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Table 3 Multivariate logistic regression analysis for predicting the need for life-saving interventions amongst trauma patients

From: Development of a heart rate variability and complexity model in predicting the need for life-saving interventions amongst trauma patients

  OR (95% CI) P value
All variable
 Age 1.026 (0.987–1.066) 0.200
 Age greater than 56 0.363 (0.079–1.674) 0.194
 Systolic blood pressure 0.989 (0.972–1.006) 0.201
 Heart rate 1.021 (0.997–1.044) 0.085
 Glasgow Coma Scale 0.819 (0.699–0.960) 0.014
 avHR 1.002 (0.973–1.031) 0.916
 NN50 0.996 (0.958–1.036) 0.840
 pNN50 1.043 (0.903–1.204) 0.570
 LF 1.000 (0.999–1.002) 0.328
 HF 0.999 (0.999–1.000) 0.224
 LF norm 0.817 (0.574–1.162) 0.261
 HF norm 0.842 (0.590–1.199) 0.340
 LF/HF 1.000 (0.998–1.002) 0.837
 Approximate entropy 1.866 (0.060–58.263) 0.723
 Sample entropy 0.364 (0.056–2.366) 0.290
 DFA1 10.157 (1.621–63.659) 0.013
 DFA2 3.758 (0.653–21.629) 0.138
Variables after backward Wald selection
 Glasgow Coma Scale 0.756 (0.660–0.864) < 0.001
 DFA-α1 3.932 (1.256–12.314) 0.019
 DFA-α2 5.200 (1.060–25.504) 0.042
  1. OR odds ratio, CI confidence interval, avHR mean of the instantaneous heart rate in electrocardiograms, NN50 number of consecutive RR intervals differing by more than 50 ms in electrocardiograms, pNN50 percentage of consecutive RR intervals differing by more than 50 ms in electrocardiograms, LF low frequency power in electrocardiograms, HF high frequency power in electrocardiograms, norm normalised, LF/HF ratio of LF power to HF power in electrocardiograms, TP total power derived from variance of all RR intervals in electrocardiograms, DFA detrended fluctuation analysis