Predicting Hospice Transitions in Dementia Caregiving Dyads: An Exploratory Machine Learning Approach

Interventions and Predictors of Transition to Hospice for People Living With Dementia: An Integrative Review

BACKGROUND AND OBJECTIVES

Goal-concordant transition to hospice is an important facet of end-of-life care for people living with dementia. The objective of this integrative review was to appraise existing evidence and gaps focused on interventions and predictors of transition to hospice and end-of-life care for persons living with dementia across healthcare to inform future research.

RESEARCH DESIGN AND METHODS

Using integrative review methodology by Whittemore and Knafl, 5 databases were searched (PubMed, CINAHL, Web of Science, Google Scholar, and Cochrane Database for Systematic Reviews) for articles between 2000 and 2023. The search focused on dementia, hospice care, transitions, care management and/or coordination, and intervention studies.

RESULTS

Fourteen articles met inclusion criteria after critical appraisal. Most were cross-sectional in design and conducted in nursing homes and hospitals in the U.S. persons living with dementia had multiple chronic conditions including cancer, diabetes, heart disease, and stroke. Interventions included components of hospice decision-making delivered through advance care planning, checklist-based care management for hospice transition, and palliative care for those with severe dementia. Predictors included increasing severity of illness including functional decline, organ failure, intensive care use, and the receipt of palliative care. Other predictors were related to insurance status, race and ethnicity, and caregiver burden. Overall, despite moderate to high-quality evidence, the studies were limited in scope and sample and lacked racial and ethnic diversity.

DISCUSSION AND IMPLICATIONS

Prospective, multisite randomized trials and population-based analyses including larger and diverse samples are needed for improved end-of-life dementia illness counseling and hospice care transitions for persons living with dementia and their caregivers.

A Telemedicine Analytic Framework for Fully and Semi-Automatic Alzheimer's Disease Screening Using Clock Drawing Test

More than 6 million Americans are at risk for Alzheimer's Disease Related Dementias (ADRD), most of whom are 65 or older. The clock drawing test (CDT) is a quick, simple, and effective technique that has the potential advantage of self-management and screening for ADRD patients. Current CDT-based ADRD screening studies focus more on efficacy, involving many handcrafted features, ignoring data modalities, and lacking validation. This paper aims to propose a unified telemedicine framework for fully and semi-automatic effective early ADRD screening based on multimodal and agile data fusion, focusing on the interpretability and validation of the model by using gradient-weighted class activation mapping (Grad-CAM) and locally linear embedding (LLE). The datasets for this work include 1,662 samples of CDT images and related demographic and cognitive information. The fully automatic case involving only CDT images can achieve the highest AUC of 81 with a 75 recall rate in binary screening. The multimodal data fusion in the semi-automatic case can achieve up to 90 AUC with an 83 recall rate. The visualization of the Convolutional Neural Networks (CNNs) shows that it can automatically obtain critical information about the outline, scale, and clock hands from CDT images, and the analysis of structured features shows that the memory test is key to effective ADRD screening.

Identifying High-Need Primary Care Patients Using Nursing Knowledge and Machine Learning Methods

BACKGROUND

Patient cohorts generated by machine learning can be enhanced with clinical knowledge to increase translational value and provide a practical approach to patient segmentation based on a mix of medical, behavioral, and social factors.

OBJECTIVES

This study aimed to generate a pragmatic example of how machine learning could be used to quickly and meaningfully cohort patients using unsupervised classification methods. Additionally, to demonstrate increased translational value of machine learning models through the integration of nursing knowledge.

METHODS

A primary care practice dataset (N = 3,438) of high-need patients defined by practice criteria was parsed to a subset population of patients with diabetes (n = 1233). Three expert nurses selected variables for k-means cluster analysis using knowledge of critical factors for care coordination. Nursing knowledge was again applied to describe the psychosocial phenotypes in four prominent clusters, aligned with social and medical care plans.

RESULTS

Four distinct clusters interpreted and mapped to psychosocial need profiles, allowing for immediate translation to clinical practice through the creation of actionable social and medical care plans. (1) A large cluster of racially diverse female, non-English speakers with low medical complexity, and history of childhood illness; (2) a large cluster of English speakers with significant comorbidities (obesity and respiratory disease); (3) a small cluster of males with substance use disorder and significant comorbidities (mental health, liver and cardiovascular disease) who frequently visit the hospital; and (4) a moderate cluster of older, racially diverse patients with renal failure.

CONCLUSION

This manuscript provides a practical method for analysis of primary care practice data using machine learning in tandem with expert clinical knowledge.