Table 3

Discrimination performance of laboratory test for the prediction of hospital mortality in logistic regression models with and without healthcare process variables

VariablesAUC for model 1 (95% CI)AUC for model 2 (95% CI)P value*
PaO20.664 (0.634 to 0.694)0.639 (0.609 to 0.670)0.001
Hematocrit0.605 (0.573 to 0.636)0.583 (0.554 to 0.613)0.147
Potassium0.612 (0.580 to 0.644)0.600 (0.570 to 0.631)0.356
Albumin0.594 (0.567 to 0.622)0.590 (0.563 to 0.618)0.333
Glucose0.630 (0.601 to 0.660)0.555 (0.534 to 0.575)<0.001
Bilirubin0.611 (0.581 to 0.640)0.603 (0.574 to 0.632)0.022
Bicarbonate0.692 (0.661 to 0.723)0.664 (0.635 to 0.694)0.001
WBC0.634 (0.603 to 0.666)0.610 (0.580 to 0.641)0.015
BUN0.681 (0.650 to 0.712)0.665 (0.635 to 0.695)0.025
pH0.665 (0.635 to 0.695)0.636 (0.605 to 0.666)0.001
Sodium0.615 (0.583 to 0.647)0.587 (0.558 to 0.616)0.043
Creatinine0.671 (0.640 to 0.701)0.620 (0.590 to 0.650)<0.001
  • *The AUCs of two ROC curves were compared using DeLong method, and p values for the comparisons were reported.

  • Model 2 included only the pathophysiology value; model 1 included healthcare process variables (number of test for the first 24 hours, clock time of the test and the measurement time relative to ICU admission) and the pathophysiology value of corresponding test.

  • AUC, area under curve; BUN, blood urea nitrogen; ROC, receiver operating characteristic; WBC, white cell count.