OBJECTIVE: To present a new systematic approach for summarizing @entity1230 diagnosis data into @entity1 injury profiles. DESIGN: International Classification of Diseases, ninth revision, clinical modification injury diagnosis codes were classified using a modification of the Barell body region by nature of injury diagnosis matrix, then grouped by body region, injury nature, or a combination of both. Profiles were built which describe @entity1 ' injury combinations based on matrix units, enabling the analysis of @entity1 , and not only the study of injuries. SETTING: The Israeli national @entity130 registry was used to retrieve @entity1 demographic data, @entity130 , and information on treatment and outcome. @entity1 or subjects: All hospitalized @entity1 injured in road traffic accidents and included in the @entity130 registry from January 1997 to December 2000 were included. MAIN OUTCOME MEASURES: @entity1 profiles consisting of body regions, injury natures, their combination, and their clinical outcomes. RESULTS: The study population comprised 17459 @entity1 . Head and @entity9389 were the most frequent in all subpopulations except for motorcyclists who sustained most injuries in the extremities. @entity174 were the most common injury nature (60%). Pedestrians and drivers had the highest proportion of @entity1230 in both profiles. Forty eight percent of the @entity1 had a single cell profile. The most frequent conditions as a sole condition were extremity @entity174 (14%), @entity542 (11%), and injuries of other nature to the torso (6%). Mortality, length of stay, and intensive care unit treatment varied dramatically between profiles and increased for @entity1230 profiles. Inpatient @entity204 was an outcome for 3.3% overall; however, in @entity1 with an internal injury to the head and torso, inpatient @entity204 rate was nine times higher, at 31%. CONCLUSIONS: Profiles maintain information on body region and nature of injury. The use of injury profiles in describing the injured improves the understanding of casemix and can be useful for efficient staffing in multidisciplinary @entity130 teams and for various comparisons.

A new approach to the analysis of XXXX using data from a national @entity130 registry.

@entity1 :: ('9606', 'Species') :: ['Patients', 'patients', 'patient', 'Patient'] @entity174 :: ('MESH:D050723', 'Disease') :: ['fractures', 'Fractures'] @entity9389 :: ('MESH:D019838', 'Disease') :: ['neck injuries'] @entity542 :: ('MESH:D006259', 'Disease') :: ['internal injuries to the head'] @entity1230 :: ('MESH:D009104', 'Disease') :: ['multiple injury', 'multiple injuries'] @entity204 :: ('MESH:D003643', 'Disease') :: ['death'] @entity130 :: ('MESH:D014947', 'Disease') :: ['injury details', 'trauma']
@entity1230 :: (MESH:D009104,Disease) :: ['multiple injuries']
