The infected diabetic foot: Can serum biomarkers predict osteomyelitis after hospital discharge for diabetic foot infections?

Diabetic Foot Infections

Diabetes is a growing public health concern, with diabetic foot infections (DFI) being one of the leading causes of lower extremity limb amputation in the United States. Accurate diagnosis of DFI requires a combination of clinical, laboratory, and radiologic tests to determine the extent and depth of infection, including the presence of osteomyelitis. Treatment often includes a combination of antibiotics and surgical debridement. Addressing comorbidities such as peripheral vascular disease, glycemic control, and offloading pressure from ulcers and bony prominences is paramount to achieving a successful outcome, and patients should ideally be managed by dedicated interdisciplinary teams.

Update of biomarkers to diagnose diabetic foot osteomyelitis: A meta-analysis and systematic review

The aim of this study was to evaluate the diagnostic characteristics of biomarker for diabetic foot osteomyelitis (DFO). We searched PubMed, Scopus, Embase and Medline for studies who report serological markers and DFO before December 2022. Studies must include at least one of the following diagnostic parameters for biomarkers: area under the curve, sensitivities, specificities, positive predictive value, negative predictive value. Two authors evaluated quality using the Quality Assessment of Diagnostic Accuracy Studies tool. We included 19 papers. In this systematic review, there were 2854 subjects with 2134 (74.8%) of those patients being included in the meta-analysis. The most common biomarkers were erythrocyte sedimentation rate (ESR), C-reactive protein (CRP) and procalcitonin (PCT). A meta-analysis was then performed where data were evaluated with Forrest plots and receiver operating characteristic curves. The pooled sensitivity and specificity were 0.72 and 0.75 for PCT, 0.72 and 0.76 for CRP and 0.70 and 0.77 for ESR. Pooled area under the curves for ESR, CRP and PCT were 0.83, 0.77 and 0.71, respectfully. Average diagnostic odds ratios were 16.1 (range 3.6-55.4), 14.3 (range 2.7-48.7) and 6.7 (range 3.6-10.4) for ESR, CRP and PCT, respectfully. None of the biomarkers we evaluated could be rated as 'outstanding' to diagnose osteomyelitis. Based on the areas under the curve, ESR is an 'excellent' biomarker to detect osteomyelitis, and CRP and PCT are 'acceptable' biomarkers to diagnose osteomyelitis. Diagnostic odds ratios indicate that ESR, CRP and PCT are 'good' or 'very good' tools to identify osteomyelitis.

The infected diabetes-related foot: Comparison of erythrocyte sedementation rate/albumin and C-reactive protein/albumin ratios with erythrocyte sedimentation rate and C-reactive protein to differentiate bone and soft tissue infections

The objective of this study was to evaluate the effectiveness of C-reactive protein (CRP)/albumin, erythrocyte sedimentation rate (ESR)/albumin ratio, ESR, CRP and albumin to differentiate bone and soft tissue infection in persons with diabetes. We retrospectively evaluated 242 individuals admitted to hospital with diabetes-related foot infections (DFI). We categorised DFI cases as either bone (OM) or soft tissue infection based on bone culture and/or histology. We evaluated the diagnostic accuracy of CRP, ESR, albumin, CRP/albumin and ESR/albumin as biomarkers to diagnose OM in persons with diabetes. The median age was 53 years (74% male). There were 224 diabetes-related patients of which 125 had been diagnosed with osteomyelitis. The ESR/albumin and CRP/albumin ratios cut-points were >17.84 and >1.83, respectively. ESR/albumin and CRP/albumin ratios had similar diagnostic parameters: AUC (0.71, 0.71), sensitivity (70.0%, 57.0%), specificity (62.0%, 75.0%), positive predictive value (67.0%, 71.0%) and negative predictive value (66.0% and 71.0%). In contrast diagnostic efficiency of CRP and ESR were AUC 0.71 and 0.71, sensitivity (45.6%, 71.2%), specificity (85.5%, 60.7%), positive predictive value (70.0%, 65.9%) and negative predictive value (59.5%, 66.4%), respectively. When comparing area under the curves, the results showed that ESR/albumin was not significantly different to ESR alone (Delong test pvs ESR  >0.1). Similarly, CRP/albumin was not significantly different to CRP alone (Delong test pvs CRP  >0.1). In conclusion, ESR/albumin and CRP/albumin ratios provided comparable results as using ESR and CRP alone.

Classification Model for Diabetic Foot, Necrotizing Fasciitis, and Osteomyelitis

Simple Summary

Necrotizing fasciitis (NF) and osteomyelitis (OM) are severe complications in patients with diabetic foot ulcers (DFUs). Although NF and OM often cause results including limb amputation and death, definite diagnoses of these are challenging. To aid the prompt and proper diagnosis of NF and OM in patients with DFU, we developed and evaluated a novel prediction model based on machine learning technology. In summary, our prediction model appropriately discriminated the NF and OM from diabetic foot. Moreover, this prediction model has advantages in that it is based on the demographic data and routine laboratory results, which requires no additional examinations which are complicated or expensive.

Abstract

Diabetic foot ulcers (DFUs) and their life-threatening complications, such as necrotizing fasciitis (NF) and osteomyelitis (OM), increase the healthcare cost, morbidity and mortality in patients with diabetes mellitus. While the early recognition of these complications could improve the clinical outcome of diabetic patients, it is not straightforward to achieve in the usual clinical settings. In this study, we proposed a classification model for diabetic foot, NF and OM. To select features for the classification model, multidisciplinary teams were organized and data were collected based on a literature search and automatic platform. A dataset of 1581 patients (728 diabetic foot, 76 NF, and 777 OM) was divided into training and validation datasets at a ratio of 7:3 to be analyzed. The final prediction models based on training dataset exhibited areas under the receiver operating curve (AUC) of the 0.80 and 0.73 for NF model and OM model, respectively, in validation sets. In conclusion, our classification models for NF and OM showed remarkable discriminatory power and easy applicability in patients with DFU.

Diabetic Wound-Healing Science

Diabetes mellitus is an increasingly prevalent chronic metabolic disease characterized by prolonged hyperglycemia that leads to long-term health consequences. It is estimated that impaired healing of diabetic wounds affects approximately 25% of all patients with diabetes mellitus, often resulting in lower limb amputation, with subsequent high economic and psychosocial costs. The hyperglycemic environment promotes the formation of biofilms and makes diabetic wounds difficult to treat. In this review, we present updates regarding recent advances in our understanding of the pathophysiology of diabetic wounds focusing on impaired angiogenesis, neuropathy, sub-optimal chronic inflammatory response, barrier disruption, and subsequent polymicrobial infection, followed by current and future treatment strategies designed to tackle the various pathologies associated with diabetic wounds. Given the alarming increase in the prevalence of diabetes, and subsequently diabetic wounds, it is imperative that future treatment strategies target multiple causes of impaired healing in diabetic wounds.