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Assessing Family Caregiver Readiness for Hospital Discharge of Patients With Serious or Life-Limiting Illness Using Electronic Health Record (EHR) and Self-Reported Data.
Health Serv Res
January 2025
College of Nursing, Marquette University, Milwaukee, Wisconsin, USA.
Objective: To assess how patient and caregiver factors influence caregiver readiness for hospital discharge in palliative care patients.
Study Setting And Design: This transitional care study uses cross-sectional data from a randomized controlled trial conducted from 2018 to 2023 testing an intervention for caregivers of hospitalized adult patients with a serious or life-limiting illness who received a palliative care consult prior to transitioning out of the hospital.
Data Sources And Analytical Sample: Caregiver readiness was measured with the Family Readiness for Hospital Discharge Scale (n = 231).
Machine learning based prediction models for cardiovascular disease risk using electronic health records data: systematic review and meta-analysis.
Eur Heart J Digit Health
January 2025
School of Life Course & Population Sciences, King's College London, SE1 1UL London, UK.
Cardiovascular disease (CVD) remains a major cause of mortality in the UK, prompting the need for improved risk predictive models for primary prevention. Machine learning (ML) models utilizing electronic health records (EHRs) offer potential enhancements over traditional risk scores like QRISK3 and ASCVD. To systematically evaluate and compare the efficacy of ML models against conventional CVD risk prediction algorithms using EHR data for medium to long-term (5-10 years) CVD risk prediction.
View Article and Find Full Text PDFLow Vision Rehabilitation Referral Characteristics for Patients with Neovascular Age-Related Macular Degeneration.
Healthcare (Basel)
January 2025
Division of Ophthalmology, Department of Surgery, UMass Chan-Lahey School of Medicine, Burlington, MA 01805, USA.
: Despite evidence that low vision rehabilitation (LVR) services can improve visual function in patients with neovascular age-related macular degeneration (nAMD), many patients are not directed to access these resources. This study was conducted to determine factors associated with LVR referral and to assess the visual outcomes from completed evaluations. : The study comprised a retrospective, cross-sectional analysis of patients with nAMD.
View Article and Find Full Text PDFCT-Based Body Composition Measures and Systemic Disease: A Population-Level Analysis Using Artificial Intelligence Tools in Over 100,000 Patients.
AJR Am J Roentgenol
January 2025
Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA.
CT-based abdominal body composition measures have shown associations with important health outcomes. Artificial intelligence (AI) advances now allow deployment of tools that measure body composition in large patient populations. To assess associations of age, sex, and common systemic diseases on CT-based body composition measurements derived using a panel of fully automated AI tools in a population-level adult patient sample.
View Article and Find Full Text PDFBackground: The long-term impact of opioid use disorder (OUD) on brain health has been little explored although of potentially high public health importance.
Objectives: To investigate the potential causal impact of OUD on later life brain health outcomes, including dementia, stroke and brain structure.
Methods: Observational and Mendelian randomization (MR) analyses were conducted.
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