Machine learning techniques for mortality prediction in critical traumatic patients: anatomic and physiologic variables from the RETRAUCI study

Table 4 Logistic Regression Binary model for mortality prediction

Variable	B coefficient	Standard error	OR (95% CI)	p-value
Age	0.051	0.002	1.05 (1.04–1.06)	< 0.001
AHEAD
0	Reference
1	0.091	0.281	1.10 (0.63–1.90)	0.746
2	0.114	0.202	1.12 (0.75–1.66)	0.573
3	0.150	0.147	1.16 (0.87–1.55)	0.307
4	0.911	0.135	2.49 (1.91–3.24)	< 0.001
5	2.011	0.128	7.47 (5.82–9.60)	< 0.001
6	4.394	1.200	80.99 (7.72–849.01)	< 0.001
ATHORAX
0	Reference
1	−0.256	0.305	0.77 (0.43–1.41)	0.401
2	−0.048	0.169	0.95 (0.68–1.33)	0.777
3	−0.513	0.124	0.60 (0.47–0.76)	< 0.001
4	−0.122	0.140	0.88 (0.67–1.16)	0.381
5	−0.036	0.195	0.96 (0.66–1.41)	0.855
6	−1.263	1.199	0.28 (0.03–3.12)	0.303
AUPPEREXT
0	Reference
1	0.107	0.306	1.11 (0.61–2.02)	0.727
2	−0.315	0.121	0.73 (0.57–0.92)	0.009
3	−0.544	0.295	0.58 (0.33–1.03)	0.065
4	−1.17	1.082	0.31 (0.03–2.57)	0.278
ALOWEREXT
0	Reference
1	−0.783	0.380	0.46 (0.22–0.96)	0.039
2	−0.559	0.169	0.57 (0.41–0.79)	0.001
3	−0.509	0.158	0.60 (0.44–0.82)	0.001
4	−0.931	0.251	0.39 (0.24–0.64)	< 0.001
5	0.400	0.228	1.49 (0.95–2.33)	0.079
PointSBP
4	Reference
3	0.316	0.152	1.37 (1.01–1.85)	0.038
2	0.356	0.148	1.43 (1.07–1.91)	0.016
1	0.815	0.375	2.26 (1.08–4.71)	0.030
0	1.325	0.185	3.76 (2.62–5.40)	< 0.001
PointGCS
4	Reference
3	0.063	0.142	1.07 (0.81–1.41)	0.658
2	0.392	0.137	1.48 (1.13–1.94)	0.004
1	1.072	0.157	2.92 (2.15–3.97)	< 0.001
0	1.810	0.122	6.11 (4.81–7.76)	< 0.001
MV	0.858	0.142	2.36 (1.93–2.89)	< 0.001
MASSIVEHEM	0.554	0.153	1.74 (1.29–2.35)	< 0.001
HEMODINAM	0.818	0.099	2.27 (1.86–2.75)	< 0.001
RESPIRATORY	0.541	0.106	1.72 (1.39–2.11)	< 0.001
AKIDNEY	0.641	0.098	1.89 (1.57–2.30)	< 0.001
COAGULOP	0.610	0.109	1.84 (1.49–2.28)	< 0.001

RF Respiratory frequency, SBP Systolic blood pressure, GCS Glasgow coma score. MV Mechanical ventilation, OR Odds Ratio, CI Confidence interval

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