Skip to main content

Table 1 Clinical characteristics (n = 348) and performance statistics. Age and lab values are shown as mean ± std.

From: Projecting COVID-19 disease severity in cancer patients using purposefully-designed machine learning

 

Clinical variable

Severe-late

Severe-early

Non-severe

Basic patient information

# of patients

71

71

206

 

Male (%)

49.3

49.3

50.0

 

Age (years)

62.6 ± 13.9

69.8 ± 12.9

60.3 ± 16.9

Labs

Absolute Lymphocyte Counts (K/mcL)

3.57 ± 17.04

1.30 ± 2.02

2.45 ± 14.00

 

D-Dimer (mcg/mL)

6.58 ± 7.26

3.52 ± 2.90

2.16 ± 1.75

 

Interleukin 6 (pg/mL)

469.60 ± 816.95

116.50 ± 90.25

62.62 ± 47.35

 

Ferritin (ng/mL)

1016.36 ± 1045.78

983.09 ± 1279.56

467.76 ± 541.13

 

Platelets (K/mcL)

171.31 ± 114.04

196.04 ± 112.71

218.33 ± 118.85

Cancer-related

Lymphoma (%)

11.3

5.6

1.9

 

Lung (%)

5.6

21.1

6.8

 

Leukemia (%)

19.7

8.5

5.3

Diagnosis (ICD)

I1 - Hypertensive diseases (%)

52.1

71.8

56.8

 

I4 - Cardiac disorders (%)

31.0

35.2

26.2

 

J4 - Chronic lower respiratory disease (%)

19.7

28.2

17.5

Radiology

Retic. Opacities (%)

16.9

29.6

14.6

 

Effusions (%)

12.7

15.5

7.8

 

Airspace Opacity (%)

32.4

81.7

38.3

Performance

AUROC (Our method)

0.704

0.829

0.710

 

AUROC (Yan et al.)

0.456

0.634

0.499

 

AUROC (Huang et al.)

0.600

0.638

0.604

 

Avg. Precision (Our method)

0.366

0.578

0.772

 

Avg. Precision (Yan et al.)

0.190

0.315

0.588

 

Avg. Precision (Huang et al.)

0.291

0.326

0.691