Application of machine learning models in predicting the risk of thromboembolic events in patients with nonvariceal gastrointestinal bleeding

Table 1 Characteristics of patients in the thromboembolism group and non-thromboembolism group, mean ± SD

Variable	Thromboembolism group (n = 294)	Non-thromboembolism group (n = 854)	P value
Age (years)	73.10 ± 11.55	64.85 ± 16.97	< 0.001
Gender (male) (%)	64.07	69.56	0.088
Heart rate (beats/minute)	83.21 ± 15.18	86.92 ± 18.87	0.002
Hemoglobin (g/L)	80.15 ± 27.47	90.01 ± 30.61	< 0.01
Albumin (g/L)	3306 ± 6.62	34.91 ± 9.87	0.003
Intensive care unit admission (%)	22.37	10.77	< 0.001
History of anticoagulant drug use (%)	58.64	25.41	< 0.001
D-dimer level (μg/L)	4177.46 ± 8129.90	2114.73 ± 6339.46	< 0.001
Length of hospital stay (days)	12.90 ± 9.52	8.57 ± 6.88	< 0.001
Use of hemostatic drugs (%)	10.17	11.82	0.04
Thromboembolism history (%)	35.25	10.42	< 0.001
Alanine aminotransferase (U/L)	23.19 ± 50.04	23.55 ± 64.62	0.931
Creatinine (μmoI/L)	121.59 ± 123.56	115.05 ± 144.02	0.486
International normalized ratio	5.57 ± 6.62	1.17 ± 0.64	0.044
Prothrombin time (seconds)	14.03 ± 9.73	13.56 ± 6.97	0.376
History of nonsteroidal anti-inflammatory drug use (%)	15.93	17.68	0.493
Shock (%)	10.17	10.77	0.772
Red cell distribution width (%)	15.56 ± 2.99	15.09 ± 5.04	0.13
Education (%)1	9.52	15.69	0.09

¹Proportion of individuals with education level of high school or above.