Characteristics of Nondisabled Older Patients Developing New Disability Associated with Medical Illnesses and Hospitalization

OBJECTIVE: To identify demographic, clinical, and biological characteristics of older nondisabled patients who develop new disability in basic activities of daily living (BADL) during medical illnesses requiring hospitalization. DESIGN: Longitudinal observational study. SETTING: Geriatric and Internal Medicine acute care units. PARTICIPANTS: Data are from 1,686 patients aged 65 and older who independent in BADL 2 weeks before hospital admission, enrolled in the 1998 survey of the Italian Group of Pharmacoepidemiology in the Elderly Study. MEASUREMENTS: Study outcome was new BADL disability at time of hospital discharge. Sociodemographic, functional status, and clinical characteristics were collected at hospital admission; acute and chronic conditions were classified according to the International Classification of Disease, ninth revision; fasting blood samples were obtained and processed with standard methods. RESULTS: At the time of hospital discharge 113 patients (6.7%) presented new BADL disability. Functional decline was strongly related to patients’ age and preadmission instrumental activities of daily living status. In a multivariate analysis, older age, nursing home residency, low body mass index, elevated erythrocyte sedimentation rate, acute stroke, high level of comorbidity expressed as Cumulative Illness Rating Scale score, polypharmacotherapy, cognitive decline, and history of fall in the previous year were independent and significant predictors of BADL disability. CONCLUSION: Several factors might contribute to loss of physical independence in hospitalized older persons. Preexisting conditions associated with the frailty syndrome, including physical and cognitive function, comorbidity, body composition, and inflammatory markers, characterize patients at high risk of functional decline

Conducting High-Value Secondary Dataset Analysis: An Introductory Guide and Resources

Secondary analyses of large datasets provide a mechanism for researchers to address high impact questions that would otherwise be prohibitively expensive and time-consuming to study. This paper presents a guide to assist investigators interested in conducting secondary data analysis, including advice on the process of successful secondary data analysis as well as a brief summary of high-value datasets and online resources for researchers, including the SGIM dataset compendium (www.sgim.org/go/datasets). The same basic research principles that apply to primary data analysis apply to secondary data analysis, including the development of a clear and clinically relevant research question, study sample, appropriate measures, and a thoughtful analytic approach. A real-world case description illustrates key steps: (1) define your research topic and question; (2) select a dataset; (3) get to know your dataset; and (4) structure your analysis and presentation of findings in a way that is clinically meaningful. Secondary dataset analysis is a well-established methodology. Secondary analysis is particularly valuable for junior investigators, who have limited time and resources to demonstrate expertise and productivity

Sparse Representation of Brain Aging: Extracting Covariance Patterns from Structural MRI

An enhanced understanding of how normal aging alters brain structure is urgently needed for the early diagnosis and treatment of age-related mental diseases. Structural magnetic resonance imaging (MRI) is a reliable technique used to detect age-related changes in the human brain. Currently, multivariate pattern analysis (MVPA) enables the exploration of subtle and distributed changes of data obtained from structural MRI images. In this study, a new MVPA approach based on sparse representation has been employed to investigate the anatomical covariance patterns of normal aging. Two groups of participants (group 1∶290 participants; group 2∶56 participants) were evaluated in this study. These two groups were scanned with two 1.5 T MRI machines. In the first group, we obtained the discriminative patterns using a t-test filter and sparse representation step. We were able to distinguish the young from old cohort with a very high accuracy using only a few voxels of the discriminative patterns (group 1∶98.4%; group 2∶96.4%). The experimental results showed that the selected voxels may be categorized into two components according to the two steps in the proposed method. The first component focuses on the precentral and postcentral gyri, and the caudate nucleus, which play an important role in sensorimotor tasks. The strongest volume reduction with age was observed in these clusters. The second component is mainly distributed over the cerebellum, thalamus, and right inferior frontal gyrus. These regions are not only critical nodes of the sensorimotor circuitry but also the cognitive circuitry although their volume shows a relative resilience against aging. Considering the voxels selection procedure, we suggest that the aging of the sensorimotor and cognitive brain regions identified in this study has a covarying relationship with each other

Predictors of depressive symptoms in persons with diabetic peripheral neuropathy: a longitudinal study

The aim of the study was to determine whether diabetic peripheral neuropathy (DPN) is a risk factor for depressive symptoms and examine the potential mechanisms for this relationship. This longitudinal study (9 and 18 month followup) of 338 DPN patients (mean age 61 years; 71% male; 73% type 2 diabetes) examined the temporal relationships between DPN severity (mean±SD; neuropathy disability score [NDS], 7.4±2.2; mean vibration perception threshold, 41.5±9.5 V), DPN somatic experiences (symptoms and foot ulceration), DPN psychosocial consequences (restrictions in activities of daily living [ADL] and social selfperception)and the Hospital Anxiety and Depression subscale measuring depressive symptoms (HADS-D; mean 4.9±3.7). Controlling for baseline HADS-D and demographic/disease variables, NDS at baseline significantly predicted increased HADS-D over 18 months. This association was mediated by baseline unsteadiness, which was significantly associated with increased HADS-D. Baseline ADL restrictions significantly predicted increased HADS-D and partly mediated the association between baseline unsteadiness and change in HADS-D. Increased pain, unsteadiness and ADL restrictions from baseline to 9 months each significantly predicted increased HADS-D over 18 months. Change in social self-perception from baseline to 9 months significantly predicted increased HADS-D and partly mediated the relationships of change in unsteadiness and ADL restrictions with change in HADS-D. These results confirm that neuropathy is a risk factor for depressive symptoms because it generates pain and unsteadiness. Unsteadiness is the symptom with the strongest association with depression,and is linked to depressive symptoms by perceptions of diminished self-worth as a result of inability to perform social roles