System for Prediction of Recurrent DFU
B
50 7
40 6
A
30
20
10
0
0
1
2
3
4
5
6
5
4
3
2
1
0
7
0
5
Uses per Week
C
15
20
25
30
D
50
7
40
Adherence,
10
Weeks after Enrollment
30
20
10
0
0
1
2
3
4
5
6
7
Uses per Week
6
5
4
3
2
1
0
0
5
10
15
20
25
30
Weeks after Enrollment
Figure 3: Participant adherence to daily use of study device using an ITT (A and B) and a perprotocol (C
and D) approach.
RESULTS
T
or recurrent DFU during the study and those who
did not. Interestingly, none of the demographics
able 1 summarizes the demographic
characteristics of the cohort across the seven
or characteristics were found to be a statistically
significant predictor of the development of DFU in
enrolling sites with evaluable participants (N = this cohort, although participant weight, BMI, and
129). Although 132 participants were enrolled, 3 insulin use were found to be correlated with DFU
participants from an eighth site were subsequently occurrence at nearly significant levels. Furthermore,
removed from the study because it was closed early we found all effect sizes for participant demographics
for unevaluable data. A total of 37 participants (28.7%) to be small. The largest of these were weight (Cohen’s
presented with 53 DFU (0.62 DFU/participant/year) d = 0.37; 95% CI 0.02–0.75; P = 0.06), insulin
during the study. dependence (OR 2.2; 95% CI 1.04–5.0; P = 0.07), and
Device Accuracy
BMI (Cohen’s d = 0.32; 95% CI 0.07–0.70; P = 0.09).
In contrast to these results, the maximum
48
Two subcohorts were compared in this high-risk temperature asymmetry of scans in the 2 months
population: those who developed at least one new preceding clinical presentation of a DFU was strongly
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