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A 4-year review of severe pediatric trauma in eastern Ontario: a descriptive analysis.

https://arctichealth.org/en/permalink/ahliterature191929
Source
J Trauma. 2002 Jan;52(1):8-12
Publication Type
Article
Date
Jan-2002
Author
Martin H Osmond
Maureen Brennan-Barnes
Allyson L Shephard
Author Affiliation
Department of Pediatrics, University of Ottawa, Ottawa, Ontario, Canada. osmond@cheo.on.ca
Source
J Trauma. 2002 Jan;52(1):8-12
Date
Jan-2002
Language
English
Publication Type
Article
Keywords
Accident prevention
Accidental Falls - prevention & control - statistics & numerical data
Accidents, Traffic - prevention & control - statistics & numerical data
Adolescent
Age Distribution
Athletic Injuries - epidemiology - etiology - prevention & control
Child
Child Abuse - prevention & control - statistics & numerical data
Child, Preschool
Craniocerebral Trauma - epidemiology - etiology - prevention & control
Female
Hospitals, Pediatric - statistics & numerical data
Humans
Infant
Infant, Newborn
Male
Ontario - epidemiology
Retrospective Studies
Sex Distribution
Time Factors
Trauma Centers - statistics & numerical data
Trauma Severity Indices
Wounds and Injuries - epidemiology - etiology - prevention & control
Abstract
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.
PubMed ID
11791045 View in PubMed
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Overcoming barriers to population-based injury research: development and validation of an ICD10-to-AIS algorithm.

https://arctichealth.org/en/permalink/ahliterature127719
Source
Can J Surg. 2012 Feb;55(1):21-6
Publication Type
Article
Date
Feb-2012
Author
Barbara Haas
Wei Xiong
Maureen Brennan-Barnes
David Gomez
Avery B Nathens
Author Affiliation
Department of Surgery, St. Michael's Hospital, University of Toronto, Toronto, Ont. barbara.haas@utoronto.ca
Source
Can J Surg. 2012 Feb;55(1):21-6
Date
Feb-2012
Language
English
Publication Type
Article
Keywords
Abbreviated Injury Scale
Abstracting and Indexing as Topic
Adult
Algorithms
Female
Humans
International Classification of Diseases
Male
Ontario
Registries
Wounds and Injuries - classification
Abstract
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.
Notes
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PubMed ID
22269308 View in PubMed
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