Circulating endothelial precursor cells are associated with a healed diabetic foot ulcer evaluated in a prospective cohort study.

The goal of this multicentre study was to evaluate whether circulating endothelial precursor cells and microparticles can predict diabetic foot ulcer healing by the 16th week of care. We enrolled 207 subjects, and 40.0% (28.4, 41.5) healed by the 16th week of care. Using flow cytometry analysis, several circulating endothelial precursor cells measured at the first week of care were associated with healing after adjustment for wound area and wound duration. For example, CD34+ CD45dim , the univariate odds ratio was 1.19 (95% confidence interval: 0.88, 1.61) and after adjustment for wound area and wound duration, the odds ratio was (1.67 (1.16, 2.42) p = 0.006). A prognostic model using CD34+ CD45dim , wound area, and wound duration had an area under the curve of 0.75 (0.67, 0.82) and CD34+ CD45dim per initial wound area, an area under the curve of 0.72 (0.64, 0.79). Microparticles were not associated with a healed wound. Previous studies have indicated that circulating endothelial precursor cells measured at the first office visit are associated with a healed diabetic foot ulcer. In this multicentred prospective study, we confirm this finding, show the importance of adjusting circulating endothelial precursor cells measurements by wound area, and show circulating endothelial precursor cells per wound area is highly predictive of a healed diabetic foot ulcer by 16th week of care.

Further evidence that wound size and duration are strong prognostic markers of diabetic foot ulcer healing.

Diabetic foot ulcers (DFU) are a critical problem for those with diabetes mellitus. Predicting the healing likelihood of a DFU is important to implementing appropriate care, allocating resources, having access to advanced therapies, having successful clinical trials, calibrating clinical trial results, and providing information to administrative entities on patient and provider outcomes. Prognostic modelling can also be important when attempting to compare results across trials or care centres. In a prospective cohort study, we demonstrate and replicate that simple wound characteristics like wound area and wound duration can be used to predict wound healing by the 16th week of care. The models were based on previous literature and replicated using a machine learning algorithm. The use of wound duration and wound area in a prognostic model continues to be important when comparing study results, centre-based outcomes, as well as designing clinical trials.