Journal article Open Access
Background: Assessing the traumatic injuries severity, as well as estimating the severe trauma patient’s prognosis are the key moments in their management. Predictive models for severe trauma outcome need improvement.
Material and methods: In the clinical study (65 severe trauma patients), proteases, antiproteases and treatment outcome (survival/non-survival) were considered. There were used two statistical instruments – dimension reduction analysis (principal component analysis) to prepare the data for modeling and modeling itself through multivariate logistic regression.
Results: Principal component analysis evidenced 12 “latent” factors grouped in four models. The survival predictive model had the following characteristics: calibration χ²=1.547, df=7, р=.981; determination – 0.759; discrimination, sensitivity – 90.7%, specificity – 81.8 %, area under RОС curve – 0.95 (95%CI 0.912, 1.000). The model enrolled four “latent” factors (three destructive and one protective), male gender and ARDS development.
Conclusions: In our research, the survival predictive model for severe trauma patients on base of proteases/antiproteases system components after dimension reduction procedure was elaborated. The model showed good characteristics and needs validation to be implemented in daily clinical practice