Is Chen

Trends and outcome predictors after traumatic brain injury surgery: a nationwide population-based study in Taiwan

OBJECT The authors sought to analyze trends in hospital resource utilization and mortality rates in a population of patients who had received traumatic brain injury (TBI) surgery. METHODS This nationwide population-based cohort study retrospectively analyzed 18,286 patients who had received surgical treatment for TBI between 1998 and 2010. The multiple linear regression model and Cox proportional hazards model were used for multivariate assessment of outcome predictors. RESULTS The prevalence rate of surgical treatment for patients with TBI gradually but significantly (p < 0.001) increased by 47.6% from 5.0 per 100,000 persons in 1998 to 7.4 per 100,000 persons in 2010. Age, sex, Deyo-Charlson comorbidity index score, hospital volume, and surgeon volume were significantly associated with TBI surgery outcomes (p < 0.05). Over the 12-year period analyzed, the estimated mean hospital treatment cost increased 19.06%, whereas the in-hospital mortality rate decreased 10.9%. The estimated mean time of overall survival after TBI surgery (± SD) was 83.0 ± 4.2 months, and the overall in-hospital and 1-, 3-, and 5-year survival rates were 74.5%, 67.3%, 61.1%, and 57.8%, respectively. CONCLUSIONS These data reveal an increased prevalence of TBI, especially in older patients, and an increased hospital treatment cost but a decreased in-hospital mortality rate. Health care providers and patients should recognize that attributes of the patient and of the hospital may affect hospital resource utilization and the mortality rate. These results are relevant not only to other countries with similar population sizes but also to countries with larger populations.

Long-Term Prediction of Emergency Department Revenue and Visitor Volume Using Autoregressive Integrated Moving Average Model

This study analyzed meteorological, clinical and economic factors in terms of their effects on monthly ED revenue and visitor volume. Monthly data from January 1, 2005 to September 30, 2009 were analyzed. Spearman correlation and cross-correlation analyses were performed to identify the correlation between each independent variable, ED revenue, and visitor volume. Autoregressive integrated moving average (ARIMA) model was used to quantify the relationship between each independent variable, ED revenue, and visitor volume. The accuracies were evaluated by comparing model forecasts to actual values with mean absolute percentage of error. Sensitivity of prediction errors to model training time was also evaluated. The ARIMA models indicated that mean maximum temperature, relative humidity, rainfall, non-trauma, and trauma visits may correlate positively with ED revenue, but mean minimum temperature may correlate negatively with ED revenue. Moreover, mean minimum temperature and stock market index fluctuation may correlate positively with trauma visitor volume. Mean maximum temperature, relative humidity and stock market index fluctuation may correlate positively with non-trauma visitor volume. Mean maximum temperature and relative humidity may correlate positively with pediatric visitor volume, but mean minimum temperature may correlate negatively with pediatric visitor volume. The model also performed well in forecasting revenue and visitor volume.