Table 1 Description of the study cohorts

From: Leveraging AI and transfer learning to enhance out-of-hospital cardiac arrest outcome prediction in diverse setting

 

Vietnam (n = 243)

Singapore (n = 15,916)

Age (years), median (Q1-Q3)

54 (42–67)

72 (59–83)

Gender (Male)

186 (76.5)

10,058 (63.2)

Bystander AED (Yes)

10 (4.0)

1364 (8.6)

First Rhythm, n(%)

VF/pVT

38 (15.6)

2273 (14.3)

PEA

0 (0)

4391 (27.6)

Asystole

205 (84.4)

9252 (58.1)

No Flow Time (min), median (Q1–Q3)

—

14 (5–22)

Low Flow Time (min), median (Q1–Q3)

—

42 (39–50)

Prehospital Defibrillation (Yes)

38 (15.6)

3281 (20.6)

Prehospital Epinephrine (Yes)

111 (45.7)

12,197 (76.6)

Prehospital Advanced Airway (Yes)

100 (41.2)

13,966 (87.7)

Rhythm at ED arrival, n(%)

VF/pVT

19 (7.8)

1580 (9.9)

PEA

1 (0.4)

1959 (12.3)

Asystole

147 (60.5)

10,622 (66.7)

ROSC

76 (31.3)

1755 (11.0)

Neurological Outcome (Good)

13 (5.2)

572 (3.6)

  1. Data are presented as count (percentage) of patients unless otherwise indicated.
  2. Variables ‘No Flow Time’ and ‘Low flow time’ are not available in the Vietnam dataset, and their descriptions are reported only for the Singapore dataset.
  3. AED Automated external defibrillator, VF ventricular fibrillation, VT ventricular tachycardia, PEA pulseless electrical activity, ROSC return of spontaneous circulation, ED emergency department. Q1, 25%tile, Q3, 75%tile.
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