Microbial etiology of diabetic foot ulcers: Swab versus tissue culture

Sankar, Nathan; Moinuddin, Shaik Khaja; Mohan, Sneha

doi:10.33545/surgery.2020.v4.i2c.408

Cited by 1 publication

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“…Predominantly Gram-positive bacteria such as methicillin-resistant Staphylococcus aureus 19 , methicillin sensitive S. aureus , Streptococcus species are typically identified in the acute stage of wounds while gram-negative bacteria such as Escherichia coli , Pseudomonas aeruginosa , Klebsiella spp ., are detected in chronic wounds. 20–22 Our study showed the presence of Enterococcus spp. (17.8%) majorly followed with the presence of Klebsiella spp.…”

Section: Discussionmentioning

confidence: 60%

A Clinical Study to Evaluate Autofluorescence Imaging of Diabetic Foot Ulcers Using a Novel Artificial Intelligence Enabled Noninvasive Device

Viswanathan

Govindan

Selvaraj

et al. 2021

The International Journal of Lower Extremity Wounds

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Diabetic foot ulcers, with worldwide prevalence ranging from 12%-25%, are an important cause of nontraumatic lower limb amputation. Evidence-based assessment of early infection can help the clinician provide the right first line treatment thus helping improve the wound closure rate. Illuminate®, a novel point of care device working on multispectral autofluorescence imaging, helps in the rapid identification and classification of bacteria. This study was aimed to evaluate the diagnostic accuracy of the device in detecting bacterial gram type against standard culture methods. A total of 178 patients from a tertiary care center for diabetes was recruited and 203 tissue samples were obtained from the wound base by the plastic surgeon. The device was handled by the trained investigator to take wound images. The tissue samples were taken from the color-coded infected region as indicated by the device's Artificial Intelligence algorithm and sent for microbial assessment. The results were compared against the Gram type inferred by the device and the device was found to have an accuracy of 89.54%, a positive predictive value of 86.27% for detecting Gram-positive bacteria, 80.77% for Gram-negative bacteria, and 91.67% for no infection. The negative predictive value corresponded to 87.25% for Gram-positive, 92% for Gram-negative, and 96.12% for no infection. The Results exhibited the accuracy of this novel autofluorescence device in identifying and classifying the gram type of bacteria and its potential in significantly aiding clinicians towards early infection assessment and treatment.

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