Objective: This study investigated the characteristics and survival rates of patients with unintentional severe trauma who visited a regional trauma center (TC) or a non-TC.Methods: This retrospective, national, population-based, observational, case-control study included patients with abnormal Revised Trauma Score from January 2018 to December 2018. We divided hospitals into two types, TC and non-TC, and compared several variables including in-hospital mortality. Propensity score matching was used to eliminate the effect of confounding variables that influence survival outcome variables.Results: Of the 25,743 patients, 5,796 visited a TC and 19,947 visited a non-TC. Compared to patients treated at non-TCs, patients treated at TCs were more likely to have a higher Injury Severity Score (TC, 11.5; non-TC, 7.4; P<0.001), higher rate of surgery or transcatheter arterial embolization (TC, 39.2%; non-TC, 17.6%; P<0.001), and higher admission rate (TC, 64.7%; non-TC, 36.9%; P<0.001) through the emergency department. After propensity score matching, 2,800 patients from both groups were analyzed. Patients in the TC had a higher survival rate than patients that were not treated in the TC (TC, 83.0%; non-TC, 78.6%; P=0.003).
Conclusion:This study using Korean emergency medical services data showed that initial transport to trauma centers might be effective for mortality reduction. Further research is required because of limitations with use of single-year data and retrospective design.
Principal component analysis (PCA) describes the variation of multivariate data in terms of a set of uncorrelated variables. Since each principal component is a linear combination of all variables and the loadings are typically non-zero, it is difficult to interpret the derived principal components. Sparse principal component analysis (SPCA) is a specialized technique using the elastic net penalty function to produce sparse loadings in principal component analysis. When data are structured by groups of variables, it is desirable to select variables in a grouped manner. In this paper, we propose a new PCA method to improve variable selection performance when variables are grouped, which not only selects important groups but also removes unimportant variables within identified groups. To incorporate group information into model fitting, we consider a hierarchical lasso penalty instead of the elastic net penalty in SPCA. Real data analyses demonstrate the performance and usefulness of the proposed method.
Local composite quantile regression is a useful non-parametric regression method widely used for its high efficiency. Data smoothing methods using kernel are typically used in the estimation process with performances that rely largely on the smoothing parameter rather than the kernel. However, L 2 -norm is generally used as criterion to estimate the performance of the regression function. In addition, many studies have been conducted on the selection of smoothing parameters that minimize mean square error (MSE) or mean integrated square error (MISE). In this paper, we explored the optimality of selecting smoothing parameters that determine the performance of non-parametric regression models using local linear composite quantile regression. As evaluation criteria for the choice of smoothing parameter, we used mean absolute error (MAE) and mean integrated absolute error (MIAE), which have not been researched extensively due to mathematical difficulties. We proved the uniqueness of the optimal smoothing parameter based on MAE and MIAE. Furthermore, we compared the optimal smoothing parameter based on the proposed criteria (MAE and MIAE) with existing criteria (MSE and MISE). In this process, the properties of the proposed method were investigated through simulation studies in various situations.
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