Browsing by Subject "Geriatric Assessment"

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    Cognitive effects of cancer and its treatments at the intersection of aging: what do we know; what do we need to know?
    (Elsevier, 2013-12) Mandelblatt, Jeanne S.; Hurria, Arti; McDonald, Brenna C.; Saykin, Andrew J.; Stern, Robert A.; VanMeter, John W.; McGuckin, Meghan; Traina, Tiffani; Denduluri, Neelima; Turner, Scott; Howard, Darlene; Jacobsen, Paul B.; Ahles, Tim; Department of Radiology and Imaging Sciences, IU School of Medicine
    There is a fairly consistent, albeit non-universal body of research documenting cognitive declines after cancer and its treatments. While few of these studies have included subjects aged 65 years and older, it is logical to expect that older patients are at risk of cognitive decline. Here, we use breast cancer as an exemplar disease for inquiry into the intersection of aging and cognitive effects of cancer and its therapies. There are a striking number of common underlying potential biological risks and pathways for the development of cancer, cancer-related cognitive declines, and aging processes, including the development of a frail phenotype. Candidate shared pathways include changes in hormonal milieu, inflammation, oxidative stress, DNA damage and compromised DNA repair, genetic susceptibility, decreased brain blood flow or disruption of the blood-brain barrier, direct neurotoxicity, decreased telomere length, and cell senescence. There also are similar structure and functional changes seen in brain imaging studies of cancer patients and those seen with "normal" aging and Alzheimer's disease. Disentangling the role of these overlapping processes is difficult since they require aged animal models and large samples of older human subjects. From what we do know, frailty and its low cognitive reserve seem to be a clinically useful marker of risk for cognitive decline after cancer and its treatments. This and other results from this review suggest the value of geriatric assessments to identify older patients at the highest risk of cognitive decline. Further research is needed to understand the interactions between aging, genetic predisposition, lifestyle factors, and frailty phenotypes to best identify the subgroups of older patients at greatest risk for decline and to develop behavioral and pharmacological interventions targeting this group. We recommend that basic science and population trials be developed specifically for older hosts with intermediate endpoints of relevance to this group, including cognitive function and trajectories of frailty. Clinicians and their older patients can advance the field by active encouragement of and participation in research designed to improve the care and outcomes of the growing population of older cancer patients.
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    Improving the validity of activity of daily living dependency risk assessment
    (SAGE Publications, 2015-04) Clark, Daniel O.; Stump, Timothy E.; Tu, Wanzhu; Miller, Douglas K.; Department of Medicine, IU School of Medicine
    OBJECTIVES: Efforts to prevent activity of daily living (ADL) dependency may be improved through models that assess older adults' dependency risk. We evaluated whether cognition and gait speed measures improve the predictive validity of interview-based models. METHOD: Participants were 8,095 self-respondents in the 2006 Health and Retirement Survey who were aged 65 years or over and independent in five ADLs. Incident ADL dependency was determined from the 2008 interview. Models were developed using random 2/3rd cohorts and validated in the remaining 1/3rd. RESULTS: Compared to a c-statistic of 0.79 in the best interview model, the model including cognitive measures had c-statistics of 0.82 and 0.80 while the best fitting gait speed model had c-statistics of 0.83 and 0.79 in the development and validation cohorts, respectively. CONCLUSION: Two relatively brief models, one that requires an in-person assessment and one that does not, had excellent validity for predicting incident ADL dependency but did not significantly improve the predictive validity of the best fitting interview-based models.