Injury prevention priority setting based on the National Injury Surveillance data in Korea.

For the reduction of injury burden, injury prevention efforts are essential. However, financial, material and human resources are far from sufficient, and this situation necessitates setting priorities for effective injury prevention. Hence, the aim of this study is to prioritise 13 injury mechanisms for prevention of injury using four injury burden criteria. National death certificate, hospital discharge data and emergency data during 2004 were used. According to the 13 most frequent injury mechanisms (ICD-10), mortality priority score, years of potential life lost (YPLL) priority score, morbidity priority score and hospital charge priority score were calculated. Injury mechanisms which were ranked fourth or higher on at least three of the four criteria scores were arranged in rank orders. Traffic crashes ranked highest on all four of the priority criteria for injury prevention followed by fall and poisoning in second and third positions, respectively. Categorised by age groups, in 0-19 year olds, traffic crashes, suffocation and drowning; in 20-59 years, traffic crashes, suffocation and poisoning; and in 60 and over, traffic crashes and fall were shown to be significant injury prevention priority in rank orders. Injury prevention priorities identified from this study could be useful in strategically allocating limited resources and implementing more focused prevention policies in Korea.

Acute Physiology and Chronic Health Evaluation II and Simplified Acute Physiology Score II in predicting hospital mortality of neurosurgical intensive care unit patients.

We study the predictive power of Acute Physiology and Chronic Health Evaluation II (APACHE II) and Simplified Acute Physiology Score II (SAPS II) in neurosurgical intensive care unit (ICU) patients. Retrospective investigation was conducted on 672 consecutive ICU patients during the last 2 yr. Data were collected during the first 24 hours of admission and analyzed to calculate predicted mortality. Mortality predicted by two systems was compared and, multivariate analyses were then performed for subarachnoid hemorrhage (SAH) and traumatic brain injury (TBI) patients. Observed mortality was 24.8% whereas predicted mortalities were 37.7% and 38.4%, according to APACHE II and SAPS II. Calibration curve was close to the line of perfect prediction. SAPS II was not statistically significant according to a Lemeshow-Hosmer test, but slightly favored by area under the curve (AUC). In SAH patients, SAPS II was an independent predictor for mortality. In TBI patients, both systems had independent prognostic implications. Scoring systems are useful in predicting mortality and measuring performance in neurosurgical ICU setting. TBI patients are more affected by systemic insults than SAH patients, and this discrepancy of predicting mortality in each neurosurgical disease prompts us to develop a more specific scoring system targeted to cerebral dysfunction.

Development and validation of the Excess Mortality Ratio-adjusted Injury Severity Score Using the International Classification of Diseases 10th Edition.

OBJECTIVES: This study aimed to develop and validate a new method for measuring injury severity, the excess mortality ratio-adjusted Injury Severity Score (EMR-ISS), using the International Classification of Diseases 10th Edition (ICD-10). METHODS: An injury severity grade similar to the Abbreviated Injury Scale (AIS) was converted from the ICD-10 codes on the basis of quintiles of the EMR for each ICD-10 code. Like the New Injury Severity Score (NISS), the EMR-ISS was calculated from three maximum severity grades using data from the Korean National Injury Database. The EMR-ISS was then validated using the Hosmer-Lemeshow goodness-of-fit chi-square (HL chi-square, with lower values preferable), the area under the receiver operating characteristic curve (AUC-ROC), and the Pearson correlation coefficient to compare it with the International Classification of Diseases 9th Edition-based Injury Severity Score (ICISS). Nationwide hospital discharge abstract data (DAD) from stratified-sample general hospitals (n = 150) in 2004 were used for an external validation. RESULTS: The total number of study subjects was 29,282,531, with five subgroups of particular interest identified for further study: traumatic brain injury (TBI, n = 3,768,670), traumatic chest injury (TCI,n = 1,169,828), poisoning (n = 251,565), burns (n = 869,020), and DAD (n = 26,374). The HL chi-square was lower for EMR-ISS than for ICISS in all groups: 42,410.8 versus 55,721.9 in total injury, 7,139.6 versus 20,653.9 in TBI, 6,603.3 versus 4,531.8 in TCI, 2,741.2 versus 9,112.0 in poisoning, 764.4 versus 4,532.1 in burns, and 28.1 versus 49.4 in DAD. The AUC-ROC for death was greater for EMR-ISS than for ICISS: 0.920 versus 0.728 in total injury, 0.907 versus 0.898 in TBI, 0.675 versus 0.799 in TCI, 0.857 versus 0.900 in poisoning, 0.735 versus 0.682 in burns, and 0.850 versus 0.876 in DAD. The Pearson correlation coefficient between the two scores was )0.68 in total injury, )0.76 in TBI, )0.86 in TCI, )0.69 in poisoning,)0.58 in burns, and )0.75 in DAD. CONCLUSIONS: The EMR-ISS showed better calibration and discrimination power for prediction of death than the ICISS in most injury groups. The EMR-ISS appears to be a feasible tool for passive injury surveillance of large data sets, such as insurance data sets or community injury registries containing diagnosis codes. Additional further studies for external validation on prospectively collected data sets should be considered.