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  • Meeting abstract
  • Open Access

Biomechanical evaluation of diabetic foot through hierarchical cluster analysis

  • 1Email author,
  • 1,
  • 2,
  • 1,
  • 2 and
  • 1
Journal of Foot and Ankle Research20147 (Suppl 1) :A72

https://doi.org/10.1186/1757-1146-7-S1-A72

  • Published:

Keywords

  • Hierarchical Cluster
  • Peripheral Neuropathy
  • Motion Capture
  • Stance Phase
  • Gait Analysis

Introduction

Type 2 diabetes is predicted to become the 7th leading cause of death in the world by the year 2030 [1]. Diabetic foot is the most common long-term diabetic complication, and it is a major risk factor for plantar ulceration (PU), it is determined by peripheral neuropathy (PN), vascular disease, increased foot pressures, foot trauma, deformity and callus [1].

The aim of this study is to develop a methodology for automatic detection of patients at risk for PU based on 3 dimensional (3D) multisegment foot biomechanics through cluster analysis.

Methods

For this purpose 44 subjects, 20 with (PN) and 24 without PN (noPN) were enrolled. Simultaneous kinematic, kinetic and plantar pressure (PP) data were acquired during gait with a BTS motion capture system (6 cameras, 60-120 Hz) synchronized with 2 Bertec force plate (FP4060-10) and 2 Winpod pressure plate as in [2]. After gait analysis 5 years clinical follow up was performed on each subject including: neuropathy diagnosis following ADA recommendation as in [2, 3], electroneurophysiological study; Index of Winsor, cardiovascular autonomic tests, HbA1c values, micro-macroalbuminuria values, a carotid artery Doppler ultrasound examination.

A hierarchical cluster (HC) technique was adopted [3] using TimeClust1.1. In the present work kinematics, kinetics and PP data estimated as in [2] were used as input. Peak value and its position in term of stance phase of gait’s percentage was extract from each variable. HC was performed either using each type of variable and putting them all together as input or by using each type of variable separately (3D kinematics, kinetics, PP). In order to explore how the subjects were distributed in the proposed cluster, descriptive statistics was used. Statistical differences of both biomechanics and clinical variables between the obtained clusters were investigated using Student T-test and Pearson correlation (MatlabR2011b). After 5 years follow up 3 subjects ulcerated.

Results

Results of HC analysis (see Table 1 and Figure 1) performed either using only 3D subsegments kinematics or kinetics defined two groups, one including PU subjects and one not. The cluster containing PU subjects was characterized by larger number of diabetes complications and higher values of biomechanics variables.
Table 1

Clinical data of subjects for each cluster. In the upper part of table are collected data of cluster using Ground Reaction Force (GRF) input; in the lower part are collected data of luster using kinematics input.

GRF

CL 1

CL 2

p Value

Subjects per Cluster

11

32

 
 

Mean and St. Dev

 

Year of Disease

16,73

10,80

20,68

12,61

 

HbA1c

7,97

1,26

7,96

1,14

 
 

Presence of Complications

 

Vasculopathy

3 (27.27%)

5 (15.625%)

 

microalbuminuria

2 (18.18%)

4 (12.5%)

 

Neuropathy

3 (27.27%)

17 (53.125%)

 

Autonomic Neuropathy

2 (18.18%)

7 (21.875%)

 

Finger Deformity

1 (9.09%)

13 (40.625%)

 

Callosity

2 (18.18%)

18 (56.25%)

0,02926

Ulcer

0

3(9.37%)

 

KINEMATICS

CL 1

CL 2

p Value

Subjects per Cluster

18

26

 
 

Mean and St. Dev

 

Year of Disease

23,67

11,82

16,44

11,56

 

HbA1c

7,95

1,27

7,97

1,11

 
 

Presence of Complications

 

Vasculopathy

3 (16.67%)

5 (19.23%)

 

microalbuminuria

3 (16.67%)

3 (11.54%)

 

Neuropathy

6 (33.33%)

8 (30.77%)

 

Autonomic Neuropathy

10 (55.56%)

10 (38.46%)

 

Finger Deformity

12 (66.67%)

9 (34.62%)

0,03662

Callosity

5 (27.78%)

4 (15.39%)

 

Ulcer

0

3(11.5%)

 
Figure 1
Figure 1

Peak of Medio Lateral, Vertical and Anterior Posterior Force of Midfoot in each cluster (in blu Cluster 1, in red Cluster 2).

Conclusions

In conclusion, our work highlighted the presence of warning signs of neuropathy even in diabetic subjects without a clinical diagnosis of PN. Furthermore 2 type of variables were able to correctly identify the 3 subjects who developed PU within the 5 years (e.g. 3D foot kinematics and kinetics).

Authors’ Affiliations

(1)
Department of Information Engineering, University of Padova, Italy
(2)
Department of Clinical Medicine and Metabolic Disease, University Polyclinic, Padova, Italy

References

  1. American Diabetes Association, American Academy of Neurology: Consensus Statement. Report and Recommendations of the San Antonio Conference on Diabetic Neuropathy. Diabetes Care. 1988, 11: 592-597.View ArticleGoogle Scholar
  2. Sawacha Z, Guarneri G, Cristoferi G, Guiotto A, Avogaro A, Cobelli C: Integrated kinematics–kinetics–plantar pressure data analysis: A useful tool for characterizing diabetic foot biomechanics. Gait & Posture. 2012, 36: 20-26. 10.1016/j.gaitpost.2011.12.007.View ArticleGoogle Scholar
  3. Magni P, Ferrazzi F, Sacchi L, Bellazzi R: TimeClust: a clustering tool for gene expression time series. Bioinformatics. 2008, 24 (3): 430-2. 10.1093/bioinformatics/btm605.View ArticlePubMedGoogle Scholar

Copyright

© Sawacha et al; licensee BioMed Central Ltd. 2014

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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