Table 5.

Model calibration assessment of predicting in-hospital mortality using Elixhauser measures from three different models based on the 2021 AHRQ POA guidelines^a

Model	Traditional logistic regression			Elastic net model			Artificial neural network
Measure	E1	E2	E3	E1	E2	E3	E1	E2	E3
MAE	0.050	0.050	0.050	0.050	0.050	0.050	0.048	0.048	0.047
H-L statistics	1.070	0.648	0.667	1.070	0.531	0.583	7.601	2.912	1.942
H-L p-value	0.998	0.999	0.999	0.998	0.999	0.999	0.473	0.940	0.980
Cox intercept	−0.005	−0.019	−0.031	−0.005	−0.018	−0.029	−0.003	−0.011	0.007
Cox slope	1.001	0.996	0.992	1.001	0.996	0.993	0.996	0.991	1.002

^aModels were built on training data (n=1,267,467 patient admissions), and model discrimination performance results were from testing data (n=528,704 patient admissions); E1: VW Elixhauser composite score; E2: 31 individual Elixhauser comorbidities; E3: 31 individual Elixhauser comorbidities plus other patient characteristics; MAE=Mean absolute error; H-L= Hosmer–Lemeshow.