Prognostic factors in the prediction of chronic wound healing by electrical stimulation

The aim of the study is to determine the effects of wound, patient and treatment attributes on the wound healing rate and to propose a system for wound healing rate prediction. Predicting the wound healing rate from the initial wound, patient and treatment data collected in a database of 300 chronic wounds is not possible. After considering weekly follow-ups, it was determined that the best prognostic factors are weekly follow-ups of the wound healing process, which alone were found to predict accurately the wound healing rate after a minimum follow-up period of four weeks (at least five measurements of wound area). After combining the follow-ups with wound, patient and treatment attributes, the minimum follow-up period was reduced to two weeks (at least three measurements of wound area). After a follow-up period of two weeks, it was possible to predict the wound healing rate of an independent test set of chronic wounds with a relative squared error of 0.347, and after three weeks, with a relative squared error of 0.181 (using regression trees with linear equations in its leaves). Regression trees with a relative squared error close to 0 produce better prediction than with an error closer to 1. Results show that the type of treatment is just one of many prognostic factors. Arranged in order of decreasing prediction capability, prognostic factors are: wound size, patient's age, elapsed time from wound appearance to the beginning of the treatment, width-to-length ratio, location and type of treatment. The data collected support former findings that the biphasic- and direct-current stimulation contributes to faster healing of chronic wounds. The model of wound healing dynamics aids the prediction of chronic wound healing rate, and hence helps with the formulation of appropriate treatment decisions.