Despite the heterogeneity of published studies included in this review, the preponderance of evidence supports the recommendation that the American Heart Association should elevate depression to the status of a risk factor for adverse medical outcomes in patients with acute coronary syndrome.

Older individuals assimilate, and are targeted by, contradictory positive and negative age stereotypes. It was unknown whether the influence of stereotype valence is stronger when the stereotype content corresponds to the outcome domain. We randomly assigned older individuals to either positivecognitive, negative-cognitive, positive-physical, or negative-physical subliminal-age-stereotype groups and assessed cognitive and physical outcomes. As predicted, when the age stereotypes corresponded to the outcome domains, their valence had a significantly greater impact on cognitive and physical performance. This suggests that if a match occurs, it is more likely to generate expectations that become self-fulfilling prophecies.

Background and Purpose The Centers for Medicare and Medicaid Services (CMS) proposes to use 30-day hospital readmissions after ischemic stroke as part of the Hospital Inpatient Quality Reporting Program for payment determination beginning in 2016. The proportion of post-stroke readmissions that is potentially preventable is unknown. Methods Thirty-day readmissions for all Medicare fee-for-service beneficiaries aged ≥65 years discharged alive with a primary diagnosis of ischemic stroke (ICD-9-CM 433, 434, 436) between 12/2005-11/2006 were analyzed. Preventable readmissions were identified based on 14 Prevention Quality Indicators developed for use with administrative data by the US Agency for Healthcare Research and Quality (AHRQ). National, hospital-level, and regional preventable readmission rates were estimated. A random-effects logistic regression model was also used to determine patient-level factors associated with preventable readmissions. Results Among 307,887 ischemic stroke discharges, 44,379 (14.4%) were readmitted within 30 days; 5,322 (1.7% of all discharges) were the result of a preventable cause (e.g., pneumonia) and 39,057 (12.7%) were for other reasons (e.g., cancer). In multivariate analysis, older age and cardiovascular-related comorbid conditions were strong predictors of preventable readmissions. Preventable readmission rates were highest in the Southeast, Mid-Atlantic, and US territories and lowest in the Mountain and Pacific regions. Conclusions Based on the AHRQ Prevention Quality Indicators, we found that a small proportion of readmissions after ischemic stroke were classified as preventable. Although other causes of readmissions not reflected in the AHRQ measures could also be avoidable, hospital-level programs intended to reduce all-cause readmissions and reduce costs should target these high-risk patients.

Background and Purpose-Risk-standardized hospital readmission rates are used as publicly reported measures reflecting quality of care. Valid risk-standardized models adjust for differences in patient-level factors across hospitals. We conducted a systematic review of peer-reviewed literature to identify models that compare hospital-level poststroke readmission rates, evaluate patient-level risk scores predicting readmission, or describe patient and process-of-care predictors of readmission after stroke. Methods-Relevant studies in English published from January 1989 to July 2010 were identified using MEDLINE, PubMed, Scopus, PsycINFO, and all Ovid Evidence-Based Medicine Reviews. Authors of eligible publications reported readmission within 1 year after stroke hospitalization and identified Ն1 predictors of readmission in risk-adjusted statistical models. Publications were excluded if they lacked primary data or quantitative outcomes, reported only composite outcomes, or had Ͻ100 patients. Results-Of 374 identified publications, 16 met the inclusion criteria for this review. No model was specifically designed to compare risk-adjusted readmission rates at the hospital level or calculate scores predicting a patient's risk of readmission. The studies providing multivariable models of patient-level and/or process-of-care factors associated with readmission varied in stroke definitions, data sources, outcomes (all-cause and/or stroke-related readmission), durations of follow-up, and model covariates. Few characteristics were consistently associated with readmission. Conclusions-This review identified no risk-standardized models for comparing hospital readmission performance or predicting readmission risk after stroke. Patient-level and system-level factors associated with readmission were inconsistent across studies. The current literature provides little guidance for the development of risk-standardized models suitable for the public reporting of hospital-level stroke readmission performance. (Stroke. 2010;41:2525-2533.)