Objective: To examine the effects of a medication review project by geriatricians and geriatric medicine fellows on polypharmacy in a teaching nursing home.
Design: Quality improvement intervention study
Setting: Long-term care facility in Honolulu, HI PARTICIPANTS: Seventy-four patients with the Minimum Data Set quality indicator criteria of polypharmacy (9 or more medications).
Intervention: Geriatric medicine fellows and faculty reviewed each patient's medication list, consulted the updated Beers Criteria and Epocrates online drug-drug interaction program, and recommended medication changes to the patients' primary care physicians.
Measurements: Descriptive statistics, including means, standard deviations, and sums of variables were obtained for the number of medications in the following categories: total number, scheduled, pro re nata, high risk, contraindicated, with potential drug-drug interactions, and with no indication.
Results: Of 160 patients residing in a nursing home, 74 were on 9 or more medications. After the intervention, the mean number of medications per patient in the following categories decreased significantly: total number (16.64 to 15.54, P < .001), scheduled (11.3 to 10.99, P < .001), pro re nata (5.33 to 4.56, P < .001), high risk (0.94 to 0.73, P < .001), contraindicated (0.29 to 0.13, P = .004), with potential drug-drug interactions (6.1 to 4.83, P < .001), and with no indication (3.34 to 3.29, P = .045).
Conclusion: Polypharmacy in long-term care is prevalent and can lead to increased adverse effects and potentially inappropriate prescriptions. This study demonstrates an effective geriatrician-led intervention that both reduced polypharmacy and provided core competency training for geriatric medicine fellows.
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http://dx.doi.org/10.1016/j.jamda.2010.08.013 | DOI Listing |
Sci Rep
December 2024
Department of Comprehensive Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China.
Sacubitril/valsartan, a first-in-class angiotensin receptor neprilysin inhibitor, is widely used to treat heart failure. Despite its efficacy, sacubitril/valsartan inevitably causes adverse events such as hypotension, renal dysfunction, hyperkalemia, and angioedema. Sacubitril/valsartan-associated ototoxicity is often underreported in clinical studies and real-world settings.
View Article and Find Full Text PDFNat Commun
December 2024
Center for Health and Data Science (CHDS), the Second Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China.
Limited whole genome sequencing (WGS) studies in Asian populations result in a lack of representative reference panels, thus hindering the discovery of ancestry-specific variants. Here, we present the South and East Asian reference Database (SEAD) panel ( https://imputationserver.westlake.
View Article and Find Full Text PDFNat Commun
December 2024
Laboratory of Aging Research and Cancer Drug Target, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China.
The immune escape capacities of XBB variants necessitate the authorization of vaccines with these antigens. In this study, we produce three recombinant trimeric proteins from the RBD sequences of Delta, BA.5, and XBB.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Oasis Diagnostics® Corporation, Vancouver, Washington, USA.
There is a pressing need for accessible biomarkers with high diagnostic accuracy for Alzheimer's disease (AD) diagnosis to facilitate widespread screening, particularly in underserved groups. Saliva is an emerging specimen for measuring AD biomarkers, with distinct contexts of use that could complement blood and cerebrospinal fluid and detect various analytes. An interdisciplinary, international group of AD and related dementias (ADRD) researchers convened and performed a narrative review of published studies on salivary AD biomarkers.
View Article and Find Full Text PDFJ Dtsch Dermatol Ges
December 2024
Department of Dermatology, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University, The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, Guangdong, China.
Background: Basal cell carcinoma (BCC) is a prevalent type of skin cancer in which the inherent subjectivity of dermoscopy poses diagnostic challenges. Existing AI systems, which provide mainly image-level insights, lack the interpretability that is crucial for effective clinical decisions and patient education.
Patients And Methods: Our study developed a refined BCC dataset from the Human‒Machine Adversarial Model (HAM10000), which was annotated by clinicians to identify key diagnostic features.
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