Unlabelled: : Inappropriate medication use and polypharmacy are extremely common among older adults. Numerous studies have discussed the importance of a comprehensive medication assessment in the general geriatric population. However, only a handful of studies have evaluated inappropriate medication use in the geriatric oncology patient. Almost a dozen medication screening tools exist for the older adult. Each available tool has the potential to improve aspects of the care of older cancer patients, but no single tool has been developed for this population. We extensively reviewed the literature (MEDLINE, PubMed) to evaluate and summarize the most relevant medication screening tools for older patients with cancer. Findings of this review support the use of several screening tools concurrently for the elderly patient with cancer. A deprescribing tool should be developed and included in a comprehensive geriatric oncology assessment. Finally, prospective studies are needed to evaluate such a tool to determine its feasibility and impact in older patients with cancer.
Implications For Practice: The prevalence of polypharmacy increases with advancing age. Older adults are more susceptible to adverse effects of medications. "Prescribing cascades" are common, whereas "deprescribing" remains uncommon; thus, older patients tend to accumulate medications over time. Older patients with cancer are at high risk for adverse drug events, in part because of the complexity and intensity of cancer treatment. Additionally, a cancer diagnosis often alters assessments of life expectancy, clinical status, and competing risk. Screening for polypharmacy and potentially inappropriate medications could reduce the risk for adverse drug events, enhance quality of life, and reduce health care spending for older cancer patients.
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http://dx.doi.org/10.1634/theoncologist.2015-0492 | DOI Listing |
Eur J Pediatr
January 2025
Department of Maternal Infantile and Urological Sciences, Sapienza University of Rome, Rome, Italy.
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January 2025
Hasselt University, Faculty of Medicine and Life Sciences / Limburg Clinical Research Centre, Agoralaan, Diepenbeek, Belgium.
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Recent Findings: Recent research highlights the rising prevalence of AFMR, now accounting for nearly one-third of significant mitral regurgitation cases.
BMJ Evid Based Med
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Department of Public Health, History of Science, and Gynecology, Miguel Hernandez University of Elche Faculty of Medicine, Sant Joan D'Alacant, Comunidad Valenciana, Spain
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Expert Opin Drug Saf
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Department of Endocrinology, Guang'anmen Hospital of China Academy of Chinese Medical Sciences, Beijing, China.
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J Clin Med
January 2025
Anesthesiology, Critical Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Viale Gramsci 14, 43126 Parma, Italy.
Sepsis is one of the leading causes of mortality in hospital settings, and early diagnosis is a crucial challenge to improve clinical outcomes. Artificial intelligence (AI) is emerging as a valuable resource to address this challenge, with numerous investigations exploring its application to predict and diagnose sepsis early, as well as personalizing its treatment. Machine learning (ML) models are able to use clinical data collected from hospital Electronic Health Records or continuous monitoring to predict patients at risk of sepsis hours before the onset of symptoms.
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