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Acute care readmission risk is an increasingly recognized problem that has garnered significant attention, yet the reasons for acute care readmission in the inpatient rehabilitation population are complex and likely multifactorial. Information on both medical comorbidities and functional status is routinely collected for stroke patients participating in inpatient rehabilitation. We sought to determine whether functional status is a more robust predictor of acute care readmissions in the inpatient rehabilitation stroke population compared with medical comorbidities using a large, administrative data set. A retrospective analysis of data from the Uniform Data System for Medical Rehabilitation from the years 2002 to 2011 was performed examining stroke patients admitted to inpatient rehabilitation facilities. A Basic Model for predicting acute care readmission risk based on age and functional status was compared with models incorporating functional status and medical comorbidities (Basic-Plus) or models including age and medical comorbidities alone (Age-Comorbidity). C-statistics were compared to evaluate model performance. There were a total of 803,124 patients: 88,187 (11%) patients were transferred back to an acute hospital: 22,247 (2.8%) within 3 days, 43,481 (5.4%) within 7 days, and 85,431 (10.6%) within 30 days. The C-statistics for the Basic Model were 0.701, 0.672, and 0.682 at days 3, 7, and 30 respectively. As compared to the Basic Model, the best-performing Basic-Plus model was the Basic+Elixhauser model with C-statistics differences of +0.011, +0.011, and + 0.012, and the best-performing Age-Comorbidity model was the Age+Elixhauser model with C-statistic differences of -0.124, -0.098, and -0.098 at days 3, 7, and 30 respectively. Readmission models for the inpatient rehabilitation stroke population based on functional status and age showed better predictive ability than models based on medical comorbidities.

Purpose With improved survival, long-term effects of burn injuries on quality of life, particularly community integration, are important outcomes. This study aims to assess the Community Integration Questionnaire's psychometric properties in the adult burn population. Methods Data were obtained from a multicenter longitudinal data set of burn survivors. The psychometric properties of the Community Integration Questionnaire (n = 492) were examined. The questionnaire items were evaluated for clinical and substantive relevance; validation procedures were conducted on different samples of the population; construct validity was assessed using exploratory factor analysis; internal consistency reliability was examined using Cronbach's α statistics; and item response theory was applied to the final models. Results The CIQ-15 was reduced by two questions to form the CIQ-13, with a two-factor structure, interpreted as self/family care and social integration. Item response theory testing suggests that Factor 2 captures a wider range of community integration levels. Cronbach's was 0.80 for Factor 1, 0.77 for Factor 2, and 0.79 for the test as a whole. Conclusions The CIQ-13 demonstrates validity and reliability in the adult burn survivor population addressing issues of self/family care and social integration. This instrument is useful in future research of community reintegration outcomes in the burn population.

Objective To examine functional status versus medical comorbidities as predictors of acute care readmissions in medically complex patients. Design Retrospective database study. Setting U.S. inpatient rehabilitation facilities. Participants Subjects included 120,957 patients in the Uniform Data System for Medical Rehabilitation admitted to inpatient rehabilitation facilities under the medically complex impairment group code between 2002 and 2011. Interventions A Basic Model based on gender and functional status was developed using logistic regression to predict the odds of 3-, 7-, and 30-day readmission from inpatient rehabilitation facilities to acute care hospitals. Functional status was measured by the FIM ® motor score. The Basic Model was compared to six other predictive models—three Basic Plus Models that added a comorbidity measure to the Basic Model and three Gender-Comorbidity Models that included only gender and a comorbidity measure. The three comorbidity measures used were the Elixhauser index, Deyo-Charlson index, and Medicare comorbidity tier system. The c-statistic was the primary measure of model performance. Main Outcome Measures We investigated 3-, 7-, and 30-day readmission to acute care hospitals from inpatient rehabilitation facilities. Results Basic Model c-statistics predicting 3-, 7-, and 30-day readmissions were 0.69, 0.64, and 0.65, respectively. The best-performing Basic Plus Model (Basic+Elixhauser) c-statistics were only 0.02 better than the Basic Model, and the best-performing Gender-Comorbidity Model (Gender+Elixhauser) c-statistics were more than 0.07 worse than the Basic Model. Conclusions Readmission models based on functional status consistently outperform models based on medical comorbidities. There is opportunity to improve current national readmission risk models to more accurately predict readmissions by incorporating functional data.

With this book, you will learn not just about R, but how to use R to answer conceptual, scientific, and experimental questions.Beginning with an overview of fundamental R concepts, you'll learn how R can be used to achieve the most commonly needed scientific data analysis tasks: testing for statistically significant differences between groups and model relationships in data. You will delve into linear algebra and matrix operations with an emphasis not on the R syntax, but on how these operations can be used to address common computational or analytical needs. This book also covers the application of matrix operations for the purpose of finding structure in high-dimensional data using the principal component, exploratory factor, and confirmatory factor analysis in addition to structural equation modeling. You will also master methods for simulation and learn about an advanced analytical method.

ABSTRACT BACKGROUND Hospital readmissions are expensive and they may signal poor quality of care. Whether functional status is related to hospital readmissions using a representative U.S sample remains unexplored . OBJECTIVE We aimed to assess the relationship between functional status and all-cause 30-day hospital readmissions using a representative sample of the US population. DESIGN This was a retrospective observational study (2003–2011). PATIENTS The study included 3,772 patients who completed the SF-12 before being hospitalized. Three hundred and eighteen (8.4 %) were readmitted within 30 days after being discharged. MEASUREMENTS The Medical Expenditure Panel Survey (MEPS) was employed. Functional status was measured with the Short-Form 12-Item Health Survey Version 2® (SF-12). The probability of being readmitted was estimated using a logistic model controlling for demographic characteristics, comorbid conditions, insurance coverage, physical (PCS) and mental (MCS) summaries of the SF-12, reason for hospitalization, length of hospital stay, region, and residential area. RESULTS A one-unit difference in PCS reduced the odds of readmission by 2 % (odds ratio 0.98 [95 % CI, 0.97 to 0.99]; p  < 0.001), which implies an 18 % reduction in the odds of readmissions for a ten-unit difference (one standard deviation) in PCS. The c-statistic of the model was 0.72. CONCLUSION Baseline physical function is associated with hospital readmissions. The SF-12 improves the ability to identify patients at high risk of hospital readmission.

With this book, you will learn not just about R, but how to use R to answer conceptual, scientific, and experimental questions. Beginning with an overview of fundamental R concepts, you'll learn how R can be used to achieve the most commonly needed scientific data analysis tasks: testing for statistically significant differences between groups and model relationships in data. You will delve into linear algebra and matrix operations with an emphasis not on the R syntax, but on how these operations can be used to address common computational or analytical needs. This book also covers the application of matrix operations for the purpose of finding structure in high-dimensional data using the principal component, exploratory factor, and confirmatory factor analysis in addition to structural equation modeling. You will also master methods for simulation and learn about an advanced analytical method.

The novel wake-promoting agent modafinil has been in use for the treatment of several sleep disorders for a few years and is now undergoing clinical trials for its use in the treatment of stimulant addiction, but its primary mechanism of action remains elusive. Previous laboratory studies have shown that modafinil has antioxidative and neuroprotective effects, which have not previously been suggested to be related to its wake-promoting effects. However, recent research indicates that free radicals may be related to sleep induction as well as cellular damage, suggesting that a common target of action may mediate modafinil's ability to oppose both of these effects. In this review we summarize and discuss previously published research on modafinil's neural, cytoprotective, and cognitive effects, and we propose possible primary biochemical targets that could underlie the effects of modafinil observed in these studies. We also suggest neurocognitive mechanisms responsible for modafinil's cognitive enhancing effects and its therapeutic potential in the treatment of stimulant addiction.