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Pathophysiology/Complications

Feasibility and Efficacy of a Smart Mat Technology to Predict Development of Diabetic Plantar Ulcers

  1. Robert G. Frykberg1⇑,
  2. Ian L. Gordon2,
  3. Alexander M. Reyzelman3,
  4. Shawn M. Cazzell4,
  5. Ryan H. Fitzgerald5,
  6. Gary M. Rothenberg6,
  7. Jonathan D. Bloom7,
  8. Brian J. Petersen7,
  9. David R. Linders7,
  10. Aksone Nouvong8 and
  11. Bijan Najafi9,10
  1. 1Phoenix VA Health Care System, Phoenix, AZ
  2. 2VA Long Beach Healthcare System, Long Beach, CA
  3. 3Center for Clinical Research, Castro Valley, CA
  4. 4Limb Preservation Platform Inc., Fresno, CA
  5. 5Greenville Health System, Greenville, SC
  6. 6University of Michigan, Ann Arbor, MI
  7. 7Podimetrics, Inc., Somerville, MA
  8. 8VA Greater Los Angeles Healthcare System, Los Angeles, CA
  9. 9University of Arizona, Tucson, AZ
  10. 10Baylor College of Medicine, Houston, TX
  1. Corresponding author: Robert G. Frykberg, robert.frykberg{at}va.gov.
Diabetes Care 2017 Jul; 40(7): 973-980. https://doi.org/10.2337/dc16-2294
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    Figure 1

    The study device was an in-home, wireless, thermometric mat designed for remote temperature monitoring of patients at risk for inflammatory foot diseases.

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    Figure 2

    Comparison of thermometric data from a participant who did not develop a new DFU during the study (left) with the data from a participant who did (right). In the thermograms, the plantar aspect of the foot is viewed from below so that the right foot is at image left.

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    Figure 3

    Participant adherence to daily use of study device using an ITT (A and B) and a per-protocol (C and D) approach.

Tables

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  • Table 1

    Cohort demographic characteristics and comparison of participants who developed a new DFU during the study and those who did not

    All participantsNo DFU during studyDFU during study
    Number of DFU53053
    Number of participants1299237
    Age (years)61.8 ± 10.562.2 ± 11.061.0 ± 9.3
    Male86.0% (111/129)87.0% (80/92)83.8% (31/37)
    Height (m)1.78 ± 0.11.77 ± 0.111.79 ± 0.08
    Weight (kg)105.9 ± 23.7103.1 ± 23.9111.7 ± 22.2
    BMI (kg/m2)33.4 ± 6.632.7 ± 6.934.8 ± 5.9
    History of smoking42.6% (52/122)44.2% (38/86)38.9% (14/36)
    History of alcohol use41.9% (39/93)42.6% (29/68)40.0% (10/25)
    Performs regular exercise35.2% (45/128)35.2% (32/91)35.1% (13/37)
    Living conditions
     Alone35.9% (46/128)38.0% (35/92)30.6% (11/36)
     With others64.1% (82/128)62.0% (57/92)69.4% (25/36)
    Ambulatory status
     Active without assistance79.1% (102/129)78.3% (72/92)81.1% (30/37)
     Active with assistance17.8% (23/129)17.4% (16/92)18.9% (7/37)
     Inactive3.1% (4/129)4.3% (4/92)0.0% (0/37)
    Years since diabetes diagnosed17.6 ± 10.816.9 ± 10.919.1 ± 10.7
    Insulin-dependent60.5% (78/129)55.4% (51/92)73.0% (27/37)
    Hemoglobin A1c
     DCCT-derived (%)8.3 ± 2.08.2 ± 2.18.6 ± 1.8
     IFCC-recommended (mmol/mol)67 ± 2266 ± 2370 ± 20
    History of amputation55.7% (59/106)55.3% (42/76)56.7% (17/30)
    History of Charcot arthropathy6.6% (8/122)5.7% (5/87)8.6% (3/35)
    Months since last DFU healed13.9 ± 39.216.1 ± 45.18.2 ± 14.4
    DFU history
     History of hallux DFU34.9% (45/129)31.5% (29/92)43.2% (16/37)
     History of minor toe DFU55.8% (72/129)52.2% (48/92)64.9% (24/37)
     History of metatarsal DFU41.9% (54/129)40.2% (37/92)45.9% (17/37)
     History of midfoot or heel DFU4.7% (6/129)3.3% (3/92)8.1% (3/37)
    Vascular status
     Left ankle-brachial index1.14 ± 0.181.14 ± 0.161.17 ± 0.21
     Right ankle-brachial index1.18 ± 0.281.20 ± 0.311.15 ± 0.19
     Peripheral vascular disease9.9% (12/121)11.5% (10/87)5.9% (2/34)
     History of vascular surgery15.6% (20/128)15.4% (14/91)16.2% (6/37)
    Neurological status
     Detects left 10-g monofilament17.9% (21/117)19.8% (16/81)13.9% (5/36)
     Detects right 10-g monofilament17.2% (20/116)18.8% (15/80)13.9% (5/36)
    Wears therapeutic shoes86.3% (107/124)86.5% (77/89)85.7% (30/35)
    VHA participant45.0% (58/129)46.7% (43/92)40.5% (15/37)
    Study adherence (uses/week)5.5 ± 1.25.4 ± 1.35.6 ± 1.1
    Temperature asymmetry (°C)**3.10 ± 1.572.81 ± 1.423.94 ± 1.68
    • Data are means ± SD or percentage (n/N).

    • DCCT, Diabetes Control and Complications Trial; IFCC, International Federation of Clinical Chemistry; VHA, Veterans Health Administration.

    • ↵**P < 0.01.

  • Table 2

    Summary of DFU prediction for four foot temperature asymmetry thresholds

    Asymmetry threshold
    2.22°C2.75°C3.20°C3.75°C
    Sensitivity (%)97907050
    Specificity (%)43576881
    Alert frequency (per participant/year)3.12.21.71.1
    Alert lead time (days)37 ± 1836 ± 1735 ± 1635 ± 17
    Positive predictive value (%)16.619.720.423.6
    Negative predictive value (%)99.298.095.192.3
    • Data are means ± SD unless otherwise indicated.

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Feasibility and Efficacy of a Smart Mat Technology to Predict Development of Diabetic Plantar Ulcers
Robert G. Frykberg, Ian L. Gordon, Alexander M. Reyzelman, Shawn M. Cazzell, Ryan H. Fitzgerald, Gary M. Rothenberg, Jonathan D. Bloom, Brian J. Petersen, David R. Linders, Aksone Nouvong, Bijan Najafi
Diabetes Care Jul 2017, 40 (7) 973-980; DOI: 10.2337/dc16-2294

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Feasibility and Efficacy of a Smart Mat Technology to Predict Development of Diabetic Plantar Ulcers
Robert G. Frykberg, Ian L. Gordon, Alexander M. Reyzelman, Shawn M. Cazzell, Ryan H. Fitzgerald, Gary M. Rothenberg, Jonathan D. Bloom, Brian J. Petersen, David R. Linders, Aksone Nouvong, Bijan Najafi
Diabetes Care Jul 2017, 40 (7) 973-980; DOI: 10.2337/dc16-2294
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