Objective: To describe the most common types of poisoning exposures, implicated substances and underlying sources of medication error in people with dementia.
Design: Retrospective analysis of call records from the New South Wales (NSW) Poisons Information Center (PIC).
Setting And Participants: People with dementia who had a poisoning exposure reported to the NSW PIC (Australia's largest PIC).
Methods: A retrospective study was conducted using data from the NSW PIC from July 2014 to July 2019. All calls pertaining to individuals with a reported diagnosis of dementia (Alzheimer's disease or other) or who were taking an antidementia drug were included. Descriptive analysis was performed to characterize poisoning exposures, substances involved, and sources of error.
Results: A total of 2726 cases involving individuals with dementia [mean age = 79.5 (standard deviation 11.0) years; 56.2% female] were reported to the NSW PIC after intentional or unintentional poisoning. Therapeutic errors comprised 1692 (62.1%) of all reported cases followed by accidental exposures which contributed 711 (26.1%). The most common therapeutic substances responsible for therapeutic errors were donepezil (137 cases, 8.1%) and paracetamol (87 cases, 5.1%). The greatest proportion of all accidental exposures was attributed to hand sanitizer (46 cases, 6.5%). Over one-half of therapeutic errors (n = 1021, 60.3%) were linked to double dosing or mistiming of medications, and nursing home or carer errors were implicated in 385 cases (22.8%). Calls were most commonly made by family (n = 1187, 43.5%) and handled at home (n =1444, 53.0%).
Conclusions And Implications: Therapeutic errors and accidental poisonings are of concern in people with dementia. Strategies to reduce these potentially preventable adverse events should be further explored.
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http://dx.doi.org/10.1016/j.jamda.2020.11.024 | DOI Listing |
Aging Cell
January 2025
College of Artificial Intelligence, Nankai University, Tianjin, China.
Understanding the complex biological process of aging is of great value, especially as it can help develop therapeutics to prolong healthy life. Predicting biological age from gene expression data has shown to be an effective means to quantify aging of a subject, and to identify molecular and cellular biomarkers of aging. A typical approach for estimating biological age, adopted by almost all existing aging clocks, is to train machine learning models only on healthy subjects, but to infer on both healthy and unhealthy subjects.
View Article and Find Full Text PDFMed Phys
January 2025
National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Background: Respiratory motion during radiotherapy (RT) may reduce the therapeutic effect and increase the dose received by organs at risk. This can be addressed by real-time tracking, where respiration motion prediction is currently required to compensate for system latency in RT systems. Notably, for the prediction of future images in image-guided adaptive RT systems, the use of deep learning has been considered.
View Article and Find Full Text PDFJ Chromatogr A
December 2024
Department of Analytical Chemistry, University of Belgrade-Faculty of Chemistry, Studentski trg 12-16, 11158 Belgrade, Serbia. Electronic address:
Skin aging, characterized by reduced elasticity, wrinkles, and changes in pigmentation, presents significant challenges in the cosmetics industry. Identifying compounds that can help mitigate these effects is crucial to developing effective anti-aging treatments and improving skin health. An advanced analytical approach for identifying skin anti-aging compounds within complex natural mixtures must be developed to achieve this.
View Article and Find Full Text PDFPLoS One
January 2025
Department of Pharmacology and Therapeutics, College of Medicine and Health Sciences, The United Arab Emirates University, Al Ain, United Arab Emirates.
J Rehabil Med
January 2025
University of Florence, Department of Experimental and Clinical Medicine, Firenze, Italy; IRCCS Fondazione Don Carlo Gnocchi, Firenze, Italy.
Background: The Motricity Index (MI) is a commonly used method of measuring muscle strength in post-stroke hemiparesis. This study aimed to produce the MI Italian version (MI-IT) and assess its reliability in subjects with stroke.
Methods: Phase-1: stepwise approach to MI-IT production and pilot-testing with 10 health professionals to ensure clarity of each item and instructions for administration and scoring.
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