Background: Cognitive impairment is an age-related condition as the rate of cognitive decline rapidly increases with aging. It is especially important to better understand factors involving in cognitive decline for the countries where the older population is growing rapidly. The aim of this study was to examine the association between socio-demographic and health-related factors and cognitive impairment in the elderly in Taiwan.Methods: We analysed data from 2119 persons aged 65 years and over who participated in the 2005 National Health Interview Survey. Cognitive impairment was defined as having the score of the Mini Mental State Examination lower than 24. The χ²test and multiple logistic regression models were used to evaluate the association between cognitive impairment and variables of socio-demography, chronic diseases, geriatric conditions, lifestyle, and dietary factors.Results: The prevalence of cognitive impairment was 22.2%. Results of multivariate analysis indicated that low education, being single, low social support, lower lipid level, history of stroke, physical inactivity, non-coffee drinking and poor physical function were associated with a higher risk of cognitive impairment.Conclusion: Most of the characteristics in relation to cognitive impairment identified in our analysis are potentially modifiable. These results suggest that improving lifestyle behaviours such as regular exercise and increased social participation could help prevent or decrease the risk of cognitive impairment. Further investigations using longitudinal data are needed to clarify our findings.

We intended to explore the effect of level of frailty on, and relationship with, 1-, 3-, and 6-month postoperative emergency department visits, readmissions, and mortality. This is a prospective multicenter observational cohort study design. Patients aged 50 years or older treated for hip fracture (n = 245) were taken from Orthopedic wards in one medical center (n = 131) and one district hospital (n = 114) in Changhua County, Taiwan. Frailty was defined as measured by the validated Clinical Frailty Scale and categorized as robust, pre-frail, and frail. We used Kaplan-Meier analysis to estimate survival rates and Cox regression to estimate the risk of frailty associated with adverse outcomes. To examine the longitudinal associations between frailty and adverse outcome, the cross-lagged models were explored. Of the 245 patients, 55 (22.4%) were classified as frail, 113 (46.1%) as pre-frail, and 77 (31.4%) as robust. More cumulative events occurred for frail than for robust patients for each adverse outcome. Frailty has long-term effect on each adverse outcome after discharge, rather than the effect simultaneously. Targeting pre-frailty and frailty is essential for prevent adverse outcomes and improving the overall health of older adults after discharge for hip fracture.

A database for hepatocellular carcinoma (HCC) patients who had received hepatic resection was used to develop prediction models for 1-, 3- and 5-year disease-free survival based on a set of clinical parameters for this patient group. The three prediction models included an artificial neural network (ANN) model, a logistic regression (LR) model, and a decision tree (DT) model. Data for 427, 354 and 297 HCC patients with histories of 1-, 3- and 5-year disease-free survival after hepatic resection, respectively, were extracted from the HCC patient database. From each of the three groups, 80% of the cases (342, 283 and 238 cases of 1-, 3- and 5-year disease-free survival, respectively) were selected to provide training data for the prediction models. The remaining 20% of cases in each group (85, 71 and 59 cases in the three respective groups) were assigned to validation groups for performance comparisons of the three models. Area under receiver operating characteristics curve (AUROC) was used as the performance index for evaluating the three models. The ANN model outperformed the LR and DT models in terms of prediction accuracy. This study demonstrated the feasibility of using ANNs in medical decision support systems for predicting disease-free survival based on clinical databases in HCC patients who have received hepatic resection.