The objective of this study was to describe a population of children admitted to a tertiary care pediatric hospital with severe trauma to identify key areas for injury prevention research, and programming.
Retrospective chart review conducted on all children 0-17 years admitted to the Children's Hospital of Eastern Ontario (CHEO) between April 1, 1996, and March 31, 2000, following acute trauma. Each record was reviewed and assigned an ISS using the AIS 1990 revision. All cases with an ISS > 11 were included in the study.
There were 2610 trauma cases admitted to CHEO over the study period. Of these, 237 (9.1%) had severe trauma (ISS > 11). Sixty-two percent were male. Twenty-nine percent were between the ages of 10 and 14 years, 27% between 5 and 9 years, 16% between 15 and 17 years, 15% between 1 and 4 years, and 13% less than 1 year old. The most common mechanisms of injury were due to motor vehicle traffic (39%), falls (24%), child abuse (8%), and sports (5%). Of those resulting from motor vehicle traffic, 53 (57%) were occupants, 22 (24%) were pedestrians, and 18 (19%) were cyclists. When combining traffic and nontraffic mechanisms, 26 (11% of all severe trauma cases) occurred as a result of cycling incidents. The most severe injury in 65% of patients was to the head and neck body region.
Research efforts and activities to prevent severe pediatric trauma in our region should focus on road safety, protection from head injuries, avoidance of falls, and prevention of child abuse.
Hospital administrative databases are a useful source of population-level data on injured patients; however, these databases use the International Classification of Diseases (ICD) system, which does not provide a direct means of estimating injury severity. We created and validated a crosswalk to derive Abbreviated Injury Scale (AIS) scores from injury-related diagnostic codes in the tenth revision of the ICD (ICD-10).
We assessed the validity of the crosswalk using data from the Ontario Trauma Registry Comprehensive Data Set (OTRCDS). The AIS and Injury Severity Scores (ISS) derived using the algorithm were compared with those assigned by expert abstractors. We evaluated the ability of the algorithm to identify patients with AIS scores of 3 or greater. We used ? and intraclass correlation coefficients (ICC) as measures of concordance.
In total, 10 431 patients were identified in the OTRCDS. The algorithm accurately identified patients with at least 1 AIS score of 3 or greater (? 0.65), as well as patients with a head AIS score of 3 or greater (? 0.78). Mapped and abstracted ISS were similar; ICC across the entire cohort was 0.83 (95% confidence interval 0.81-0.84), indicating good agreement. When comparing mapped and abstracted ISS, the difference between scores was 10 or less in 87% of patients. Concordance between mapped and abstracted ISS was similar across strata of age, mechanism of injury and mortality.
Our ICD-10-to-AIS algorithm produces reliable estimates of injury severity from data available in administrative databases. This algorithm can facilitate the use of administrative data for population-based injury research in jurisdictions using ICD-10.