• Journal of Trauma and Injury
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• v.24 no.2
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• pp.89-94
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• 2011

Purpose: A few studies have assessed the factors affecting the prognoses for major trauma patients and those improving the circumstances when dealing with the trauma system. In that light, we analyzed factors, such as pre-hospital factors, the time to admission, the length of stay in the emergency department (ED) and emergency operation, influencing the outcomes for trauma patients. Methods: The patients who visited our emergency department from April 1, 2009, to February 29, 2011, due to major trauma were enrolled in the study. The inclusion criterion was a revised trauma score (RTS) < 7 or injury severity score (ISS) ${\geq}$ 16. We used reviews of medical records, to analyze each step of emergency medical care with respect to patients' sex, age, visit time and visit date. Continuous variables were described as a median with an interquartile range, and we compared the variables between the survival and the mortality groups by using the Mann-Whitney U test. Fisher's exact test was used for nominal variables. Using the variables that showed statistical significance in univariate comparisons, we performed a logistic regression analysis, and we tested the model's adequacy by the using the Hosmer-Lemeshow method. Results: A total of 261 patients with major trauma satisfied either the RTS score criterion or the ISS score criterion. Excluding 12 patients with missing data, 249 patients were included in this study. The overall mortality rate was 16.9%. Time to ED arrival, time to admission, time of ED stay, RTS, ISS, and visit date being a holiday showed statistically significant differences between the survival and the mortality groups in the univariate analysis. RTS, ISS, length of ED stay, and visit date being a holiday showed statistical significance in the multivariate analysis. Conclusion: The mortality rate did not show a significant relationship with the time to ED arrival, use of 119, on time to admission. Rather, it elicited a quite significant correlation with the trauma scoring system (RTS and ISS), the time of ED stay, and the visit date being a holiday.

• Journal of Trauma and Injury
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• v.24 no.2
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• pp.98-104
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• 2011

Purpose: The aim of this study was to evaluate the quality of the trauma care system of our hospital, in which emergency physicians care for major trauma patients in the emergency intensive care unit (ICU) in consultation with intervention radiologists and surgeons. Methods: This was a retrospective observational study conducted in an emergency ICU of a tertiary referral hospital. We enrolled consecutive patients who had been admitted to our emergency ICU with major trauma from March 2007 to September 2010. We collected data with respect to demographic findings, mechanisms of injury, the trauma and injury severity score (TRISS), emergency surgery, angiographic intervention, and 6-month mortality. Then, we compared the observed and predicted survivals of the patients. The Hosmer-Lemeshow test and calibration plots by using 10 groups, one for each decile, of predicted mortality were used to evaluate the fitness of TRISS. P-values of greater than 0.05 represent a fair calibration. Results: Among 116 patients, 12 (10.34%) were dead within 6 months after admission to the ICU, and 29 (25.00%) and 38 (32.80%) patients received emergency surgery and angiographic intervention, respectively. The mean injury severity score and revised trauma score were $36.97{\pm}17.73$ and $7.84{\pm}6.75$, respectively. The observed survival and the predicted survival of the TRISS were 89.66% (95% confidence interval [CI]: 84.03~95.28%) and 69.85% (95% CI: 63.80~75.91%), respectively. The calibration plots showed that the observed survival of our patients was consistently higher than the predicted survival of the TRISS ($p$ <0.001). Conclusion: The observed survival for the trauma care system of our hospital, in which emergency physicians care for major trauma patients in the emergency ICU in consultation with intervention radiologists and surgeons, was higher than the predicted survival of the TRISS.

• Health Policy and Management
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• v.17 no.3
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• pp.87-105
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• 2007

The purpose of this study was to analyze whether nonemergency, isolated coronary artery bypass graft (CABG) surgery for high- or low-risk patients biases the assessment of the risk-adjusted mortality rates of hospitals. This study used 2002 National Health Insurance claims data for tertiary hospitals in Korea. The study sample consisted of 1,959 patients from 23 tertiary hospitals. The risk-adjustment model used the patients' biological, admission, and comorbidity data identified in the claims. The subjects were classified into high- and low-risk groups based on predicted surgical risk. The crude mortality rates and risk-adjusted mortality rates for low-risk, high-risk, and all patients in a hospital were compared based on the rank and the four intervals defined by quartile. Also, the crude mortality rates of the three groups were compared with their 95% confidence intervals of predicted mortality rates. The C-statistic (0.83) and Hosmer-Lemeshow test ($X^2$=11.47, p=0.18) indicated that the risk-adjustment model performed well. Presenting crude mortality rates with their 95% confidence intervals of predicted rates showed higher agreements among the three groups than using the rank or intervals of mortality rates defined by quartile in the hospital performance assessment. The crude mortality rates for the low-risk patients in 21 of the 23 hospitals were located on the same side of their 95% confidence intervals compared to that for all patients. High-risk patients and all patients differed at only one hospital. In conclusion, the impact of risk selection by hospital on the assessment results was the smallest when comparing the crude inpatient mortality rates of CABG patients with the 95% confidence intervals of predicted mortality rates. Given the increasing importance of quality improvements in Korean health policy, it will be necessary to use the appropriate method of releasing the hospital performance data to the public to minimize any unwanted impact such as risk-based hospital selection.