作者
İlker Özşahin,Efe Precious Onakpojeruo,Berna Uzun,Dilber Uzun Ozsahin,Tracy Butler
摘要
Abstract Background AD is a neurodegenerative disorder characterized by the gradual deterioration of mental abilities over time. According to estimates, 5.8 million Americans age 65 and older currently have AD dementia, and by the year 2050, that number is projected to reach 13.8 million. There is currently no cure for AD but medications have been shown to help some people manage the disease’s symptoms. Although there are disease modifying drugs target the amyloids such as Aducanumab and Lecanemab, high costs (around $30,000/year) and severe side effects make them unfavorable for many patients suffering from AD. However, prescribing the appropriate drug considering many factors affecting the decision‐making such as cost of the drug, safety of drug, and patient’s medical history might be a relatively challenging task for a physician and time‐consuming for patients in case the first prescribed drug is not tolerated or worsens the symptoms. Therefore, there is an urgent requirement for better tools to aid in prescribing the most appropriate drug. Methods In this regard, we propose using fuzzy Preference Ranking Organization Method for Enrichment of Evaluations (PROMETHEE), a multi‐criteria decision‐making (MCDM) method, to compare, evaluate, and rank the FDA‐approved drugs for managing AD symptoms. Drugs used in this study include Donepezil [Aricept], Rivastigmine [Exelon], Galantamine [Razadyne], Memantine [Namenda], and Memantine + Donepezil [Namzaric] with the following criteria: cost, safety of drug, tolerability, interaction with other drugs, response to cognitive impairment, symptoms management rate, and side effect. To the best of our knowledge, any decision‐making methodologies have not been proposed for use in identifying the most appropriate drugs for AD patients. Result Results showed that [Namzaric] came first in the ranking, and followed by [Aricept], [Namenda], and [Razadyne] while [Exelon] ranked as the least favorable drug. Conclusion MCDM methods can be used as a decision‐aid system for evaluating AD drugs and this study can be extended including more criteria and new drugs as they become available.