Clustering and classification of regional peak plantar pressures of diabetic feet

Abstract

High plantar pressures have been associated with foot ulceration in people with diabetes, who can experience loss of protective sensation due to peripheral neuropathy. Therefore, characterization of elevated plantar pressure distributions can provide a means of identifying diabetic patients at potential risk of foot ulceration. Plantar pressure distribution classification can also be used to determine suitable preventive interventions, such as the provision of an appropriately designed insole. In the past, emphasis has primarily been placed on the identification of individual focal areas of elevated pressure. The goal of this study was to utilize k-means clustering analysis to identify typical regional peak plantar pressure distributions in a group of 819 diabetic feet. The number of clusters was varied from 2 to 10 to examine the effect on the differentiation and classification of regional peak plantar pressure distributions. As the number of groups increased, so too did the specificity of their pressure distributions: starting with overall low or overall high peak pressure groups and extending to clusters exhibiting several focal peak pressures in different regions of the foot. However, as the number of clusters increased, the ability to accurately classify a given regional peak plantar pressure distribution decreased. The balance between these opposing constraints can be adjusted when assessing patients with feet that are potentially “at risk” or while prescribing footwear to reduce high regional pressures. This analysis provides an understanding of the variability of the regional peak plantar pressure distributions seen within the diabetic population and serves as a guide for the preemptive assessment and prevention of diabetic foot ulcers.

Introduction

It has been estimated that the number of people worldwide with diabetes will surpass 365 million by 2030 (Wild et al., 2004). Foot ulceration in people with diabetes will continue to be a major public-health concern, considering the 15–25% lifetime risk of developing a foot ulcer (Abbott et al., 2005, Singh et al., 2005). At least 15% of these ulcers will lead to some form of foot amputation (http://www.diabetes.org). The burden of diabetic foot problems arises not only from the medical and economic effects, but also from the impact on the patient's quality of life (Jaksa and Mahoney, 2010).

Plantar pressures, among other clinical measurements, have been used to assess foot conditions in patients with diabetes (Singh et al., 2005). Elevated pressures are believed to increase the risk of ulceration in the diabetic foot, particularly when combined with deformity and peripheral neuropathy (Lavery et al., 2003). Footwear design to relieve elevated plantar pressure is an ongoing research area (Mueller, 1999, Cavanagh and Owings, 2006) although the evidence base for the effectiveness of various offloading techniques remains limited (Cavanagh and Bus, 2010).

Given the wide variety of foot types (Ledoux et al., 2003) and foot biomechanics, a single footwear design cannot successfully decrease peak plantar pressures for all distributions. Ideally, a customized footwear design for each patient would be preferable, but such an approach is not always feasible. A practical solution that accommodates patient-specificity would be to identify groups of patients with similar peak pressure distributions and to establish footwear design guidelines that reduce high regional pressures for each group. New patients could then be classified into one of these groups and the group-specific footwear solution could be prescribed with the expectation that it would reduce corresponding elevated plantar pressures. Waldecker (2012) successfully used logistic regression with plantar pressure, force and pressure–time integral to predict the risk of ulceration although the validity of the pressure–time calculation in this and other studies has been challenged (Waaijman and Bus, 2012, Melai et al., 2011). Acharya et al., 2008, Acharya et al., 2011 and See et al. (2010) used principal component analysis and artificial neural networks to classify patients as normal or diabetic, with and without neuropathy. The k-means clustering algorithm was chosen in the current study for its relative simplicity in comparison to these classification methods and its ability to independently classify data for different numbers of clusters, unlike hierarchical clustering algorithms (Vardaxis et al., 1998). Our goal was to use k-means cluster analysis to objectively and systematically determine characteristic regional peak plantar pressure distributions for patient classification that may be useful for footwear prescription.