Anticholinergic medicines use among older adults before and after initiating dementia medicines

Publisher's version (útgefin grein)Aims: We investigated anticholinergic medicines use among older adults initiating dementia medicines. Methods: We used Pharmaceutical Benefits Scheme dispensing claims to identify patients who initiated donepezil, rivastigmine, galantamine or memantine between 1 January 2013 and 30 June 2017 (after a period of ≥180 days with no dispensing of these medicines) and remained on therapy for ≥180 days (n = 4393), and dispensed anticholinergic medicines in the 180 days before and after initiating dementia medicines. We further examined anticholinergic medicines prescribed by a prescriber other than the one initiating dementia medicines. Results: One-third of the study cohort (1439/4393) was exposed to anticholinergic medicines up to 180 days before or after initiating dementia medicines. Among patients exposed to anticholinergic medicines, 46% (659/1439) had the same medicine dispensed before and after initiating dementia medicines. The proportion of patients dispensed anticholinergic medicines increased by 2.5% (95% confidence interval [CI]: 1.3–3.7) after initiating dementia medicines. Antipsychotics use increased by 10.1% (95% CI: 7.6–12.7) after initiating dementia medicines; driven by increased risperidone use (7.3%, 95% CI: 5.3–9.3). Nearly half of patients dispensed anticholinergic medicines in the 180 days after (537/1133), were prescribed anticholinergic medicines by a prescriber other than the one initiating dementia medicines. Conclusion: Use of anticholinergic medicines is common among patients initiating dementia medicines and this occurs against a backdrop of widespread campaigns to reduce irrational medicine combinations in this vulnerable population. Decisions about deprescribing medicines with questionable benefit among patients with dementia may be complicated by conflicting recommendations in prescribing guidelines.Australian National Health and Medical Research Council (NHMRC) Centre of Research Excellence in Medicines and Ageing, Grant/Award Number: ID: 1060407; Australian Government Department of Industry, Innovation and Science, Grant/Award Number: ID: CRC‐P‐439. This research is funded by the Australian National Health and Medical Research Council (NHMRC) Centre of Research Excellence in Medicines and Ageing (ID: 1060407), a Cooperative Research Centre Project (CRC‐P) Grant from the Australian Government Department of Industry, Innovation and Science (ID: CRC‐P‐439) and philanthropic support from Mr Ross Brown AM. Dr Zoega is supported by a Scientia Fellowship from the University of New South Wales. The views expressed in this study are those of the authors only. A.J.M. receives funding from GlaxoSmithKline for a postgraduate scholarship for a student under his supervision. S.A.P. is a member of the Drug Utilisation Sub‐Committee of the Pharmaceutical Benefits Advisory Committee. The views expressed in this paper do not represent those of the committee.Peer Reviewe

Persistent postoperative opioid use after total hip or knee arthroplasty: A systematic review and meta-analysis

DISCLAIMER: In an effort to expedite the publication of articles related to the COVID-19 pandemic, AJHP is posting these manuscripts online as soon as possible after acceptance. Accepted manuscripts have been peer-reviewed and copyedited, but are posted online before technical formatting and author proofing. These manuscripts are not the final version of record and will be replaced with the final article (formatted per AJHP style and proofed by the authors) at a later time. PURPOSE: To identify the proportion of patients with continued opioid use after total hip or knee arthroplasty. METHODS: This systematic review and meta-analysis searched Embase, MEDLINE, the Cochrane Central Register of Controlled Trials, and International Pharmaceutical Abstracts for articles published from January 1, 2009, to May 26, 2021. The search terms (opioid, postoperative, hospital discharge, total hip or knee arthroplasty, and treatment duration) were based on 5 key concepts. We included studies of adults who underwent total hip or knee arthroplasty, with at least 3 months postoperative follow-up. RESULTS: There were 30 studies included. Of these, 17 reported on outcomes of total hip arthroplasty and 19 reported on outcomes of total knee arthroplasty, with some reporting on outcomes of both procedures. In patients having total hip arthroplasty, rates of postoperative opioid use at various time points were as follows: at 3 months, 20% (95% CI, 13%-26%); at 6 months, 17% (95% CI, 12%-21%); at 9 months, 19% (95% CI, 13%-24%); and at 12 months, 16% (95% CI, 15%-16%). In patients who underwent total knee arthroplasty, rates of postoperative opioid use were as follows: at 3 months, 26% (95% CI, 19%-33%); at 6 months, 20% (95% CI, 17%-24%); at 9 months, 23% (95% CI, 17%-28%); and at 12 months, 21% (95% CI, 12%-29%). Opioid naive patients were less likely to have continued postoperative opioid use than those who were opioid tolerant preoperatively. CONCLUSION: Over 1 in 5 patients continued opioid use for longer than 3 months after total hip or knee arthroplasty. Clinicians should be aware of this trajectory of opioid consumption after surgery

Proposed guidance on cost-avoidance studies in pharmacy practice

Purpose: Cost-avoidance studies of pharmacist interventions are common and often the first type of study conducted by investigators to quantify the economic impact of clinical pharmacy services. The purpose of this primer is to provide guidance for conducting cost-avoidance studies pertaining to clinical pharmacy practice. Summary: Cost-avoidance studies represent a paradigm conceptually different from traditional pharmacoeconomic analysis. A cost-avoidance study reports on cost savings from a given intervention, where the savings is estimated based on a counterfactual scenario. Investigators need to determine what specifically would have happened to the patient if the intervention did not occur. This assessment can be fundamentally flawed, depending on underlying assumptions regarding the pharmacists' action and the patient trajectory. It requires careful identification of the potential consequence of nonaction, as well as probability and cost assessment. Given the uncertainty of assumptions, sensitivity analyses should be performed. A step-by-step methodology, formula for calculations, and best practice guidance is provided. Conclusions: Cost-avoidance studies focused on pharmacist interventions should be considered low-level evidence. These studies are acceptable to provide pilot data for the planning of future clinical trials. The guidance provided in this article should be followed to improve the quality and validity of such investigations. PURPOSE: Cost-avoidance studies of pharmacist interventions are common and often the first type of study conducted by investigators to quantify the economic impact of clinical pharmacy services. The purpose of this primer is to provide guidance for conducting cost-avoidance studies pertaining to clinical pharmacy practice. SUMMARY: Cost-avoidance studies represent a paradigm conceptually different from traditional pharmacoeconomic analysis. A cost-avoidance study reports on cost savings from a given intervention, where the savings is estimated based on a counterfactual scenario. Investigators need to determine what specifically would have happened to the patient if the intervention did not occur. This assessment can be fundamentally flawed, depending on underlying assumptions regarding the pharmacists' action and the patient trajectory. It requires careful identification of the potential consequence of nonaction, as well as probability and cost assessment. Given the uncertainty of assumptions, sensitivity analyses should be performed. A step-by-step methodology, formula for calculations, and best practice guidance is provided. CONCLUSIONS: Cost-avoidance studies focused on pharmacist interventions should be considered low-level evidence. These studies are acceptable to provide pilot data for the planning of future clinical trials. The guidance provided in this article should be followed to improve the quality and validity of such investigations

Predictive performance of Bayesian vancomycin monitoring in the critically Ill

Objectives : It is recommended that therapeutic monitoring of vancomycin should be guided by 24-hour area under the curve concentration. This can be done via Bayesian models in dose-optimization software. However, before these models can be incorporated into clinical practice in the critically ill, their predictive performance needs to be evaluated. This study assesses the predictive performance of Bayesian models for vancomycin in the critically ill. Design: Retrospective cohort study. Setting: Single-center ICU. Patients: Data were obtained for all patients in the ICU between 1 January, and 31 May 2020, who received IV vancomycin. The predictive performance of three Bayesian models were evaluated based on their availability in commercially available software. Predictive performance was assessed via bias and precision. Bias was measured as the mean difference between observed and predicted vancomycin concentrations. Precision was measured as the sd of bias, root mean square error, and 95% limits of agreement based on Bland-Altman plots. Interventions: None. Measurements and main results: A total of 466 concentrations from 188 patients were used to evaluate the three models. All models showed low bias (-1.7 to 1.8 mg/L), which was lower with a posteriori estimate (-0.7 to 1.8 mg/L). However, all three models showed low precision in terms of sd (4.7-8.8 mg/L) and root mean square error (4.8-8.9 mg/L). The models underpredicted at higher observed vancomycin concentrations (bias 0.7-3.2 mg/L for < 20 mg/L; -5.1 to -2.3 for ≥ 20 mg/L) and the Bland-Altman plots showed a great deviation between observed and predicted concentrations. Conclusions: Bayesian models of vancomycin show not only low bias, but also low precision in the critically ill. Thus, Bayesian-guided dosing of vancomycin in this population should be used cautiously

Predictive Performance of Bayesian Vancomycin Monitoring in the Critically Ill

Objectives : It is recommended that therapeutic monitoring of vancomycin should be guided by 24-hour area under the curve concentration. This can be done via Bayesian models in dose-optimization software. However, before these models can be incorporated into clinical practice in the critically ill, their predictive performance needs to be evaluated. This study assesses the predictive performance of Bayesian models for vancomycin in the critically ill. Design: Retrospective cohort study. Setting: Single-center ICU. Patients: Data were obtained for all patients in the ICU between 1 January, and 31 May 2020, who received IV vancomycin. The predictive performance of three Bayesian models were evaluated based on their availability in commercially available software. Predictive performance was assessed via bias and precision. Bias was measured as the mean difference between observed and predicted vancomycin concentrations. Precision was measured as the sd of bias, root mean square error, and 95% limits of agreement based on Bland-Altman plots. Interventions: None. Measurements and main results: A total of 466 concentrations from 188 patients were used to evaluate the three models. All models showed low bias (-1.7 to 1.8 mg/L), which was lower with a posteriori estimate (-0.7 to 1.8 mg/L). However, all three models showed low precision in terms of sd (4.7-8.8 mg/L) and root mean square error (4.8-8.9 mg/L). The models underpredicted at higher observed vancomycin concentrations (bias 0.7-3.2 mg/L for < 20 mg/L; -5.1 to -2.3 for ≥ 20 mg/L) and the Bland-Altman plots showed a great deviation between observed and predicted concentrations. Conclusions: Bayesian models of vancomycin show not only low bias, but also low precision in the critically ill. Thus, Bayesian-guided dosing of vancomycin in this population should be used cautiously