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Table 2 Model Performance in predicting death up to 7 days post-discharge, as measured by AUC, AIC and BIC

From: Predicting the cumulative risk of death during hospitalization by modeling weekend, weekday and diurnal mortality risks

Model type

Variables used

Number of variables

AUC

AIC

BIC

Optimal cut-off point (ratio of deaths to survival)

Sensitivity

Specificity

PPV

NPV

Accuracy

Model 1: Background risk

Age, Gender

2

.711 (.708 -.715)

1.0710e + 006

1.0710e + 006

0.196

71.29

66.14

2.78

99.41

66.21

Model 2: Time exposure risk

2(a): Length of stay (LOS)

1

.710 (.705 - .714)

1.3155e + 006

1.3155e + 006

0.168

69.07

73.69

3.45

99.43

73.62

2(b): LOS (Weekday, weekend), admission time

3

.692 (.687 - .697)

1.1960e + 006

1.1961e + 006

0.145

71.97

70.21

3.18

99.5

70.23

2(c): LOS (Morning, evening, or night for each of seven days), admission time

22

.786 (.782 - .79)

1.3464e + 006

1.3467e + 006

0.228

64.62

74.57

3.34

99.4

74.44

Model 3: Disease model

Charlson comorbidity index

1

.786 (.783 - .79)

1.0826e + 006

1.0826e + 006

0.1

91.54

66.27

3.56

99.8

66.60

Model 4: Background plus time exposure risk

4(a): 1 + 2(a)

3

.73 (.726 - .739)

0.9408e + 006

0.94097e + 006

0.196

76.14

61.25

2.6

99.5

61.5

4(b): 1 + 2(b)

5

.743 (.739 - .747)

1.1519e + 006

1.1520e + 006

0.227

79.95

68.72

3.36

99.6

68.87

4(c): 1 + 2(c)

24

.883 (.88 - .886)

1.4713e + 006

1.4716e + 006

0.32

82.41

77.41

4.73

99.7

77.47

Model 5: Full model

5(a): 1 + 2(a) + 3

4

.891 (.888 - .894)

1.5143e + 006

1.5143e + 006

0.197

87.09

78.32

5.18

99.8

78.43

5(b): 1 + 2(b) + 3

6

.893 (.89 - .896)

1.5118e + 006

1.5119e + 006

0.224

86.86

78.27

5.16

99.8

78.38

5(c): 1 + 2(c) + 3

25

.923 (.921 - .926)

1.6769e + 006

1.6772e + 006

0.271

88.18

81.61

6.13

99.8

81.71

Model 6: DRG-specific models 5(c) trained and tested only single DRG subpopulation

R61: Lymphoma and Non-Acute Leukemia

25

.90 (.878 - .922)

4.7669e + 003

4.9223e + 003

0.149

81.45

82.63

34.67

97.5

82.51

E02: Other Respiratory System OR Procedures

25

.940 (.911 - .969)

2.6019e + 003

2.7281e + 003

0.398

80.0

95.25

21.05

99.6

94.99

F70: Major Arrhythmia and Cardiac Arrest

25

.762 (.736 - .802)

982.0188

1.1044e + 003

0.412

75.86

65.79

68.75

73.3

70.81

E64: Pulmonary Oedema and Respiratory Failure

25

.85 (.807 - .894)

466.95

476.32

0.616

71.34

88.41

87.5

73.05

79.32

J62: Malignant Breast Disorders

25

.903 (.874 - .933)

704.03

713.95

0.438

77.69

86.49

74.26

88.54

83.55

Model 7: Emergency Department admission 5(c) trained and tested only on ED or non ED subpopulations

ED admission

25

.909 (.905 - .912)

3.0017e + 005

3.0044e + 005

0.16

87.80

77.32

11.91

99.45

77.67

Non-ED admissions

25

.925 (.921 - .928)

1.4626e + 006

1.4629e + 006

0.218

87.70

84.38

4.47

99.9

84.41

  1. An optimal cut-off value is selected as the point in the receiver operating characteristic curve where the sum of sensitivity and specificity is maximum. Sensitivity, specificity, model accuracy, positive predictive value (PPV) and negative predictive value (NPV) are reported at the optimal ratio of patient deaths to survival.