CLEAR’s Human Performance Laboratory at Rosalind Franklin University of Medicine and Science

Over the past two generations, elective foot surgery has become ubiquitous. The most common surgeries on extremities have moved from amputations to osteotomies. However, the assessment of success of these procedures has, in some people’s opinion, varied little in those intervening years. We are able to ask the patient how he or she feels, but that can vary day to day. We are able to evaluate radiographs and other imaging tools, but those are of somewhat value in predicting overall function. For more than 15 years, elite centers have used plantar pressure measurement systems. However, these have largely focused on peak plantar pressure. However, this paradigm is not without its problems.

Recently, clinicians at CLEAR have been considering what they see pre and postoperatively and success has not necessarily been associated with reduction in peak pressure—but rather with relative modifications of pressure.  If we used a mountain range example, a single peak coming off of a low-altitude plain, like Kilimanjaro, for instance, might be very different than a peak coming off a series of high peaks, like K2 or Mount Everest in the Himalayas. Kilimanjaro appears relatively higher to the observer on the plain than Everest would to someone standing at 20,000 feet.   In that same context, a single peak of pressure—even one that is relatively mild, may be more clinically important than a very high peak in the company of other sites of high pressure. It is in that way that we believe we can mathematically help to define what some of us have seen clinically for many years.

Attached to this podcast on DiabeticFootOnline.com are a couple of figures showing how members of our Human Performance Laboratory, led by Bijan Najafi, are able to assess each foot as part of a bell-shaped curve of distribution of pressures, if you will. One can then assign a regression factor, which represents the similarity of the actual pressure distribution with the normalized distribution. These range from negative 1 to positive 1. As the value increases, so does the similarity between the actual and normalized pressure distributions. So, you can see a patient with a severe Charcot arthropathy with a rather poor regression factor normalized to a much more acceptable one at one year postoperatively. This is true even though his peak pressures are noticeably higher than the normal patients listed in the diagram.

It is this value that we believe can make a substantial difference as an additional quantitative measure of success in this population. We look forward to further discourse that can better confirm or refute these initial surgical and laboratory observations.