To investigate the effects of number of medications and Drug Burden Index (DBI) on transitions between frailty stages and death in community-dwelling older men.Cohort study.Sydney, Australia.Community-dwelling men aged 70 and older (N=1,705).Self-reported questionnaires and clinic visits were conducted at baseline and 2 and 5 years. Frailty was assessed at all three waves according to the modified Fried frailty phenotype. The total number of regular prescription medications and DBI (a measure of exposure to sedative and anticholinergic medications) were calculated over the three waves. Data on mortality over 9 years were obtained. Multistate modeling was used to characterize the transitions across three frailty states (robust, prefrail, frail) and death.Each additional medication was associated with a 22% greater risk of transitioning from the robust state to death (adjusted 95% confidence interval (CI)=1.06-1.41). Every unit increase in DBI was associated with a 73% greater risk of transitioning from the robust state to the prefrail state (adjusted 95% CI=1.30-2.31) and a 2.75 times greater risk of transitioning from the robust state to death (adjusted 95% CI=1.60-4.75). There was no evidence of an adjusted association between total number of medications or DBI and the other transitions.Although the possibility of confounding by indication cannot be excluded, additional medications were associated with greater risk of mortality in robust community-dwelling older men. Greater DBI was also associated with greater risk of death and transitioning from the robust state to the prefrail state.
Anticholinergic drug exposure is associated with adverse outcomes in older people. While a number of tools have been developed to measure anticholinergic drug exposure, there is limited information about the agreement and overlap between the various scales. The aim of this study was to investigate the agreement and overlap between different measures of anticholinergic drug exposure in a cohort of community-dwelling older men.A cross-sectional study was used to compare anticholinergic drug exposure calculated using the Anticholinergic Risk Scale (ARS), the Anticholinergic Drug Scale (ADS), the Anticholinergic Cognitive Burden (ACB) and the Drug Burden Index anticholinergic subscale (DBI-ACH) in a cohort of community-dwelling men aged 70 years and older (n = 1696). Statistical agreement, expressed as Cohen's kappa (κ), between these measurements was calculated.Differences were found between the tools regarding the classification of anticholinergic drug exposure for individual participants. Thirteen percent of the population used a drug listed as anticholinergic on the ARS, 39% used a drug listed on the ADS and the ACB, and 18% of the population used one or more anticholinergic drugs listed on the DBI-ACH. While agreement was good between the ACB and ADS (κ = 0.628, 95% CI 0.593, 0.664), little agreement was found between remaining tools (κ = 0.091-0.264).With the exception of the ACB and ADS, there was poor agreement regarding anticholinergic drug exposure among the four tools compared in this study. Great care should be taken when interpreting anticholinergic drug exposure using existing scales due to the wide variability between the different scales.
Observational studies using real-world data (RWD) can address gaps in knowledge on deprescribing medications but are subject to methodological issues. Limited data exist on the methods employed to use RWD to measure the effects of deprescribing.
Abstract Introduction Transitions of care are a major contributing factor to medication‐related adverse events in people with dementia. There is limited knowledge on how well guidance on medication management is provided to carers at hospital discharge, and there is no tool that comprehensively reports on medication management guidance at discharge. The study aim was to evaluate carers’ experiences of medication management guidance for people with dementia at hospital discharge and explore the underlying factors of the CATCH tool as preliminary validation of the tool. Methods A cross‐sectional survey of the CATCH tool was distributed across Australia between March and November 2022. The CATCH tool contained 30 Likert‐type items and three dichotomous (yes/no) items. The results of the survey were analysed descriptively, and exploratory factor and regression analyses were performed. Results A total of 185 survey responses were completed (completion rate 66.8%). Most respondents were informal carers (n = 116, 62.7%), and were predominantly provided medication management guidance on the day of discharge (n = 79, 42.7%). One‐third (n = 61, 33.0%) responded that medication management guidance could be improved. Regarding the safe use of medications, respondents felt information was not well provided on: medications that may interact with each other (n = 30, 17.6%), possible side‐effects of medications (n = 28,16.1%), and medications that might act on the brain (n = 27, 15.8%). Thirty respondents (17.8%) reported that they were not included in decisions by hospital staff about medications for people with dementia. The CATCH tool contained two factors: 1) person‐centred guidance in the safe use of medications during and after discharge; and 2) support of carers in medication management. Internal consistency of all factors were acceptable with Cronbach’s alpha (>0.8). The carer reported measure of how medication management guidance is provided is positively related to their confidence in management of medications post‐discharge and satisfaction (p< 0.05 for both). Conclusion Opportunities to improve delivery of person‐focused medication management guidance involve provision of information on the safe use of medications to the carer and engagement in medication decisions. The CATCH tool is the first scale developed that evaluates person‐centred medication management guidance and support provided to carers of people with dementia at discharge.
ABSTRACT Background: Longitudinal studies of older adults are characterized by high dropout rates, multimorbid conditions, and multiple medication use, especially proximal to death. We studied the association between multiple medication use and incident dementia diagnoses including Alzheimer's disease (AD), vascular dementia (VD), and Lewy-body dementia (LBD), simultaneously accounting for dropout. Methods: Using the National Alzheimer's Coordinating Center data with three years of follow-up, a set of covariate-adjusted models that ignore dropout was fit to complete-case data, and to the whole-cohort data. Additionally, covariate-adjusted joint models with shared random effects accounting for dropout were fit to the whole-cohort data. Multiple medication use was defined as polypharmacy (⩾ five medications), hyperpolypharmacy (⩾ ten medications), and total number of medications. Results: Incident diagnoses were 2,032 for AD, 135 for VD, and 139 for LBD. Percentages of dropout at the end of follow-up were as follows: 71.8% for AD, 81.5% for VD, and 77.7% for LBD. The odds ratio (OR) estimate for hyperpolypharmacy among those with LBD versus AD was 2.19 (0.78, 6.15) when estimated using complete-case data and 3.00 (1.66, 5.40) using whole-cohort data. The OR reduced to 1.41 (0.76, 2.64) when estimated from the joint model accounting for dropout. The OR for polypharmacy using complete-case data differed from the estimates using whole-cohort data. The OR for dementia diagnoses on total number of medications was similar, but non-significant when estimated using complete-case data. Conclusion: Reasons for dropout should be investigated and appropriate statistical methods should be applied to reduce bias in longitudinal studies among high-risk dementia cohorts.
To the Editor: We thank Professor Strandberg1 for his valuable comments and interest in our recently published letter to the editor.2 The aim of our study was to determine associations between statin use and incident dementia according to disease severity and multimorbidity, but since the publication of our letter, a new Cochrane review has shed additional light on the topic.3 This Cochrane review concludes that current evidence does not support the role of statins for prevention of dementia, which is in agreement with our study findings. Ensuring rational statin use in older adults is challenging because clinical trial data in older adults with multimorbidity and polypharmacy are limited. In addition, as people age, treatment goals may change from extending duration of life to maintaining function and quality of life.4 Therefore, more research is warranted to generate information about benefits and side effects of statins in real-world data from older adults. Research into deprescribing medications is an emerging area and requires robust evidence to inform clinical decisions.5 The need for evidence-based deprescribing guidelines for a range of medication classes including statins was highlighted in a recent Delphi process.6 We appreciate Professor Strindberg's comments, and we support more research efforts to inform rational drug prescribing for older adults worldwide. Conflict of Interest: The editor in chief has reviewed the conflict of interest checklist provided by the authors and has determined that the authors have no financial or any other kind of personal conflicts with this paper. Author Contributions: All authors contributed to this paper. Sponsor's Role: None.
Abstract Males and females may respond differently to medications, yet knowledge about sexual dimorphisms in the effects of polypharmacy remains limited, particularly in aging. This study aimed to assess the effect of high Drug Burden Index (DBI) polypharmacy treatment compared to control on physical function and behavior in young and old, male and female mice. We studied whether age and sex play a role in physical function and behavior following polypharmacy treatment and whether they are paralleled by differences in serum drug levels. Young (2.5 months) and old (21.5 months), C57BL/6 mice were randomized to control or high DBI polypharmacy treatment (simvastatin, metoprolol, oxybutynin, oxycodone, and citalopram; n = 6–8/group) for 4–6 weeks. Compared to control, polypharmacy reduced physical function (grip strength, rotarod latency, gait speed, and total distance), middle zone distance (increased anxiety), and nesting score (reduced activities of daily living) in mice of both ages and sexes (p < .001). Old animals had a greater decline in nesting score (p < .05) and midzone distance (p < .001) than young animals. Grip strength declined more in males than females (p < .05). Drug levels at steady state were not significantly different between polypharmacy-treated animals of both ages and sexes. We observed polypharmacy-induced functional impairment in both age and sex groups, with age and sex interactions in the degree of impairment, which were not explained by serum drug levels. Studies of the pathogenesis of functional impairment from polypharmacy may improve management strategies in both sexes.
We thank Faure and colleagues for their interest in use of the Drug Burden Index (DBI). We share their desire to generalize this tool to allow it to be used more easily internationally. We wish to discuss their approach as outlined in the research letter, "A Standard International Version of the Drug Burden Index for Cross-National Comparison of the Functional Burden of Medications in Older People,"1 and to consider the implications not only for the proposed utility of comparing DBI between countries with existing formularies, but also for calculation of DBI in countries with limited national formularies, in which the tools to readily calculate a national DBI are not available. For countries with well-developed drug regulatory authorities and an advanced drug formulary structure, DBI as originally defined2 can be calculated from the formulary to measure exposure to medications with clinical anticholinergic or sedative effects. DBI has been calculated and validated against a range of clinical outcomes in different continents, countries, and settings, including the United States,2 Australia,3 the United Kingdom,4 and Finland.5 The DBI uses δ, the minimum registered or licensed dose on the national formulary, as an estimate of the dose required to provide 50% of the maximal effect (DR50). This is based on the pharmacological principle that a registered dose must have some efficacy and that the minimum dose is likely to have less than maximal efficacy. The actual DR50 for most therapeutic drugs in older adults is not known. There are some differences between countries in the minimum registered or licensed doses of medicines used in the calculations, which may be due to the effect of ethnicity on drug response6 or to other issues influencing the regulatory decisions. For example, diazepam has a minimum registered or licensed dose of 4 mg in the United States and 5 mg in Australia. In studies comparing DBI exposure between countries with well-developed national formularies, we agree that a consistent estimate of DR50 is required when differences between countries in minimum registered or licensed dose are unlikely to be related to the effect of ethnicity on pharmacological exposure and response. Using the median minimum registered or licensed dose for the countries studied should give a better estimate of DR50 than the World Health Organization (WHO) Defined Daily Dose (DDD)7 proposed in DBI-WHO1 for the reasons outlined below. For countries in which the WHO list of essential medications and WHO model formulary are used as primary resources to support and improve medication prescribing, we believe that calculation of DBI may best be performed using the WHO model formulary recommended adult starting dosage.8 Review of the WHO model formulary adult starting doses indicates that they rather closely reflect the minimum registered or licensed doses in the countries in which DBI has been validated against functional status and physical performance. This dose is, in most cases, substantially lower than the DDD7 substituted for δ in calculation of DBI-WHO.1 DDD is the average maintenance dose per day for a drug's main indication in adults and is not related to the drug's DR50. The DDD can differ markedly from the minimum registered dose for some, but not all, anticholinergic and sedative drugs that are commonly used in older people. For example, diazepam has a DDD of 10 mg, and the WHO model formulary recommended adult starting dosage is 5 mg. The finding that DBI-WHO produces a lower value for DBI is anticipated, because the effect of using a higher dose value is simply using a larger value for δ in the DBI equation. We cannot predict where the calculated value falls on the dose-response curve for these complex integrated functions or how this would affect the relationship between DBI and physical function. Therefore, we enthusiastically support the effort to make an index such as DBI available as widely as possible, because we strongly believe it has the potential to improve the prescribing of sedative and anticholinergic medicine in older adults worldwide. We would be delighted to work with Faure and colleagues to realize this potential. Conflict of Interest: The editor in chief has reviewed the conflict of interest checklist provided by the authors and has determined that the authors have no financial or any other kind of personal conflicts with this paper. Dr. Gnjidic is supported by a National Health and Medical Research Council Early Career Fellowship. Author Contributions: All authors contributed to the concepts in this letter and preparation of the manuscript. Sponsor's Role: None.
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