Clinical signs of infection in diabetic foot ulcers with high microbial load

Abstract

Aims: One proposed method to diagnose diabetic foot ulcers (DFUs) for infection is clinical examination. Twelve different signs of infection have been reported. The purpose of this study was to examine diagnostic validity of each individual clinical sign, a combination of signs recommended by the Infectious Disease Society of America (IDSA), and a composite predictor based on all signs of localized wound infection in identifying DFU infection, among a sample of DFUs.

Methods: A cross-sectional research design was used. Sixty-four individuals with DFUs were recruited from a Department of Veterans Affairs Medical Center and an academic-affiliated hospital. Each DFU was independently assessed by 2 research team members using the clinical signs and symptoms checklist. Tissue specimens were then obtained via wound biopsy and quantitatively processed. Ulcers with more than 106 organisms per gram of tissue were defined as having high microbial load. Individual signs and the IDSA combination were assessed for validity by calculating sensitivity, specificity, and concordance probability. The composite predictor was analyzed using c-index and receiver operating curves.

Results: Twenty-five (39%) of the DFUs had high microbial loads. No individual sign was a significant predictor of high microbial load. The IDSA combination was not a significant predictor either. The c-index of the composite predictor was .645 with a 95% confidence interval of .559-.732.

Conclusions: Individual signs of infection do not perform well nor does the IDSA combination of signs. However, a composite predictor based on all signs provides a moderate level of discrimination, suggesting clinical use. Larger sample sizes and alternate reference standards are recommended.

Figures

Figure 1

Clinical Signs and Symptoms Checklist

Figure 2

Scatterplot of Pairs (Sensitivity, 1 – Specificity) for Each Sign and IDSA Definition. Sensitivity = 1 – specificity along the chance line.

Figure 3

Composite Predictor ROC Curves. The upper line (uncorrected for overfitting) is the empirical ROC curve for the full sample. The middle line (overfitting corrected) shows the theoretical ROC curve, assuming a binormal model, that corresponds to the overfitting-corrected c index. The chance line corresponds to a c index value of .5.