Bioburden Predictors of Diabetic Ulcer Complications

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DESCRIPTION (provided by applicant): It is difficult to identify diabetic foot ulcers (DFU) destined for infection-related complications. The objective of this application is to develop strategies to accurately identify DFUs that are likely to proceed to develop infection-related complications. The aims are to 1) determine the prognostic efficacy of a) high microbial load, b) microbial diversity, and c) Staphylococcus aureus and anaerobes based on swab cultures obtained using Levine's technique in predicting infection-related complications among diabetic foot ulcers without signs of clinical infection; and 2) Determine the extent to which combining microbiological dimensions will improve the predictive ability above any single dimension. A prospective research design will be used to observe infection-related complications among DFUs before wound closure. A sample of 150 subjects with DFUs but without signs of clinical infection will be assessed for wound bioburden and followed for infection-related complications every two weeks until 1) wound closure, 2) DFU-related amputation, 3) loss to follow-up or 4) the end of six months of data collection. Wound bioburden has three dimensions: microbial load, microbial diversity, and presence of virulent organisms. Each dimension will be measured by quantitatively culturing swab specimens obtained using Levine's technique. Levine's technique samples fluid from deep tissue layers and has been shown to be a valid measure of wound bioburden when compared to cultures of wound tissue. Repeat measures of wound bioburden will be collected during each follow-up. In addition, the outcomes of wound deterioration, osteomyelitis, or amputation will be assessed during follow-up. ROC analysis will be used to evaluate the prognostic efficacy in terms of area under the ROC curve (AUC). Two outcomes will be considered: (a) the development of wound deterioration and/or osteomyelitis before wound closure, and (b) amputation before wound closure. A composite predictor will be developed using multivariable logistic regression. The findings from this research will improve the discrimination of DFUs at-risk from those not-at-risk for developing complications. In this way, resources can be more cost efficiently directed toward patients for whom more vigorous interventions can prevent undesirable outcomes.