition (ie, lowering the qualifying MoCA score to <24) the underlying prevalence of MCI was approximately half. Deficits in executive functioning, visuospatial abilities and short-term memory were common, as were overlapping deficits across multiple cognitive domains. Those found to have MCI were more likely to have lower education levels and the highest thromboembolic risk (primarily calculated on the basis of more advanced age and cardiovascular insults) while being most likely to be prescribed agents with the narrowest benefit-to-risk ratios.

As such, these data confirm previous studies suggesting high levels of cognitive dysfunction in those with AF.8 ,9 They also represent an important clinical warning, particularly given that the risk factors we identified are frequently seen in those being treated for AF. The role of digoxin, a commonly prescribed drug in the presence of AF, but with clear potential for toxicity and associated mental confusion in older individuals,20 ,21 in potentially contributing to MCI in this cohort is also an important finding. Low digoxin levels (0.5–0.8 µg/l) have been shown to be associated with better clinical outcomes (including cognitive functioning) and lower mortality.21 However, cognitive impairment has also been found to occur at levels of serum digoxin that are within the therapeutic range.20 ,22 In the current study, those with MCI and concurrent digoxin levels had lower body weight but greater digoxin levels, potentially reflecting higher tissue saturation and, thus, digoxin toxicity albeit at a therapeutic level. Recommendations have been made outlining the need for digoxin withdrawal in any patient displaying cognitive symptoms, even when the serum digoxin level is within the normal therapeutic range.20

Our findings are of obvious clinical concern in AF due to the heavy reliance on self-care behaviours crucial to managing this complex chronic disease successfully. Inadequate self-care has been identified as a major contributor to hospital readmission and to the poor outcomes associated with heart failure;23 the same can be assumed for AF. Cause and effect is difficult to delineate, however. Individuals with MCI may have problems performing complex tasks such as preparing meals or paying bills while still being able to perform activities of daily living (and thus maintain a sense of independence).24 The ability to learn and acquire knowledge is also influenced by MCI, and lack of knowledge about chronic disease and self-care has been shown to result in poor self-care maintenance and management behaviours.25 Intact executive functions have been identified as crucial for the management of chronic conditions.26 Impaired executive functioning limits an individual's ability to recognise symptoms and make decisions related to symptom management.27 These were all factors that influenced the subsequent management of those allocated to the intervention arm of SAFETY—with incremental surveillance and support applied according to a traffic-light classification system28 that ‘titrates’ post-discharge support in this context using more than just clinical parameters to determine an increased risk of suboptimal health outcomes. In this case, the presence of MCI signified a need to modify expectations of ‘self-care’ and increase the role of caregivers and other healthcare providers (eg, incremental community pharmacist support and/or use of medicine reminder tools). Whether this was sufficiently robust in terms of additional support will be a major factor in determining the overall success of the home-based, nurse-led intervention being compared to usual post-discharge care.15

Beyond the impact of the SAFETY intervention, these data have immediate clinical implications. Screening of MCI can and should be used as a risk delineation strategy to add to the suite of risk stratification tools available for the assessment of patients with AF. Pitfalls of therapy, clinical lability or adverse behaviours can be identified and highlighted, triggering increased patient surveillance for those at high risk of instability or events. Increased surveillance is often required to promote treatment adherence and self-care management. In addition, education focussed on addressing peripheral risk factors can be tailored according to individual needs. Furthermore, AF-specific interventions should be focused on strengthening the areas of cognition most commonly affected. In their recent systematic review, Eggermont et al6 suggested that MCI may be addressed simply by optimising cardiac treatment. The incorporation of physical activity programmes into interventions has also been suggested due to positive improvements in cognition observed in patients with CAD and chronic heart failure.6 ,29 In addition to the screening and detection of MCI, identifying associated independent predictors of lower education level, higher thromboembolic risk and the use of digoxin allows the potential to address or increase surveillance on these peripheral factors in an attempt to prevent the development of MCI and vascular dementia in those who present with AF.

There are a number of limitations that require comment. First, we applied only one clinical assessment tool to assess cognitive function. While the MoCA has been shown to be more sensitive and specific at discerning cognitive deficits than the MMSE, more extensive psychometric testing in addition to the testing of functional capacity would be required to confirm the presence of MCI.18 However, the MoCA could reasonably constitute an initial screening of MCI due to its short length (approximately 10 min) and ease of administration. It is also publicly available for use. The decision to use less than 26 as the cut-off score for defining MCI also influences this research, particularly when such a large decrease in the proportion of classified MCI within this population was observed in the sensitivity analysis. Whether a more stringent cut-off of less than 24 is required for more specific determination of the prevalence in these patients should be the subject of further research. The exclusion of non-English-speaking individuals may have introduced selection bias, although English competency is necessary for comprehensive understanding of the test questions. Furthermore, the often chaotic acute clinical setting under which the MoCA was conducted in addition to the patients’ altered health state may not represent an ideal testing situation. Moreover, the duration and cause of MCI is not known and cannot be distinguished in this cohort. Finally, despite the selection of a ‘real world’ cohort, we cannot discount the possibility of bias in study selection and these data may not readily apply to other hospitalised cohorts.

Despite these limitations, these data have important clinical implications for the post-discharge management of patients with chronic AF. We found that MCI is highly prevalent in typically older patients who have been hospitalised with chronic AF before discharge. Overall, cognitive deficits are abundant in this population. Beyond education levels, MCI correlated with increasing thromboembolic risk and the use of digoxin. In addition, residual levels of MCI remain high in these individuals post-discharge. Therefore, we would recommend that patients with AF should be routinely screened for MCI (as a potential source of risk delineation), and subsequent disease management programmes implemented in the setting of AF (as has been recommended in relation to heart failure management)30 should be modulated if MCI is detected.