Background: Although delirium is a powerful tool for identifying high‐risk older patients at the emergency department (ED), the feasibility and importance of cognitive screening beyond delirium remain debated in fast‐paced healthcare settings. We estimated the effect of comprehensive but pragmatic cognitive screening, capturing delirium and preexisting cognitive impairment, on predicting adverse outcomes within 90 days of admission in older adults at the ED.
Method: We conducted a prospective cohort study comprising patients aged ≥65 years who were consecutively admitted to the ED of a large general hospital in Sao Paulo, Brazil. Trained healthcare professionals administered the brief Confusion Assessment Method (bCAM) and, for those without delirium, also the 10‐point Cognitive Screener (10‐CS), a practical 2‐minute tool validated for detecting preexisting cognitive impairment. Cognitive status was defined as normal (negative bCAM and 10‐CS>5), preexisting cognitive impairment (negative bCAM, but 10‐CS≤5), or delirium (positive bCAM). Investigators blinded to baseline data conducted telephone interviews to assess health outcomes, including functional decline in activities of daily living (ADL) and mortality over 90 days post‐admission. We used proportional hazards models to investigate associations between cognitive status and outcomes after adjusting for sociodemographic and clinical factors.
Result: Among 830 patients (mean age = 80±9 years; women = 47%), 434 (52%) were classified as normal cognition and 396 (48%) as cognitive impairment (165 [20%] had delirium and 231 [28%] had preexisting cognitive impairment without delirium) (Table 1). Interestingly, 52% of patients with preexisting cognitive impairment without delirium had neither been diagnosed with dementia nor reported memory complaints. Patients with preexisting cognitive impairment without delirium, similar to those with delirium, showed an increased risk of functional decline in ADL (sub‐HR = 1.59; 95% CI = 1.01‐2.49) and mortality (HR = 2.19; 95% CI = 1.18‐4.09) within 90 days of admission, compared to patients with normal cognition (Figures 1‐2).
Conclusion: In older patients at the ED, preexisting cognitive impairment without delirium emerges as a crucial risk factor for adverse outcomes, underscoring the need for comprehensive yet expedient cognitive screening in fast‐paced healthcare settings. The 10‐CS battery, swift and practical, is ideal for detecting frequently overlooked preexisting cognitive impairments, enabling the quick identification of high‐risk acutely ill older patients.
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http://dx.doi.org/10.1002/alz.089508 | DOI Listing |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11716471 | PMC |
Alzheimers Dement
December 2024
Eisai Inc., Nutley, NJ, USA
Background: Timely identification of mild cognitive impairment (MCI) is key to early intervention. While primary care providers are the most likely entry point to detect early signs of MCI, their detection rates are low. Building upon a published study, we used electronic health records (EHR) to develop a clinically enhanced MCI risk prediction algorithm.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
University of São Paulo Medical School, São Paulo, São Paulo, Brazil
Background: The profound impact of dementia on acute care, compounded by frequent underdiagnosis, is a significant challenge, especially among certain ethnic and minority groups, and remains largely unexplored in hospital settings. We used data from a multicentric study comprising 43 public and private hospitals in five countries to estimate the prevalence of undiagnosed dementia across different sociodemographic measures.
Method: The CHANGE (Creating a Hospital Assessment Network in Geriatrics) Study, an ongoing cohort designed to identify age‐related conditions like dementia, included patients aged ≥65 years admitted to 43 acute hospitals throughout Brazil and four other countries: Angola, Chile, Colombia, and Portugal.
Background: As disease‐modifying interventions advance, there is a critical need to detect Alzheimer’s disease and related dementias (ADRD) at the earlier, pre‐symptomatic stages. Transformer is a powerful model used to understand high‐dimensional data like images and languages. In this study, we propose a transformer‐based algorithm for predicting mild cognitive impairment (MCI) and ADRD 12 to 36 months in advance based on electronic health records (EHR).
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Innovation Center for Neurological Disorders, Xuanwu Hospital, Capital Medical University, Beijing, Beijing, China
Background: The prevalence of dementia among adults with diabetes represents a significant health challenge. Current dementia risk assessment tools are not tailored to the diabetic population. This study aims to develop more precise dementia risk predictive models for patients with diabetes using extensive blood proteomics.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Newcastle University, Newcastle Upon Tyne, UK
Background: Using digital technologies to detect the early signs of dementia‐causing diseases could improve the timely detection of these diseases. To support this approach, we explored users’ perspectives on the acceptability of using a variety of digital technologies.
Method: A sub‐group of participants from Boston University’s Alzheimer’s Disease Research Centre were recruited.
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