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Table 1 Patients’ characteristics

From: Machine learning-based prediction models for accidental hypothermia patients

Variables

Development cohort

Validation cohort

(N = 288)

(N = 244)

Men

144 (50.0%)

126 (51.6%)

Age, years

79 (69–87)

79 (64–87)

 < 60

37 (12.8%)

47 (19.3%)

 60–69

35 (12.2%)

37 (15.2%)

 70–79

75 (26.0%)

48 (19.7%)

 ≥ 80

140 (48.6%)

117 (48.0%)

Activities of daily living

 Disturbance

96 (33.3%)

66 (27.0%)

Comorbidity

 Cardiovascular diseases

126 (43.8%)

111 (45.5%)

 Neurological diseases

53 (18.4%)

40 (16.4%)

 Endocrine diseases

83 (28.8%)

47 (19.3%)

 Psychiatric diseases

55 (19.1%)

63 (25.8%)

 Malignant diseases

12 (4.2%)

4 (1.6%)

 Dementia

57 (19.8%)

51 (20.9%)

 Other

56 (19.4%)

38 (15.6%)

External and minimally invasive rewarming

 Warm intravenous fluid

223 (77.4%)

168 (68.9%)

 Forced warm air

80 (27.8%)

4 (1.6%)

 Warm environment, warm blanket

242 (84.0%)

222 (91.0%)

 Other

23 (8.0%)

15 (6.1%)

Active internal rewarming

 Lavage

29 (10.1%)

15 (6.1%)

 CHDF

4 (1.4%)

17 (7.0%)

 VV-ECMO

0 (0%)

2 (0.8%)

 VA-ECMO

3 (1.0%)

17 (7%)

In-hospital mortality

64 (22.2%)

66 (27.0%)

  1. Categorical variables: n (%), continuous variables: median [interquartile range]
  2. CHDF Continuous hemodiafiltration, VV-ECMO Veno-venous extracorporeal membrane oxygenation, V-A ECMO Veno-arterial membrane oxygenation