Using a Clinical Decision Support System to Improve Anticoagulation in Patients with Nonvalve Atrial Fibrillation in China’s Primary Care Settings: A Feasibility Study
Table 1
Basic demographics of the subjects.
Software group (n = 53)
Control group (n = 31)
Total (n = 84)
2/Z/t
Gender, n (%)$
0.000
0.991
Male
29 (54.7)
17 (54.8)
46 (54.8)
Female
24 (45.3)
14 (45.2)
38 (45.2)
Age (y), mean (SD&)#
79.96 (7.025)
73.58 (7.205)
75.71 (7.237)
2.109
0.038
Hypertension, n (%)$
50 (94.3)
26 (83.9)
76 (90.5)
2.488
0.115
Heart failure, n (%)$
5 (9.4)
4 (14.8)
9 (11.3)
0.519
0.471
Diabetes, n (%)$
17 (32.1)
11 (35.5)
28 (33.3)
0.102
0.749
Stroke/TIA/thromboembolism history, n (%)$
13 (24.5)
3 (11.1)
16 (20.0)
2.013
0.156
Antiplatelets or NSAIDs, n (%)$
20 (37.7)
8 (29.6)
28 (35.0)
0.517
0.472
CHA2DS2-VASc score, n (%)$
1.183
0.277
2
52 (98.1)
29 (93.5)
81 (96.4)
<2
1 (1.9)
2 (6.5)
3 (3.6)
HAS-BLED score, n (%)$
4.363
0.037
3
33 (62.3)
12 (38.7)
45 (53.6)
<3
20 (37.7)
19 (61.3)
39 (46.4)
Clinical visits (times), median (IQR)%
7 (3, 16.5)
6 (4, 11)
6 (3, 13)
−0.023
0.981
Follow-up time (m), median (IQR)%
15 (13, 16.5)
16 (14, 17)
15 (13, 17)
1.209
0.227
Note. $Chi-square test; &standard deviation; #two-tailed unpaired student’s t-test;interquartile range; %Mann–Whitney U test. Values in bold were statistically significant.
