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. 2022 Dec 23;10:54. doi: 10.1186/s40635-022-00482-3

Table 2.

Performance of various multivariable logistic regression models to predict 6-month post-injury outcomes

Model Favorable vs unfavorable outcome Alive vs dead
AUC (95% CI) AIC Nagelkerke R2 AUC (95% CI) AIC Nagelkerke R2
TBI-IMPACT 0.86 (0.76–0.96) 66.00 0.51 0.78 (0.66–0.90) 68.37 0.27
TBI-IMPACT + % Time CPP < 60 mmHg + % Time CPP > 70 mmHg 0.86 (0.76–0.96) 68.81 0.53 0.81 (0.69–0.93) 69.44 0.33
TBI-IMPACT + % Time CPP < 60 mmHg + % Time CPP > 70 mmHg + % Time PbtO2 < 20 mmHg 0.87 (0.77–0.96) 69.83 0.54 0.81 (0.70–0.93) 71.15 0.34
TBI-IMPACT + % Time CPP < LLR 0.86 (0.76–0.96) 65.81 0.53 0.80 (0.68–0.93) 65.85 0.32
TBI-IMPACT + % Time CPP > ULR 0.86 (0.77–0.96) 66.66 0.52 0.81 (0.69–0.92) 67.40 0.28
TBI-IMPACT + % Time CPP < LLR + % Time CPP > ULR 0.87 (0.77–0.96) 67.77 0.53 0.80 (0.68–0.93) 67.48 0.33

The TBI-IMPACT model included age, admission GCS, admission pupil exam, and Marshall score

AIC Akaike Information Criterion, AUC Area under the curve, CPP cerebral perfusion pressure, GCS Glasgow Coma Scale, LLR lower limit of reactivity, PbtO2 brain tissue oxygenation, ULR upper limit of reactivity