Tracking personalized functional health in older adults using geriatric assessments

BMC Medical Informatics and Decision Making

Marjorie Skubic¹  James Keller¹  Anup K. Mishra¹  Mihail Popescu²  Carmen Abbott³  Erin L. Robinson⁴  Laurel A. Despins⁵  Steve Miller⁵  Kari Lane⁵  Marilyn Rantz⁵ 

[1] Department of Electrical Engineering and Computer Science, University of Missouri, 65211, Columbia, MO, USA;Department of Health Management and Informatics, University of Missouri, 65211, Columbia, MO, USA;School of Health Professions, Physical Therapy, University of Missouri, 65211, Columbia, MO, USA;School of Social Work, University of Missouri, 65211, Columbia, MO, USA;Sinclair School of Nursing, University of Missouri, 65211, Columbia, MO, USA;

关键词: Functional health;    Geriatric assessments;    Older adults;    Personalized functional health trajectory;    Health status indicators;    Mixed effects modeling;   

DOI  :  10.1186/s12911-020-01283-y

来源: Springer

【 摘 要 】

BackgroundHigher levels of functional health in older adults leads to higher quality of life and improves the ability to age-in-place. Tracking functional health objectively could help clinicians to make decisions for interventions in case of health deterioration. Even though several geriatric assessments capture several aspects of functional health, there is limited research in longitudinally tracking personalized functional health of older adults using a combination of these assessments.MethodsWe used geriatric assessment data collected from 150 older adults to develop and validate a functional health prediction model based on risks associated with falls, hospitalizations, emergency visits, and death. We used mixed effects logistic regression to construct the model. The geriatric assessments included were Activities of Daily Living (ADL), Instrumental Activities of Daily Living (IADL), Mini-Mental State Examination (MMSE), Geriatric Depression Scale (GDS), and Short Form 12 (SF12). Construct validators such as fall risks associated with model predictions, and case studies with functional health trajectories were used to validate the model.ResultsThe model is shown to separate samples with and without adverse health event outcomes with an area under the receiver operating characteristic curve (AUC) of > 0.85. The model could predict emergency visit or hospitalization with an AUC of 0.72 (95% CI 0.65–0.79), fall with an AUC of 0.86 (95% CI 0.83–0.89), fall with hospitalization with an AUC of 0.89 (95% CI 0.85–0.92), and mortality with an AUC of 0.93 (95% CI 0.88–0.97). Multiple comparisons of means using Turkey HSD test show that model prediction means for samples with no adverse health events versus samples with fall, hospitalization, and death were statistically significant (p < 0.001). Case studies for individual residents using predicted functional health trajectories show that changes in model predictions over time correspond to critical health changes in older adults.ConclusionsThe personalized functional health tracking may provide clinicians with a longitudinal view of overall functional health in older adults to help address the early detection of deterioration trends and decide appropriate interventions. It can also help older adults and family members take proactive steps to improve functional health.

【 授权许可】

CC BY