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Table 1 Baseline characteristics of UNAFIED patients

From: Screening for undiagnosed atrial fibrillation using an electronic health record‒based clinical prediction model: clinical pilot implementation initiative

Variable

UNAFIED

N = 1395

AF/AFL diagnosis

n = 29

Diagnosis + OAC

n = 13

Age, years, mean (SD)

64.6 (9.6)

66.7 (8.6)

65.9 (10.4)

Age, n (%)

   

40–44 years

28 (2.0)

0

0

45–54 years

143 (10.3)

2 (6.9)

1 (7.7)

55–64 years

564 (40.4)

10 (34.5)

5 (38.5)

65–74 years

461 (33.0)

13 (44.8)

5 (38.5)

75–84 years

155 (11.1)

2 (6.9)

0

≥ 85 years

44 (3.2)

2 (6.9)

2 (15.4)

Sex, n (%)

   

Female

635 (45.5)

12 (41.4)

5 (38.5)

Male

753 (54.0)

17 (58.6)

8 (61.5)

Unknown

7 (0.5)

0

0

Race, n (%)

   

American Indian or Alaska Native

5 (0.4)

0

0

Asian

26 (1.9)

0

0

Black or African American

716 (51.3)

10 (34.5)

4 (30.8)

> 1 race

23 (1.7)

0

0

Other Pacific Islander

13 (0.9)

2 (6.9)

1 (7.7)

Unknown

73 (5.2)

2 (6.9)

1 (7.7)

White

539 (38.6)

15 (51.7)

7 (53.8)

Ethnicity, n (%)

   

Hispanic or Latino

156 (11.2)

3 (10.3)

1 (7.7)

Not Hispanic, Latino/a, or Spanish

1219 (87.4)

26 (89.7)

12 (92.3)

Unknown

20 (1.4)

0

0

Insurance type, n (%)a

   

Commercial

133 (9.5)

1 (3.4)

0

Medicaid

307 (22.0)

7 (24.1)

3 (23.1)

Medicare

795 (57.0)

15 (51.7)

6 (46.2)

Other/unknown

227 (16.3)

5 (17.2)

2 (15.4)

Uninsured

119 (8.5)

5 (17.2)

2 (15.4)

Worker’s compensation

9 (0.6)

0

3 (23.1)

  1. AF, atrial fibrillation; AFL, atrial flutter; OAC, oral anticoagulant; UNAFIED, 10-variable predictive model of 2-year AF risk
  2. aPatients may have more than one insurance type. The reported insurance was the most recent value for each patient