Diagnosis of infection in the foot of patients with diabetes: A systematic review
Bibliographic record
Abstract
BACKGROUND: Securing an early accurate diagnosis of diabetic foot infections and assessment of their severity are of paramount importance since these infections can cause great morbidity and potential mortality and present formidable challenges in surgical and antimicrobial treatment. METHODS: In June 2022, we searched the literature using PubMed and EMBASE for published studies on the diagnosis of diabetic foot infection (DFI). On the basis of pre-determined criteria, we reviewed prospective controlled, as well as non-controlled, studies in English. We then developed evidence statements based on the included papers. RESULTS: We selected a total of 64 papers that met our inclusion criteria. The certainty of the majority of the evidence statements was low because of the weak methodology of nearly all of the studies. The available data suggest that diagnosing diabetic foot infections on the basis of clinical signs and symptoms and classified according to the International Working Group of the Diabetic Foot/Infectious Diseases Society of America scheme correlates with the patient's likelihood of the need for hospitalisation, lower extremity amputation, and risk of death. Elevated levels of selected serum inflammatory markers such as erythrocyte sedimentation rate (ESR), C-reactive protein and procalcitonin are supportive, but not diagnostic, of soft tissue infection. Culturing tissue samples of soft tissues or bone, when care is taken to avoid contamination, provides more accurate microbiological information than culturing superficial (swab) samples. Although non-culture techniques, especially next-generation sequencing, are likely to identify more bacteria from tissue samples including bone than standard cultures, no studies have established a significant impact on the management of patients with DFIs. In patients with suspected diabetic foot osteomyelitis, the combination of a positive probe-to-bone test and elevated ESR supports this diagnosis. Plain X-ray remains the first-line imaging examination when there is suspicion of diabetic foot osteomyelitis (DFO), but advanced imaging methods including magnetic resonance imaging (MRI) and nuclear imaging when MRI is not feasible help in cases when either the diagnosis or the localisation of infection is uncertain. Intra-operative or non-per-wound percutaneous biopsy is the best method to accurately identify bone pathogens in case of a suspicion of a DFO. Bedside percutaneous biopsies are effective and safe and are an option to obtain bone culture data when conventional (i.e. surgical or radiological) procedures are not feasible. CONCLUSIONS: The results of this systematic review of the diagnosis of diabetic foot infections provide some guidance for clinicians, but there is still a need for more prospective controlled studies of high quality.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".