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Background: Length of stay (LOS) is one of the most important quantitative indexes that measures health service utilization within a hospital. Many studies have examined the association of three major stroke categories with LOS. Our aim is to investigate the differences of LOS among ischemic stroke subtypes, results from which are helpful to healthcare providers and government agencies to improve health care delivery efficiency. Methodology/Principal Findings: Using the Beijing Municipal Health Bureau's hospitalization reports, we performed a retrospective study among first-ever in-hospital patients with ischemic stroke (ICD-10 I63) in three general teaching hospitals in Beijing, China, from 2006 to 2010 with generalized linear model. In our study, 5,559 patients (female, 36.0%; age, 64.4±12.9 years) were included. The estimated mean LOS of ischemic stroke was 17.4±1.8 days. After adjusting for confounders, LOS of lacunar infarction (14.7 days; p<0.001) and LOS of small cerebral infarction (17.0 days; p = 0.393) were shorter than that of single cerebral infarction (17.9 days, p<0.001). LOS of multi-infarct (19.0 days; p = 0.028), brainstem infarction (19.3 days; p = 0.045), basal ganglia infarction (18.5 days; p = 0.452) and other subtypes of ischemic stroke (18.9 days; p = 0.327) were longer than that of single cerebral infarction. Conclusions: LOS of ischemic stroke patient differes across single cerebral infarction, lacunar infarction, multi-infarct and brainstem infarction patients. The ascending order of LOS was lacunar infarction, small cerebral infarction, single cerebral infarction, basal ganglia infarction, other subtypes of ischemic stroke, multi-infarct and brainstem infarction. [ABSTRACT FROM AUTHOR]

Background: Dyslipidemia is an extremely prevalent but preventable risk factor for cardiovascular disease. However, many dyslipidemia patients remain undetected in resource limited settings. The study was performed to develop and evaluate a simple and effective prediction approach without biochemical parameters to identify those at high risk of dyslipidemia in rural adult population. Methods: Demographic, dietary and lifestyle, and anthropometric data were collected by a cross-sectional survey from 8,914 participants living in rural areas aged 35-78 years. There were 6,686 participants randomly selected into a training group for constructing the artificial neural network (ANN) and logistic regression (LR) prediction models. The remaining 2,228 participants were assigned to a validation group for performance comparisons of ANN and LR models. The predictors of dyslipidemia risk were identified from the training group using multivariate logistic regression analysis. Predictive performance was evaluated by receiver operating characteristic (ROC) curve. Results: Some risk factors were significantly associated with dyslipidemia, including age, gender, educational level, smoking, high-fat diet, vegetable and fruit intake, family history, physical activity, and central obesity. For the ANN model, the sensitivity, specificity, positive and negative likelihood ratio, positive and negative predictive values were 90.41%, 76.66%, 3.87, 0.13, 76.33%, and 90.58%, respectively, while LR model were only 57.37%, 70.91%, 1.97, 0.60, 62.09%, and 66.73%, respectively. The area under the ROC cure (AUC) value of the ANN model was 0.86±0.01, showing more accurate overall performance than traditional LR model (AUC = 0.68±0.01, P,0.001). Conclusion: The ANN model is a simple and effective prediction approach to identify those at high risk of dyslipidemia, and it can be used to screen undiagnosed dyslipidemia patients in rural adult population. Further work is planned to confirm these results by incorporating multi-center and longer follow-up data. [ABSTRACT FROM AUTHOR]

Background: With the increasing prevalence of chronic noncommunicable diseases, patient education is becoming important to strengthen disease prevention and control. We aimed to systematically determine the extent to which registered, ongoing randomized controlled trials (RCTs) evaluated an educational intervention focus on patient-important outcomes (i.e., outcomes measuring patient health status and quality of life). Methods: On May 6, 2009, we searched for all ongoing RCTs registered in the World Health Organization International Clinical Trials Registry platform. We used a standardized data extraction form to collect data and determined whether the outcomes assessed were 1) patient- important outcomes such as clinical events, functional status, pain, or quality of life or 2) surrogate outcomes, such as biological outcome, treatment adherence, or patient knowledge. Principal Findings: We selected 268 of the 642 potentially eligible studies and assessed a random sample of 150. Patientimportant outcomes represented 54% (178 of 333) of all primary outcomes and 46% (286 of 623) of all secondary outcomes. Overall, 69% of trials (104 of 150) used at least one patient-important outcome as a primary outcome and 66% (99 of 150) as a secondary outcome. Finally, for 31% of trials (46 of 150), primary outcomes were only surrogate outcomes. The results varied by medical area. In neuropsychiatric disorders, patient important outcomes represented 84% (51 of 61) of primary outcomes, as compared with 54% (32 of 59) in malignant neoplasm and 18% (4 of 22) in diabetes mellitus trials. In addition, only 35% assessed the long-term impact of interventions (i.e., >6 months). Conclusions: There is a need to improve the relevance of outcomes and to assess the long term impact of educational interventions in RCTs. [ABSTRACT FROM AUTHOR]