Nutrition Screening and Assessment in Hospitalized Patients

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/141744/1/ncp0483.pd

Constructing and identifying predictors of frailty among homeless adults—A latent variable structural equations model approach

Homeless urbanites are a heterogeneous population with unique health and social service needs. The study examined situational, behavioral, health-related and resource indicators in terms of their direct impact on frailty, hypothesized as a latent variable. Using structural equation modeling (SEM), a model was tested with 150 homeless men and women, ages 40–73, from three homeless day center drop-in sites on Skid Row and one residential drug treatment (RDT) facility that works with homeless parolees and probationers. In bivariate analyses with the latent construct frailty, months homeless (p < 0.01), female gender (p < 0.05), education (p < 0.05), comorbid conditions (p < 0.001), nutrition (p < 0.001), resilience (p < 0.001), health care utilization (p < 0.01), and falls (p < 0.001) were significantly associated with frailty. In the final path model, significant predictors of frailty included educational attainment (p < 0.01), comorbid conditions (p < 0.001), nutrition (p < 0.001), resilience (p < 0.001), and falls (p < 0.01). These findings will serve as a foundation for future nurse-led, community-based initiatives that focus on key predictors of frailty among the homeless and the development of interventions

A multiscale model to predict current absolute risk of femoral fracture in a postmenopausal population

Osteoporotic hip fractures are a major healthcare problem. Fall severity and bone strength are important risk factors of hip fracture. This study aims to obtain a mechanistic explanation for fracture risk in dependence of these risk factors. A novel modelling approach is developed that combines models at different scales to overcome the challenge of a large space–time domain of interest and considers the variability of impact forces between potential falls in a subject. The multiscale model and its component models are verified with respect to numerical approximations made therein, the propagation of measurement uncertainties of model inputs is quantified, and model predictions are validated against experimental and clinical data. The main results are model predicted absolute risk of current fracture (ARF0) that ranged from 1.93 to 81.6% (median 36.1%) for subjects in a retrospective cohort of 98 postmenopausal British women (49 fracture cases and 49 controls); ARF0 was computed up to a precision of 1.92 percentage points (pp) due to numerical approximations made in the model; ARF0 possessed an uncertainty of 4.00 pp due to uncertainties in measuring model inputs; ARF0 classified observed fracture status in the above cohort with AUC = 0.852 (95% CI 0.753–0.918), 77.6% specificity (95% CI 63.4–86.5%) and 81.6% sensitivity (95% CI 68.3–91.1%). These results demonstrate that ARF0 can be computed using the model with sufficient precision to distinguish between subjects and that the novel mechanism of fracture risk determination based on fall dynamics, hip impact and bone strength can be considered validated

Nutrition Screening and Assessment in Hospitalized Patients

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/141744/1/ncp0483.pd