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 trea tment attributes on the wound healing rate and to propose a system for woun d healing rate prediction. Predicting the wound healing rate from the initi al wound, patient and treatment data collected in a database of 300 chronic wounds is not possible. After considering weekly follow-ups, it was determ ined that the best prognostic factors are weekly follow-ups of the wound he aling process, which alone were found to predict accurately the wound heali ng rate after a minimum follow-up period of four weeks (at least five measu rements of wound area). After combining the follow-ups with wound, patient and treatment attributes, the minimum follow-up period was reduced to two w eeks (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 indep endent test set of chronic wounds with a relative squared error of 0.347, a nd after three weeks, with a relative squared error of 0.181 (using regress ion trees with linear equations in its leaves). Regression trees with a rel ative 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 p rognostic factors. Arranged in order of decreasing prediction capability, p rognostic factors are: wound size, patient's age, elapsed time from wound a ppearance to the beginning of the treatment, width-to-length ratio, locatio n and type of treatment. The data collected support former findings that th e 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 approp riate treatment decisions.