Year: 2023
Type: article
Abstract: Persons living with dementia are likely to benefit from interventions to deprescribe potentially inappropriate medications (PIM). PIM are medications for which risks of use outweigh benefits or safer alternatives exist. Variation in PIM definitions limits comparison of outcomes across deprescribing studies.1-3 To test this effect, we compared the effect of using two different PIM lists in the OPTIMIZE (Optimal Medication Management in Alzheimer Disease and Dementia) trial. OPTIMIZE was a pragmatic, cluster randomized trial of deprescribing education versus usual care for patients with cognitive impairment and their families. It was conducted from April 1, 2019, to March 31, 2020, at primary care clinics within Kaiser Permanente Colorado (KPCO), a not-for-profit integrated healthcare system.4, 5 The KPCO and Johns Hopkins University institutional review boards approved the study. Eligibility criteria included mild cognitive impairment or dementia, ≥1 additional chronic conditions, and ≥5 chronic medications. The intervention was designed to raise awareness of deprescribing and did not specifically focus on PIM. The primary PIM outcome was the percentage of individuals prescribed ≥1 PIM at 6 months. We compared this outcome using the original PIM list (Supplementary Table S1) to a revised PIM list. The original list was based in part on the Beers list of drugs to avoid for individuals with cognitive impairment5, 6 plus opioids. The revised PIM list included all medications from the 2019 Revised Beers list7 and additional medications that might be broadly targeted for deprescribing (e.g., proton pump inhibitors [PPIs]) and was edited for prescription utilization at KPCO. This resulted in 56 medications being removed from the original list, 105 medications kept, and 71 medications added. Analysis was performed on an intention-to-treat basis. Odds of taking ≥1 PIM were modeled using logistic regression accounting for baseline PIM use. Models also included self-reported race/ethnicity, age, and sex. Statistical analysis was conducted with SAS, version 9.4. All p values were from two-sided tests; results were deemed statistically significant at p < 0.05. A sensitivity analysis examined whether effect estimates changed when PPIs were removed from the revised list. Of 3012 patients (Supplementary Table S2), the percentage of patients taking PIM at baseline was 393 of 1433 (27.4%) in the intervention group and 435 of 1579 (27.6%) in the control group using the original PIM list. Using the revised list, the percentages were much higher: 908 of 1433 (63.4%) took PIM in the intervention group and 1054 of 1579 (66.8%) in the control group. In the sensitivity analysis excluding PPIs, 694 of 1433 (48.4%) took PIM in the intervention group and 804 of 1579 (50.9%) in the control group. The percentages of patients taking PIM at 6 and 12 months are in Figure 1. In logistic regressions, the odds of being on PIM were similar between intervention and control groups at 6 months using the original list (Table 1). By contrast, using the revised list, the odds of being on PIM were significantly lower in the intervention versus control groups. In the sensitivity analysis excluding PPIs, the estimate was no longer significant. At 12 months, the odds of being on PIM were similar in the intervention versus control groups using both lists and in the sensitivity analysis excluding PPIs (Table 1). In this secondary analysis, we found that the absolute number of patients taking PIM was much higher using the revised PIM list. The effectiveness of the intervention for the PIM outcome depended on how PIM were defined. These results have implications for the selection of medication appropriateness measures for deprescribing interventions. Deprescribing trials should specify which medications are included in their PIM definitions and why. The lack of standardized definitions of PIM for specific populations is a hindrance to deprescribing evidence generation and a limitation to informing implementation efforts.3, 8 In our sensitivity analysis excluding PPIs, the intervention effect was no longer significant at 6 months. This suggests that PPIs were influential in increasing the proportion of people identified as taking PIM and may be a high-yield target for deprescribing interventions.9 Study limitations include lack of statistical power to identify which drug classes were most likely to be deprescribed. We may have been more likely to detect a difference in the percentage of individuals taking PPIs because they were commonly prescribed at baseline. Additionally, we were unable to determine if continuation of a PIM was consistent with patient goals or reflected a failed withdrawal trial. In summary, our findings suggest that to evaluate the relative benefits of deprescribing interventions, future research will need PIM outcome measure definitions that are comparable across studies. At 6 months follow-up, using the original PIM list, the percentage of patients taking PIM was 370 of 1433 (25.8%) in the intervention group and 417 of 1579 (26.4%) in the control group. Using the revised PIM list, 864 of 1433 (60.3%) were taking PIM in the intervention group and 1043 of 1579 (66.1%) in the control group. In the sensitivity analysis excluding PPIs from the revised list, 663 of 1433 (46.3%) were taking PIM in the intervention group and 782 of 1579 (49.5%) in the control group. At 12 months follow-up, using the original PIM list, the percentage of patients taking PIM was 351 of 1433 (24.5%) in the intervention group and 396 of 1579 (25.1%) in the control group. Using the revised PIM list, 865 of 1433 (60.4%) were taking PIM in the intervention group and 1001 of 1579 (63.4%) in the control group. In the sensitivity analysis excluding PPIs from the revised list, 662 of 1433 (46.2%) were taking PIM in the intervention group and 755 of 1579 (47.8%) in the control group. Ariel R. Green contributed to study concept and design, interpretation of data and drafting and drafting and revision of the manuscript. Elizabeth A. Bayliss and Cynthia M. Boyd contributed to study concept and design, acquisition of subjects and/or data, analysis and interpretation of data and critical review of manuscript. Linda A. Weffald, Melanie L. Drace, and Jonathan D. Norton contributed to interpretation of data and critical review of manuscript. John D. Powers contributed to study concept and design, acquisition of subjects and/or data, analysis and interpretation of data and critical review of manuscript. We thank Susan M. Shetterly for performing primary statistical analyses on the OPTIMIZE trial. Cynthia M. Boyd reported receiving royalties from UpToDate for writing a chapter on multi-morbidity and honoraria from Dynamed for reviewing a chapter on falls outside the submitted work. No other disclosures were reported. None. The study was funded by the National Institute on Aging, Grant/Award Number: R33-AG057289. Supplementary Table S1. Potentially inappropriate medication lists. Supplementary Table S2. Characteristics of the study population. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.
DOI: 10.1111/jgs.18562
Language: English
Subfield: Geriatrics and Gerontology
Field: Medicine
Domain: Health Sciences
Open Access status: bronze
Funder: National Institute on Aging
OpenAlex ID: W4386191861