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Table 3 Patient baseline characteristics

From: Joint modeling strategy for using electronic medical records data to build machine learning models: an example of intracerebral hemorrhage

 

Death

(n = 1298) (%)

Survival

(n = 2909) (%)

\({\chi }^{2}\)

P*

Age

  

9.10

0.059

 40–54

308 (23.7)

591 (20.3)

  

 55–64

294 (22.7)

648 (22.3)

  

 65–74

434 (33.4)

1032 (35.5)

  

 75–84

235 (18.1)

551 (18.9)

  

 ≥ 85

27 (2.1)

87 (3.0)

  

Gender

  

28.92

< 0.001

 Male

788 (60.7)

1506 (51.8)

  

 Female

510 (39.3)

1403 (48.2)

  

GCS

  

23.51

< 0.001

 13–15

1158 (89.2)

2706 (93.0)

  

 9–12

107 (8.3)

175 (6.0)

  

 3–8

33 (2.5)

28 (1.0)

  

Hypertension

  

13.98

< 0.001

 No

977 (75.3)

2338 (80.4)

  

 Yes

321 (24.7)

571 (19.6)

  

Diabetes

  

0.08

0.509

 No

1294 (99.7)

2903 (99.8)

  

 Yes

4 (0.3)

6 (0.2)

  

Surgery

  

148.11

< 0.001

 No

1057 (81.4)

2725 (93.7)

  

 Yes

241 (18.6)

184 (6.3)

  

Infection

  

786.05

< 0.001

 No

811 (62.5)

2780 (95.6)

  

 Yes

487 (37.5)

129 (4.4)

  

ICH location

  

168.50

< 0.001

 Supratentorial superficial

1001 (77.1)

2480 (85.3)

  

 Supratentorial deep

132 (10.2)

239 (8.2)

  

 Cerebellar

81 (6.2)

157 (5.4)

  

 Brain stem

82 (6.3)

9 (0.3)

  

 IVH

2 (0.1)

24 (0.8)

  

Supratentorial hemorrhage volume

  

185.46

< 0.001

 < 30ml

1040 (80.1)

2733 (93.9)

  

 ≥ 30ml

258 (19.9)

176 (6.1)

  

Infratentorial hemorrhage volume

  

6.15

0.013

 < 10ml

1285 (99.0)

2898 (99.6)

  

 ≥ 10ml

13 (1.0)

11 (0.4)

  
  1. *: The P value of diabetes was calculated by Fisher exact test; The P values of the remaining variables were calculated by chi-square test