Correlation between potentially inappropriate medication and Alzheimer’s disease among the elderly

Characterization of Potentially Inappropriate Medications That Need Special Attention in the Elderly with Dementia by Analyzing Pharmacy Claims Data

Community pharmacists may play a key role in promoting deprescribing of potential inappropriate medications (PIMs) that are highly prevalent among community-dwelling elderly with dementia. To characterize PIMs categories that need a special attention for dementia patients, in the present study, we analyzed the anonymized pharmacy claims data of patients aged 65 years and older (n = 333869) who visited nationwide 905 community-based pharmacies of Sugi Pharmacy Co., Ltd. during December 1-31, 2019. A dementia group was defined as patients who received typical dementia medications marketed in Japan, i.e., donepezil, galantamine, memantine or rivastigmine, and a non-dementia group was defined as patients who received no such medications. After propensity score matching on the basis of patients' age, gender and home healthcare insurance usage, the data of 11486 patients in each group were subjected to logistic regression analyses, to identify PIMs categories particularly important for dementia patients. Univariate analysis indicated that the proportions of dementia patients who received 1 and 2≤ of PIMs were significantly (p < 0.001) greater than those of non-dementia patients (odds ratios were 1.35 and 1.47, respectively). Multivariate analyses identified 5 categories of PIMs that were significantly more frequently prescribed in dementia patients, i.e., 'H2 blockers,' 'drugs for overactive bladder,' 'anti-diabetes drugs' and 'sulpiride' listed as PIMs categories for non-specific cases (adjusted odds ratios (aORs): 1.29, 1.91, 1.17, and 1.38, respectively), in addition to 'antipsychotics' listed only for dementia patients (aOR: 4.29). These results provide useful information to establish strategies for pharmacist-led deprescribing of PIMs in dementia patients.

Machine Learning in Causal Inference: Application in Pharmacovigilance

Monitoring adverse drug events or pharmacovigilance has been promoted by the World Health Organization to assure the safety of medicines through a timely and reliable information exchange regarding drug safety issues. We aim to discuss the application of machine learning methods as well as causal inference paradigms in pharmacovigilance. We first reviewed data sources for pharmacovigilance. Then, we examined traditional causal inference paradigms, their applications in pharmacovigilance, and how machine learning methods and causal inference paradigms were integrated to enhance the performance of traditional causal inference paradigms. Finally, we summarized issues with currently mainstream correlation-based machine learning models and how the machine learning community has tried to address these issues by incorporating causal inference paradigms. Our literature search revealed that most existing data sources and tasks for pharmacovigilance were not designed for causal inference. Additionally, pharmacovigilance was lagging in adopting machine learning-causal inference integrated models. We highlight several currently trending directions or gaps to integrate causal inference with machine learning in pharmacovigilance research. Finally, our literature search revealed that the adoption of causal paradigms can mitigate known issues with machine learning models. We foresee that the pharmacovigilance domain can benefit from the progress in the machine learning field.

Utilization and Spending on Potentially Inappropriate Medications by US Older Adults with Multiple Chronic Conditions using Multiple Medications

BACKGROUND

The utilization of potentially inappropriate medications (PIMs) in older adults can lead to adverse events and increased healthcare costs. Polypharmacy, the concurrent utilization of multiple medications, is common in older adults with multiple chronic conditions.

OBJECTIVE

To investigate the utilization and costs of PIMs in multimorbid older adults with polypharmacy over time.

METHODS

This retrospective cross-sectional study used linked Medicare claims and electronic health records from seven hospitals/medical centers in Massachusetts (2007-2014). Participants were ≥65 years old, had ≥2 chronic conditions (to define multimorbidity), and used drugs from ≥5 pharmaceutical classes for ≥90 days (to define polypharmacy). Chronic conditions were defined using the Chronic Conditions Indicator from the Agency for Health Research and Quality. PIMs were defined using the American Geriatrics Society 2019 version of the Beers criteria. We calculated the percentage of patients with ≥1 PIMs and the percentages of patients using different types of PIMs. We used logistic regression analyses to test the odds of taking ≥1 PIMs. We calculated mean costs spent on PIMs by dividing the costs spent on PIMs by the total medication cost.

RESULTS

≥69% of patients used ≥1 PIM. After adjusting for healthcare utilization, chronic conditions, medication intake, and demographic factors, female sex (2014: Odds ratio (OR)=1.27, 95%CI 1.25-1.30), age (2014: OR=0.92, 95%CI 0.90-0.93), and Hispanic ethnicity (2014: OR=1.41, 95%CI 1.27-1.56) were associated with PIM use. Gastrointestinal drugs and central nervous system drugs were the most commonly-used PIMs. In patients using ≥1 PIM, >10% of medication costs were spent on PIMs.

CONCLUSION

The utilization of PIMs in US older adults with multimorbidity and polypharmacy is high.

Drug interactions for elderly people with mental and behavioral disorders: a systematic scoping review

OBJECTIVES

To identify drug interactions of potentially inappropriate medications and mental and behavioral disorders, according to explicit potentially inappropriate medications criteria-based tools.

METHODOLOGY

A systematic scoping review was conducted in February 2020. Study characteristics, potentially inappropriate medications, drug interactions, rationale, and therapeutic management proposed were extracted. The commercialization and potentially inappropriate medications standard as essential in Brazil and in the world were identified. Therapeutic management was proposed for the most cited potentially inappropriate medications.

RESULTS

36 tools including 151 drug interactions, in addition to 132 potentially inappropriate medications with concerns related to six mental and behavioral disorders were identified. Cognitive impairment and dementia were the most frequently disorders reported and antipsychotics, anticholinergics, and benzodiazepines were the pharmacological classes more involved in the drug interactions. Despite the tools recommended risperidone and quetiapine when the use of antipsychotics were inevitable; levodopa + carbidopa for Parkinson's disease; and short and intermediate half-life benzodiazepines; the quality of the evidence needs to be assessed. In this review, sleep hygiene; deprescription; medication review; and clinical monitoring of adverse drug reactions are strongly recommended. In addition, to consider agomelatine, bupropion, moclobemide and melatonin as potential safer options for benzodiazepines.

CONCLUSION

Knowing the clinical conditions or risk morbidities associated with the use of potentially inappropriate medications and management of these medications for safer therapeutic equivalents or non-pharmacotherapeutic alternatives are relevant for patient safety.