The following variables were collected: age, gender, mechanism of burn, total and deep burn surface, inhalation injury and need of mechanical ventilation.
Gender, mechanism of injury, Gustillo and anatomical classification, ISS and intramedullary nailing of tibial fracture did not influence ACS.
OR Odds ratio; ¢ Model covariates: Age, gender, mechanism of injury, surgery, T-RTS, hypotension, head injury, indicator variable KAMC vs. NTDB, ISS and (KAMC vs. NTDB X ISS).
§ Model covariates: Age, gender, mechanism of injury, surgery, T-RTS, hypotension, ISS, indicator variable KAMC vs. NTDB, head injury and (KAMC vs. NTDB X head injury).
The collected data included age, gender, mechanism of injury, Injury Severity Score (ISS), duration of hospital and ICU-stay and in-hospital mortality.
The number and proportion of injuries were examined for each year by age, gender, mechanism of injury, and type of injury.
¥ Model covariates: Gender, mechanism of injury, surgery, T-RTS, hypotension, head injury, ISS, indicator variable KAMC vs. NTDB, age and (KAMC vs. NTDB X age).
Table 2 shows the number and proportion of injuries for each year by age, gender, mechanism of injury, and type of injury, as well as the results of their linear models.
We were unable to detect a significant relation between age, gender, mechanism of trauma, presence or absence of associated injuries, type of fracture, or the use of graft and the final functional result; this may be due to the small number in our sample.
Because we saw a significant increase in moderate and severe/critical injuries over the entire study period, we further fitted a trend line (0 joinpoint) to the data by age group, gender, mechanism and type of injury using linear models to further characterize factors behind these observed trends.
