Next Article in Journal
“I Think Deprescribing as a Whole Is a Gap!”: A Qualitative Study of Student Pharmacist Perceptions about Deprescribing
Previous Article in Journal
Increased Knowledge of Adult-Onset Dystonia Amongst Medical Students via Brief Video Education: A Systematic Review and Cohort Study
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Geriatrics
Volume 7
Issue 3
10.3390/geriatrics7030059
geriatrics-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Potential Targets for Deprescribing in Medically Complex Older Adults with Suspected Cognitive Impairment

by
Juliessa M. Pavon
1,2,3,*,
Theodore S. Z. Berkowitz
4,
Valerie A. Smith
4,5,6,
Jaime M. Hughes
4,7,8,
Anna Hung
5 and
Susan N. Hastings
1,2,3,4
1
Department of Medicine/Division of Geriatrics, Duke University, Durham, NC 27710, USA
2
Geriatric Research Education Clinical Center, Durham Veteran Affairs Health Care System, Durham, NC 27705, USA
3
Claude D. Pepper Center, Duke University, Durham, NC 27710, USA
4
Health Services Research & Development, Durham Veterans Affairs Health Care System, Durham, NC 27701, USA
5
Department of Population Health Sciences, Duke University, Durham, NC 27701, USA
6
Department of Medicine/Division of General Internal Medicine, Duke University, Durham, NC 27710, USA
7
Department of Implementation Science, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA
8
Section on Gerontology and Geriatric Medicine, Division of Public Health Sciences, Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, NC 27103, USA
*
Author to whom correspondence should be addressed.
Geriatrics 2022, 7(3), 59; https://doi.org/10.3390/geriatrics7030059
Submission received: 12 April 2022 / Revised: 6 May 2022 / Accepted: 16 May 2022 / Published: 19 May 2022
(This article belongs to the Section Geriatric Psychiatry and Psychology)
Browse Figure
Review Reports Versions Notes

Abstract

:
Deprescribing may be particularly beneficial in patients with medical complexity and suspected cognitive impairment (CI). We describe central nervous system (CNS) medication use and side effects in this population and explore the relationship between anticholinergic burden and sleep. We conducted a cross-sectional analysis of baseline data from a pilot randomized-controlled trial in older adult veterans with medical complexity (Care Assessment Need score > 90), and suspected CI (Telephone Interview for Cognitive Status score 20–31). CNS medication classes included antipsychotics, benzodiazepines, H2-receptor antagonists, hypnotics, opioids, and skeletal muscle relaxants. We also coded anticholinergic-active medications according to their Anticholinergic Cognitive Burden (ACB) score. Other measures included self-reported medication side effects and the Pittsburgh Sleep Quality Index (PSQI). ACB association with sleep (PSQI) was examined using adjusted linear regression. In this sample (N = 40), the mean number of prescribed CNS medications was 2.2 (SD 1.5), 65% experienced ≥ 1 side effect, and 50% had an ACB score ≥ 3 (high anticholinergic exposure). The ACB score ≥ 3 compared to ACB < 3 was not significantly associated with PSQI scores (avg diff in score = −0.1, 95% CI −2.1, 1.8). Although results did not demonstrate a clear relationship with worsened sleep, significant side effects and anticholinergic burden support the deprescribing need in this population.

1. Introduction

Deprescribing—a systematic process of identifying medications to stop, taper, or substitute—has emerged as a critical consideration in high-quality medication management, especially among older adults [1]. Deprescribing interventions that aim to reduce central nervous system (CNS) medication use, especially the use of medications with anticholinergic activity, such as tricyclic antidepressants and muscle relaxants [2,3,4], may be particularly beneficial for medically complex older adults with suspected cognitive impairment. Patients with medical complexity (measured by high hospitalization risk) often have multiple chronic conditions, exhibit patterns of uncoordinated health care utilization [5,6,7], and are at further risk for unrecognized cognitive impairment (CI) [8,9]. There are many contributors to cognitive impairment in this population, not only normative cognitive decline, but also factors that affect cognition in the short term, such as medications. While there is significant evidence demonstrating the negative impact of anticholinergic medications on cognition, physical function [10], and side effects (such as blurred vision, constipation, dry mouth, falls) [11], the association with sleep quality remains overlooked [12]. Poor sleep quality may contribute to increased fatigue and reduced daytime functioning [13]. As a result, the use of medications with anticholinergic activity may further reduce patient capacity to manage illness and treatment burden [13,14,15]. This reduced capacity may be particularly consequential in older adults with suspected cognitive impairment.
Deprescribing CNS medications or potentially inappropriate medications, such as anticholinergics, can be an opportunity to improve care and important patient-reported outcomes in this population [16,17,18]. However, the outcomes for deprescribing may vary by medication classes and the number of medications. Thus, it remains uncertain which medication classes to target for the highest impact. Here we explored potential targets for deprescribing in medically complex older adults with suspected cognitive impairment by (1) describing CNS medication use and common side effects in medically complex patients with suspected CI, (2) identifying the medications contributing to anticholinergic cognitive burden (ACB), which may increase the risk for future cognitive decline, and (3) examining the relationships between ACB and sleep. Our study is among the first to explicitly examine this relationship in a high-risk population of older adult veterans, and we hypothesized that sleep quality is worse among those with higher anticholinergic burden.

2. Methods

2.1. Participants and Setting

This is a cross-sectional secondary analysis of a pilot randomized control trial of community-dwelling older adults, aged 65 years or greater, enrolled in Durham Veterans Affairs Health Care System-affiliated primary care clinics, with medical complexity and suspected cognitive impairment [19]. The purpose of this pilot trial was to test the feasibility, acceptability, usability, and perceived value of a 14-week video-delivered nurse care management program for medically complex older veterans with CI and their care partners compared with a similar telephone-based program. Medical complexity was defined as having a Care Assessment Need (CAN) score > 90, a multi-variable score predicting hospitalization risk within 1 year. CAN score is expressed as a percentile, such that these are people identified to be in the top 10% likelihood of hospitalization within 1 year [7]. Patients who had previously recognized cognitive impairment (e.g., if medical records indicated known dementia or cognitive impairment (CI) or lack of decision-making capacity) were excluded. Potentially eligible participants were screened for cognitive impairment using the modified version of the Telephone Interview for Cognitive Status (TICS-m) [20]. Those with scores between 20 and 31 (education-adjusted), a range previously correlated with mild CI, were offered enrollment. Other exclusion criteria included serious mental illness (mental, behavioral, or emotional disorder resulting in serious functional impairment) or active substance abuse, currently hospitalized or residing in a nursing home or other institutional care setting, hospice-eligible, or unable to communicate by telephone.

2.2. Measures

2.2.1. Medication Data

Medication data were collected during the first of three scheduled study nurse calls with patients (considered study baseline) and were derived from a reconciled medication list generated by a study nurse using the patient report and the electronic health record of the patient’s current medications. Medications in the following central nervous system (CNS) medication classes were recorded: antipsychotics, benzodiazepines, H2-receptor antagonists, hypnotics, opioids, and skeletal muscle relaxants. For all medications, we also identified and coded anticholinergic-active medications according to their score on the 2012 update of the Anticholinergic Cognitive Burden (ACB) scale: ACB1, possibly anticholinergic; ACB2, definitely anticholinergic; ACB3 definitely anticholinergic and associated with delirium [21]. All other drugs were assigned a score of 0 (ACB0). ACB scores were summed for each patient and then further categorized as no/low exposure to anticholinergic medications (total ACB score = 0–2) vs. high exposure (total ACB score 3+) [11,22,23].
Of note, medications in the original study were selected to meet the study aims, which was to better target care management. Thus, medication classes were included because they are either medications that are (a) included as an anticholinergic medication in the ACB scale, (b) medication classes that can impact cognition, or (c) both. For this analysis, we included all original study medications because of their potential to impact patient outcomes.

2.2.2. Side Effects and Sleep

Patients were asked by study nurses to self-report if they had any of the following symptoms or problems: blurry vision, constipation, dizziness, dry mouth, dry eyes, sleepiness, difficulty emptying the bladder, and falls. Patients could indicate having one or more of these symptoms. The Pittsburgh Sleep Quality Index (PSQI), an 18-item self-report questionnaire, (0–21, scores ≥ 5 indicate poor sleep quality) is the gold standard self-report measure to assess sleep disturbance over the prior month [24]. The items produce seven component scores which range from 0 (no difficulty) to 3 (severe difficulty): sleep duration, sleep disturbance, sleep latency, daytime disturbance, habitual sleep efficiency, sleep quality, and use of sleep medications. All self-reported measures were also collected at baseline for all participants.

2.2.3. Analysis

Descriptive statistics were calculated for all study variables. Distributions of demographics, TICS-m, side effects, and PSQI scores measures were calculated for all patients, as well as separately for those with ACB score 0–2 versus 3+. Multivariable linear regression models were used to determine the relationship between ACB score (0–2 vs. 3+) and sleep (PSQI) adjusted for age and race. Analyses were performed using SAS, version 9.0 (SAS Institute, Inc., Cary, NC, USA).

3. Results

3.1. CNS Medication Burden and Side Effects

Among 40 community-dwelling veterans with medical complexity and suspected CI (mean age 72.4 (SD 6.1), 100% male, 37.5% Black), the mean number of prescribed medications from the included classes was 2.2 (SD 1.5). Overall, 73% were taking anticholinergic-active medications (n = 29). Among the CNS medication classes, the top two most commonly prescribed were opioids (n = 13), and skeletal muscle relaxants (n = 7) (Table 1). Further, 65% of all veterans taking the included medication classes experienced one or more side effects (Table 1), with more than a quarter of the sample experiencing any one of the following: dry mouth (38%), sleepiness (33%), dry eyes (25%), and dizziness (25%).

3.2. Most Frequently Prescribed Medications with Anticholinergic Activity

The prescribed medication classes with anticholinergic activity are presented in Figure 1, and the medication names are listed in Table 2. The two most frequently prescribed medication classes with ACB score 3 were systemic antihistamines (20% of sample) and skeletal muscle relaxants (8%). The four most frequently prescribed medications were diphenhydramine (antihistamine), methocarbamol (muscle relaxant), hydroxyzine (antihistamine), and oxybutynin (antimuscarinic), with diphenhydramine or methocarbamol prescribed to more than 20% of the sample, and to 40% of those who had a total ACB score ≥ 3.
The most frequently prescribed medication class with ACB score 2 was skeletal muscle relaxant (10% of the sample), with 100% of those receiving cyclobenzaprine. Prescribed medications with ACB score 1 belonged to a diverse range of medication classes. The majority of patients with prescriptions for medications with ACB score 1 received beta-blockers (metoprolol) (40% of sample), followed by diuretics (furosemide) (18%), systemic antihistamines (cetirizine, loratadine, desloratadine) (13%), and antithrombotics (warfarin) (10%) (Figure 1 and Table 2).

3.3. Anticholinergic Burden

Approximately three out of four study participants (73%) were prescribed medications with anticholinergic activity and exposed to anticholinergic burden (total ACB score ≥ 1). Mean total ACB score at baseline was 2.6 (SD 2.1), and 50% had a total ACB score ≥ 3 (n = 20) (high burden). Characteristics of participants with a total ACB score ≥ 3 and those with a total ACB score < 3 are shown in Table 1. The most frequently prescribed individual medications to patients with a total ACB score ≥ 3 were metoprolol (beta-blocker, ACB = 1), furosemide (diuretic, ACB = 1) (combined represents 75% of patients with a total ACB score ≥ 3), or diphenhydramine (systemic antihistamine) (Table 2).

3.4. Total ACB and Association with Sleep

The mean PSQI score was 9. Overall, 95% of participants had a PSQI score of ≥5 which indicates poor sleep quality. The ACB score was not significantly associated with PSQI scores (average difference in score between veterans with ACB score ≥ 3 vs. scores < 3 = −0.1, 95% CI −2.1, 1.8).

4. Discussion

One-half of veterans with medical complexity and suspected cognitive impairment had a high anticholinergic burden, 2 out of 3 reported side effects attributable to anticholinergic medications, and the majority had worse than average sleep quality rating compared to the general population of a similar age [25]. Although results do not demonstrate a clear relationship with worsened sleep as hypothesized, significant side effect burden, overall poor sleep quality, and other known adverse effects of CNS and anticholinergic medications support the need for deprescribing in this population.
In our study, 50% of medically complex veterans with suspected cognitive impairment are exposed to the high anticholinergic burden, which is similar to proportions seen in other older adult populations, but nearly double that seen in cognitively impaired populations [26,27]. This is a troubling result given the associations between anticholinergic burden, including cumulative burden, and limitations in cognitive and physical function [3,4,10]. Studies have also revealed longitudinal impacts on dementia and poor physical functioning [28,29,30]. However, many of these studies examine the overall burden of anticholinergics and sedative CNS medications, but the individual impact of target medications is not known. Further, there is also concern about the potential misuse of these medications [31]. In our study, diphenhydramine (ACB of 3) was one of the most frequently prescribed medications in this group. According to the 2019 Beers Criteria for Potentially Inappropriate Medication Use in Older Adults, individuals 65 years or older should avoid the use of diphenhydramine [32]; however, many over-the-counter sleep products contain diphenhydramine. In a group where poor sleep quality is common, the potentially inappropriate use of diphenhydramine is likely to be high. Methocarbamol, another frequently prescribed medicine with an ACB of 3, also produces more pronounced anticholinergic side effects in older adults, such as drowsiness, dizziness, and falls, and it should be prescribed with caution [33]. In the context that our findings do not really support using total ACB score as a targeting tool for deprescribing, particularly given that some of the most common medications among those with high total ACB scores are unlikely to contribute to anticholinergic side effects (e.g., metoprolol and furosemide), individual medications with high anticholinergic activity do become important targets for deprescribing interventions, especially those prescribed without a compelling clinical indication.
One important consideration with deprescribing is identifying the right timing and setting. Deprescribing interventions aimed at reducing acute care utilization among patients with medical complexity is an example. High-risk medication may reduce overall capacity (e.g., sleep, physical activity, cognition) in medically-complex older adults with mild cognitive impairment [10,12,13,14,15], which can negatively impact the ability to manage acute health stressors, thereby increasing acute care utilization [15]. Thus, there is a need to look for opportunities aimed at reducing acute care utilization among patients with suspected cognitive impairment. Deprescribing among those with high risk for hospitalization, not scoring well on CI screening, and who have a high anticholinergic burden, for example, could be an opportunity to intervene and improve care.
Our results also cast a new light on the side effect burden among medically complex older adults. The accumulation of side effects can promote prescribing cascades, where additional medications are prescribed to treat the adverse side effects of previously prescribed medications [34]. This can lead to polypharmacy and further increases the risk for adverse drug events and future hospitalization [35]. Telehealth care management strategies, for example, can provide systematic opportunities for identifying and managing CNS medication-related side effects [19]. These side effects can be important targets for deprescribing strategies aimed at mitigating the risk for poor outcomes in medically complex patients with suspected cognitive impairment.
Limitations include involvement of a single site, male-only enrollment, and small sample size which limits generalizability and power to detect small effect sizes, and the inability to accurately determine if a veteran was taking a medication prescribed through a non-VA provider. However, this was mitigated by using a reconciled medication list using both patient reports and the electronic health record. Although comorbidity information is not available, baseline medical complexity CAN scores, Generalized Anxiety Disorder-7, and Patient Health Questionnaire-9 scores for this sample are reported elsewhere [19]. Further, self-reported side effects relied on patient recall, such that our data likely underestimates medication-related side effects.

5. Conclusions

Older veterans with medical complexity and suspected cognitive impairment have a high anticholinergic burden, as well as significant side effects attributed to medications with anticholinergic activity. A process to reduce anticholinergic-related medications, such as deprescribing interventions and care management, is warranted. Future studies in this population assessing the longitudinal impact of an anticholinergic burden on cognition, physical activity, and sleep is needed to further support deprescribing efforts.

Author Contributions

Conceptualization: J.M.P. and S.N.H.; methodology: J.M.P., T.S.Z.B., V.A.S. and S.N.H.; formal analysis: J.M.P., T.S.Z.B., V.A.S. and S.N.H.; investigation: J.M.P. and S.N.H.; resources: J.M.P. and S.N.H.; data curation: J.M.P., T.S.Z.B., V.A.S., J.M.H., A.H. and S.N.H.; writing—original draft preparation: J.M.P., T.S.Z.B., V.A.S., J.M.H., A.H. and S.N.H.; writing—review and editing: J.M.P., T.S.Z.B., V.A.S., J.M.H., A.H. and S.N.H.; visualization: J.M.P., T.S.Z.B., V.A.S., J.M.H., A.H. and S.N.H.; supervision: S.N.H.; project administration: J.M.P. and S.N.H.; funding Acquisition: J.M.P. and S.N.H. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the United States (U.S.) Department of Veterans Affairs, Health Services Research and Development Service (PPO 15-425; HX002030-01A1); the Center of Innovation to Accelerate Discovery and Practice Transformation (CIN 13-410) at the Durham VA Health Care System (Hastings); and NIA K23 AG058788-01A1 (Pavon). The funding agency had no role in the design or conduct of the study; collection, analysis, or interpretation of the data; or preparation, review, or approval of the manuscript. The contents do not represent the views of the U.S. Department of Veterans Affairs or the United States Government.

Institutional Review Board Statement

The study was conducted in accordance with the guidelines of the Declaration of Helsinki, and approved by the Institutional Review Board of the Durham VA Health Care System.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Data available on request due to restrictions, e.g., privacy or ethical concerns.

Acknowledgments

This work is dedicated to the memory of Elizabeth P. Mahanna, MPH.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Scott, I.A.; Gray, L.C.; Martin, J.H.; Pillans, P.I.; Mitchell, C.A. Deciding when to stop: Towards evidence-based deprescribing of drugs in older populations. Evid. Based Med. 2013, 18, 121–124. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  2. Wright, R.M.; Roumani, Y.F.; Boudreau, R.; Newman, A.B.; Ruby, C.M.; Studenski, S.A.; Shorr, R.I.; Bauer, C.D.; Simonsick, E.M.; Hilmer, S.N.; et al. Health Aging and Body Composition Study. Impact of Central Nervous System (CNS) Medication Use on Cognition Decline in Community Dwelling Older Adults: Findings from the Health, Aging and Body Composition Study. J. Am. Geriatr. Soc. 2009, 57, 243–250. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  3. Gray, S.L.; Anderson, M.L.; Dublin, S.; Hanlon, J.T.; Hubbard, R.; Walker, R.; Yu, O.; Crane, P.K.; Larson, E.B. Cumulative use of strong anticholinergics and incident dementia: A prospective cohort study. JAMA Intern. Med. 2015, 175, 401–407. [Google Scholar] [CrossRef] [PubMed]
  4. Coupland, C.A.C.; Hill, T.; Dening, T.; Morriss, R.; Moore, M.; Hippisley-Cox, J. Anticholinergic Drug Exposure and the Risk of Dementia: A Nested Case-Control Study. JAMA Intern. Med. 2019, 179, 1084–1093. [Google Scholar] [CrossRef]
  5. Johnson, T.L.; Rinehart, D.J.; Durfee, J.; Brewer, D.; Batal, H.; Blum, J.; Oronce, C.I.; Melinkovich, P.; Gabow, P. For many patients who use large amounts of health care services, the need is intense yet temporary. Health Aff. 2015, 34, 1312–1319. [Google Scholar] [CrossRef]
  6. Doran, K.M.; Raven, M.C.; Rosenheck, R.A. What drives frequent emergency department use in an integrated health system? national data from the Veterans Health Administration. Ann. Emerg. Med. 2013, 62, 151–159. [Google Scholar] [CrossRef]
  7. Wang, L.; Porter, B.; Maynard, C.; Evans, G.; Bryson, C.; Sun, H.; Gupta, I.; Lowy, E.; McDonell, M.; Frisbee, K.; et al. Predicting risk of hospitalization or death among patients receiving primary care in the Veterans Health Administration. Med. Care 2013, 51, 368–373. [Google Scholar] [CrossRef]
  8. Plassman, B.L.; Langa, K.M.; Fisher, G.G.; Heeringa, S.G.; Weir, D.R.; Ofstedal, M.B.; Burke, R.J.; Hurd, D.M.; Potter, G.G.; Rodgers, W.L.; et al. Prevalence of cognitive impairment without dementia in the United States. Ann. Intern. Med. 2008, 148, 427–434. [Google Scholar] [CrossRef] [Green Version]
  9. Andrews, J.S.; Desai, U.; Kirson, N.Y.; Enloe, C.J.; Ristovska, L.; King, S.; Birnbaum, H.G.; Fleisher, A.S.; Ye, W.; Kahle-Wrobleski, K. Functional limitations and health care resource utilization for individuals with cognitive impairment without dementia: Findings from a United States population-based survey. Alzheimers Dement. 2016, 6, 65–74. [Google Scholar] [CrossRef]
  10. Fox, C.; Smith, T.; Maidment, I.; Chan, W.-Y.; Bua, N.; Myint, P.K.; Boustani, M.; Kwok, C.S.; Glover, M.; Koopmans, I.; et al. Effect of medications with anti-cholinergic properties on cognitive function, delirium, physical function and mortality: A systematic review. Age Ageing 2014, 43, 604–615. [Google Scholar] [CrossRef] [Green Version]
  11. Rudolph, J.L.; Salow, M.J.; Angelini, M.C.; McGlinchey, R.E. The Anticholinergic Risk Scale and Anticholinergic Adverse Effects in Older Persons. Arch. Intern. Med. 2008, 168, 508–513. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  12. Kumar, S.; Hasan, S.S.; Wong, P.S.; Chong, D.W.K.; Kairuz, T. Anticholinergic Burden, Sleep Quality and Health Outcomes in Malaysian Aged Care Home Residents. Pharmacy 2019, 7, 143. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  13. Deratnay, P.; Sidani, S. The effect of insomnia on functional status of community-dwelling older adults. J. Gerontol. Nurs. 2013, 39, 22–30. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  14. Zullig, L.L.; Whitson, H.E.; Hastings, S.N.; Beadles, C.; Kravchenko, J.; Akushevich, I.; Maciejewski, M.L. A Systematic Review of Conceptual Frameworks of Medical Complexity and New Model Development. J. Gen. Intern. Med. 2016, 31, 329–337. [Google Scholar] [CrossRef] [PubMed]
  15. Campbell, N.L.; Perkins, A.J.; Bradt, P.; Perk, S.; Wielage, R.C.; Boustani, M.A.; Ng, D.B. Association of Anticholinergic Burden with Cognitive Impairment and Health Care Utilization Among a Diverse Ambulatory Older Adult Population. Pharmacotherapy 2016, 36, 1123–1131. [Google Scholar] [CrossRef] [PubMed]
  16. Bayliss, E.A.; Shetterly, S.M.; Drace, M.L.; Norton, J.D.; Maiyani, M.; Gleason, K.S.; Sawyer, J.K.; Weffald, L.A.; Green, A.R.; Reeve, E.; et al. Deprescribing Education vs Usual Care for Patients With Cognitive Impairment and Primary Care Clinicians: The OPTIMIZE Pragmatic Cluster Randomized Trial. JAMA Intern. Med. 2022, 182, 534–542. [Google Scholar] [CrossRef] [PubMed]
  17. Martin, P.; Tamblyn, R.; Benedetti, A.; Ahmed, S.; Tannenbaum, C. Effect of a Pharmacist-Led Educational Intervention on Inappropriate Medication Prescriptions in Older Adults: The D-PRESCRIBE Randomized Clinical Trial. JAMA 2018, 320, 1889–1898. [Google Scholar] [CrossRef]
  18. Aubert, C.E.; Kerr, E.A.; Maratt, J.K.; Klamerus, M.L.; Hofer, T.P. Outcome Measures for Interventions to Reduce Inappropriate Chronic Drugs: A Narrative Review. J. Am. Geriatr. Soc. 2020, 68, 2390–2398. [Google Scholar] [CrossRef]
  19. Hastings, S.N.; Mahanna, E.P.; Berkowitz, T.S.Z.; Smith, V.A.; Choate, A.L.; Hughes, J.M.; Pavon, J.; Robinson, K.; Hendrix, C.; Van Houtven, C.; et al. Video-Enhanced Care Management for Medically Complex Older Adults with Cognitive Impairment. J. Am. Geriatr. Soc. 2021, 69, 77–84. [Google Scholar] [CrossRef]
  20. Welsh, K.A.; Breitner, J.C.S.; Magruder-Habib, K.M. Detection of dementia in the elderly using the telephone interview for cognitive status. Neuropsychiatry Neuropsychol. Behav. Neurol. 1993, 6, 103–110. [Google Scholar]
  21. Campbell, N.L.; Maidment, I.; Fox, C.; Khan, B.; Boustani, M. The 2012 update to the anticholinergic cognitive burden scale. J. Am. Geriatr. Soc. 2013, 61, S142–S143. [Google Scholar]
  22. Squires, P.; Pahor, M.; Manini, T.M.; Vouri, S.; Brown, J.D. Impact of Anticholinergic Medication Burden on Mobility and Falls in the Lifestyle Interventions for Elders (LIFE) Study. J. Clin. Med. 2020, 9, 2989. [Google Scholar] [CrossRef] [PubMed]
  23. Salahudeen, M.S.; Duffull, S.B.; Nishtala, P.S. Anticholinergic burden quantified by anticholinergic risk scales and adverse outcomes in older people: A systematic review. BMC Geriatr. 2015, 15, 31. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  24. Buysse, D.J.; Reynolds, C.F., 3rd; Monk, T.H.; Berman, S.R.; Kupfer, D.J. The Pittsburgh Sleep Quality Index: A new instrument for psychiatric practice and research. Psychiatry Res. 1989, 28, 193–213. [Google Scholar] [CrossRef]
  25. Landry, G.J.; Best, J.R.; Liu-Ambrose, T. Measuring sleep quality in older adults: A comparison using subjective and objective methods. Front. Aging Neurosci. 2015, 7, 166. [Google Scholar] [CrossRef] [Green Version]
  26. Cebron Lipovec, N.; Jazbar, J.; Kos, M. Anticholinergic Burden in Children, Adults and Older Adults in Slovenia: A Nationwide Database Study. Sci. Rep. 2020, 10, 9337. [Google Scholar] [CrossRef]
  27. Green, A.R.; Oh, E.; Hilson, L.; Tian, J.; Boyd, C.M. Anticholinergic Burden in Older Adults with Mild Cognitive Impairment. J. Am. Geriatr. Soc. 2016, 64, e313–e314. [Google Scholar] [CrossRef] [Green Version]
  28. Lockery, J.E.; Broder, J.C.; Ryan, J.; Stewart, A.C.; Woods, R.L.; Chong, T.T.-J.; Cloud, G.C.; Murray, A.; Rigby, J.D.; Shah, R.; et al. ASPREE Investigator Group, ASPREE Investigator Group listed on www.aspree.org. A Cohort Study of Anticholinergic Medication Burden and Incident Dementia and Stroke in Older Adults. J. Gen. Intern. Med. 2021, 36, 1629–1637. [Google Scholar] [CrossRef]
  29. Carrière, I.; Fourrier-Reglat, A.; Dartigues, J.F.; Rouaud, O.; Pasquier, F.; Ritchie, K.; Ancelin, M.L. Drugs With Anticholinergic Properties, Cognitive Decline, and Dementia in an Elderly General Population: The 3-City Study. Arch. Intern. Med. 2009, 169, 1317–1324. [Google Scholar] [CrossRef] [Green Version]
  30. Hilmer, S.; Mager, D.E.; Simonsick, E.M.; Ling, S.M.; Windham, B.G.; Harris, T.B.; Shorr, R.I.; Bauer, D.C.; Abernethy, D.R.; Health ABC Study. Drug burden index score and functional decline in older people. Am. J. Med. 2009, 122, 1142–1149.e1-2. [Google Scholar] [CrossRef] [Green Version]
  31. Kachru, N.; Carnahan, R.M.; Johnson, M.L.; Aparasu, R.R. Potentially inappropriate anticholinergic medication use in community-dwelling older adults: A national cross-sectional study. Drugs Aging 2015, 32, 379–389. [Google Scholar] [CrossRef] [PubMed]
  32. 2019 American Geriatrics Society Beers Criteria® Update Expert Panel. American Geriatrics Society 2019 Updated AGS Beers Criteria® for Potentially Inappropriate Medication Use in Older Adults. J. Am. Geriatr. Soc. 2019, 67, 674–694. [Google Scholar] [CrossRef] [PubMed]
  33. Trueman, C.; Castillo, S.; O’Brien, K.; Hoie, E. Inappropriate Use of Skeletal Muscle Relaxants in Geriatric Patients. US Pharm. 2020, 45, 25–29. [Google Scholar]
  34. Kalisch, L.M.; Caughey, G.E.; Roughead, E.E.; Gilbert, A.L. The prescribing cascade. Aust. Prescr. 2011, 34, 162–166. [Google Scholar] [CrossRef] [Green Version]
  35. Khezrian, M.; McNeil, C.J.; Murray, A.D.; Myint, P.K. An overview of prevalence, determinants and health outcomes of polypharmacy. Ther. Adv. Drug. Saf. 2020, 11, 2042098620933741. [Google Scholar] [CrossRef]
Geriatrics 07 00059 g001 550
Figure 1. Most frequently prescribed medication classes by anticholinergic cognitive burden score. ACB = anticholinergic cognitive burden; H2R = histamine type 2 receptor. antihistamine ACB 1 = cetirizine, loratadine, desloratadine; antihistamine ACB 3 = diphenhydramine, hydroxyzine; muscle relaxant ACB 2 = cyclobenzaprine; muscle relaxant ACB 3 = methocarbamol.
Figure 1. Most frequently prescribed medication classes by anticholinergic cognitive burden score. ACB = anticholinergic cognitive burden; H2R = histamine type 2 receptor. antihistamine ACB 1 = cetirizine, loratadine, desloratadine; antihistamine ACB 3 = diphenhydramine, hydroxyzine; muscle relaxant ACB 2 = cyclobenzaprine; muscle relaxant ACB 3 = methocarbamol.
Geriatrics 07 00059 g001
Table
Table 1. Baseline patient characteristics and clinical measures by total anticholinergic cognitive burden score.
Table 1. Baseline patient characteristics and clinical measures by total anticholinergic cognitive burden score.
Veteran Characteristic at BaselineTotal Sample
N = 40		ACB 0–2
n = 20		ACB 3+
n = 20
Age, mean (SD)72.4 (6.1)70.5 (5.0)74.3 (6.5)
Male gender, n (%)40 (100.0)20 (100.0)20 (100.0)
Black or African-American race, n (%)15 (37.5)9 (45.0)6 (30.0)
Married or living together, n (%)31 (77.5)16 (80.0)15 (75.0)
High school education or less, n (%)14 (35.0)10 (50.0)4 (20.0)
Fair/poor self-rated health, n (%)26 (65.0)15 (75.0)11 (55.0)
Taking anticholinergic-active medication a, n (%)29 (72.5)12 (60.0)17 (85.0)
Taking other prescribed CNS medications, n (%)
Antipsychotics000
Benzodiazepines000
H2-Receptor antagonists3 (7.5)1 (5.0)2 (10.0)
Hypnotic sedatives 2 (5.0)1 (5.0)1 (5.0)
Opioids13 (32.5)6 (30.0)7 (35.0)
Skeletal muscle relaxants7 (17.5)07 (35.0)
Any side effects, n (%)26 (65.0)12 (60.0)14 (70.0)
TICS-m score, education adjusted, mean (SD)27.6 (2.6)26.9 (2.6)28.3 (2.5)
PSQI/sleep, mean (SD)9.1 (2.8)9.3 (2.7)9.0 (2.9)
a The designation of “anticholinergic” was not exclusive to the other classifications, e.g., ranitidine was categorized as both an anticholinergic and an H2-receptor antagonist, and cyclobenzaprine was categorized as both an anticholinergic and skeletal muscle relaxant. ACB = anticholinergic cognitive burden; CNS = central nervous system; H2 = histamine type 2; TICS-m = Telephone Interview for Cognitive Status—modified; PSQI = Pittsburgh Sleep Quality Index.
Table
Table 2. Frequency of all prescribed medications, overall and in patients with low vs. high anticholinergic burden.
Table 2. Frequency of all prescribed medications, overall and in patients with low vs. high anticholinergic burden.
ACB Scale ValueClass(es)MedicationNumber (%) Patients
(N = 40)		Number (%) Patients (ACB 0–2)
(n = 20)		Number (%) Patients (ACB 3+)
(n = 20)
0OpioidOXYCODONE9 (22.5)4 (20.0)5 (25.0)
0OpioidTRAMADOL4 (10.0)1 (5.0)3 (15.0)
0Hypnotic/sedativeZOLPIDEM2 (5.0)1 (5.0)1 (5.0)
0OpioidHYDROCODONE1 (2.5)1 (5.0)0
1Anticholinergic-activeMETOPROLOL16 (40.0)7 (35.0)9 (45.0)
1Anticholinergic-activeFUROSEMIDE7 (17.5)1 (5.0)6 (30.0)
1Anticholinergic-activeWARFARIN4 (10.0)2 (10.0)2 (10.0)
1Anticholinergic-activeCETIRIZINE3 (7.5)1 (5.0)2 (10.0)
1Anticholinergic-active AND H2-receptor antagonistRANITIDINE3 (7.5)1 (5.0)2 (10.0)
1Anticholinergic-activeCOLCHICINE2 (5.0)1 (5.0)1 (5.0)
1Anticholinergic-activeISOSORBIDE2 (5.0)02 (10.0)
1Anticholinergic-activeTRAZODONE2 (5.0)02 (10.0)
1Anticholinergic-activeBUPROPION1 (2.5)1 (5.0)0
1Anticholinergic-activeDESLORATADINE1 (2.5)01 (5.0)
1Anticholinergic-activeDIGOXIN1 (2.5)01 (5.0)
1Anticholinergic-activeHYDRALAZINE1 (2.5)01 (5.0)
1Anticholinergic-activeLORATADINE1 (2.5)01 (5.0)
2Anticholinergic-active AND skeletal muscle relaxantCYCLOBENZAPRINE4 (10.0)04 (20.0)
3Anticholinergic-activeDIPHENHYDRAMINE5 (12.5)05 (25.0)
3Anticholinergic-active AND skeletal muscle relaxantMETHOCARBAMOL3 (7.5)03 (15.0)
3Anticholinergic-activeHYDROXYZINE2 (5.0)02 (10.0)
3Anticholinergic-activeOXYBUTYNIN2 (5.0)02 (10.0)
3Anticholinergic-activeDOXYLAMINE1 (2.5)01 (5.0)
ACB = anticholinergic cognitive burden.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Pavon, J.M.; Berkowitz, T.S.Z.; Smith, V.A.; Hughes, J.M.; Hung, A.; Hastings, S.N. Potential Targets for Deprescribing in Medically Complex Older Adults with Suspected Cognitive Impairment. Geriatrics 2022, 7, 59. https://doi.org/10.3390/geriatrics7030059

AMA Style

Pavon JM, Berkowitz TSZ, Smith VA, Hughes JM, Hung A, Hastings SN. Potential Targets for Deprescribing in Medically Complex Older Adults with Suspected Cognitive Impairment. Geriatrics. 2022; 7(3):59. https://doi.org/10.3390/geriatrics7030059

Chicago/Turabian Style

Pavon, Juliessa M., Theodore S. Z. Berkowitz, Valerie A. Smith, Jaime M. Hughes, Anna Hung, and Susan N. Hastings. 2022. "Potential Targets for Deprescribing in Medically Complex Older Adults with Suspected Cognitive Impairment" Geriatrics 7, no. 3: 59. https://doi.org/10.3390/geriatrics7030059

Article Metrics

Back to TopTop