Medication Profiles of Patients with Cognitive Impairment and High Anticholinergic Burden

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Abstract

<div class="section"> <a class="named-anchor" id="S1">  </a> <h5 class="section-title" id="d7435502e168">Background</h5> <p id="P1">Drugs with anticholinergic properties are considered potentially inappropriate in patients with cognitive impairment because harms – including delirium, falls and fractures – may outweigh benefits. </p> </div><div class="section"> <a class="named-anchor" id="S2">  </a> <h5 class="section-title" id="d7435502e173">Objective</h5> <p id="P2">To highlight opportunities to improve clinical decision making and care for patients with cognitive impairment and multiple chronic conditions, we identified distinct subgroups of patients with mild cognitive impairment (MCI) and dementia who had high cumulative anticholinergic burden and specific patterns of anticholinergic use. </p> </div><div class="section"> <a class="named-anchor" id="S3">  </a> <h5 class="section-title" id="d7435502e178">Patients and Methods</h5> <p id="P3">Retrospective cohort study <b>in a n</b>ot-for-profit, integrated delivery system. Participants included <b>community</b>-dwelling adults aged 65 and older (N=13,627) with MCI or dementia and at least 2 other chronic diseases. We calculated the Anticholinergic Cognitive Burden (ACB) score for each participant from pharmacy and electronic health record (EHR) data. Among individuals with a mean 12 month ACB score ≥2, we used agglomerative hierarchical clustering to identify groups, or clusters, of individuals with similar anticholinergic prescription patterns. </p> </div><div class="section"> <a class="named-anchor" id="S4">  </a> <h5 class="section-title" id="d7435502e189">Results</h5> <p id="P4">3,257 (24%;) had high anticholinergic burden, defined as an ACB score ≥2. Clinically meaningful clusters based upon anchoring medications or drug classes included: a cluster of cardiovascular medications (n = 1497; 46%); two clusters of antidepressant medications (n = 633; 20%); and a cluster based on use of bladder antimuscarinics (n = 431; 13%). Several clusters were comprised of multiple central nervous system-active drugs. </p> </div><div class="section"> <a class="named-anchor" id="S5">  </a> <h5 class="section-title" id="d7435502e194">Conclusions</h5> <p id="P5">Cardiovascular and central nervous system-active medications comprise a substantial portion of anticholinergic burden in people with cognitive impairment and multiple chronic conditions. Antidepressants were highly prevalent. Clinical profiles elucidated by these clusters of anticholinergic medications can inform targeted approaches to care. </p> </div>

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Most cited references 28

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The Anticholinergic Drug Scale as a measure of drug-related anticholinergic burden: associations with serum anticholinergic activity.

Claire Pollock, K Culp, Ryan M Carnahan … (2006)

Anticholinergic Drug Scale (ADS) scores were previously associated with serum anticholinergic activity (SAA) in a pilot study. To replicate these results, the association between ADS scores and SAA was determined using simple linear regression in subjects from a study of delirium in 201 long-term care facility residents who were not included in the pilot study. Simple and multiple linear regression models were then used to determine whether the ADS could be modified to more effectively predict SAA in all 297 subjects. In the replication analysis, ADS scores were significantly associated with SAA (R2 = .0947, P < .0001). In the modification analysis, each model significantly predicted SAA, including ADS scores (R2 = .0741, P < .0001). The modifications examined did not appear useful in optimizing the ADS. This study replicated findings on the association of the ADS with SAA. Future work will determine whether the ADS is clinically useful for preventing anticholinergic adverse effects.

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Patterns of chronic multimorbidity in the elderly population.

Laura Fratiglioni, Debora Rizzuto, Xin-Chao Wang … (2009)

To describe patterns of comorbidity and multimorbidity in elderly people. A community-based survey. Data were gathered from the Kungsholmen Project, a urban, community-based prospective cohort in Sweden. Adults aged 77 and older living in the community and in institutions of the geographically defined Kungsholmen area of Stockholm (N=1,099). Diagnoses based on physicians' examinations and supported by hospital records, drug use, and blood samples. Patterns of comorbidity and multimorbidity were evaluated using four analytical approaches: prevalence figures, conditional count, logistic regression models, and cluster analysis. Visual impairments and heart failure were the diseases with the highest comorbidity (mean 2.9 and 2.6 co-occurring conditions, respectively), whereas dementia had the lowest (mean 1.4 comorbidities). Heart failure occurred rarely without any comorbidity (0.4%). The observed prevalence of comorbid pairs of conditions exceeded the expected prevalence for several circulatory diseases and for dementia and depression. Logistic regression analyses detected similar comorbid pairs. The cluster analysis revealed five clusters. Two clusters included vascular conditions (circulatory and cardiopulmonary clusters), and another included mental diseases along with musculoskeletal disorders. The last two clusters included only one major disease each (diabetes mellitus and malignancy) together with their most common consequences (visual impairment and anemia, respectively). In persons with multimorbidity, there exists co-occurrence of diseases beyond chance, which clinicians need to take into account in their daily practice. Some pathological mechanisms behind the identified clusters are well known; others need further clarification to identify possible preventative strategies.

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The Prevalence of Mild Cognitive Impairment in Diverse Geographical and Ethnocultural Regions: The COSMIC Collaboration

Perminder Sachdev, Darren Lipnicki, Nicole Kochan … (2015)

Background Changes in criteria and differences in populations studied and methodology have produced a wide range of prevalence estimates for mild cognitive impairment (MCI). Methods Uniform criteria were applied to harmonized data from 11 studies from USA, Europe, Asia and Australia, and MCI prevalence estimates determined using three separate definitions of cognitive impairment. Results The published range of MCI prevalence estimates was 5.0%–36.7%. This was reduced with all cognitive impairment definitions: performance in the bottom 6.681% (3.2%–10.8%); Clinical Dementia Rating of 0.5 (1.8%–14.9%); Mini-Mental State Examination score of 24–27 (2.1%–20.7%). Prevalences using the first definition were 5.9% overall, and increased with age (P < .001) but were unaffected by sex or the main races/ethnicities investigated (Whites and Chinese). Not completing high school increased the likelihood of MCI (P ≤ .01). Conclusion Applying uniform criteria to harmonized data greatly reduced the variation in MCI prevalence internationally.