We aimed to evaluate surrogate markers commonly used in the literature for diabetic foot osteomyelitis remission after initial treatment for diabetic foot infections (DFIs).
Thirty-five patients with DFIs were prospectively enrolled and followed for 12 months. Osteomyelitis was determined from bone culture and histologic analysis initially and for recurrence. Fisher exact and χ2 tests were used for dichotomous variables and Student t and Mann-Whitney U tests for continuous variables (α = .05).
Twenty-four patients were diagnosed as having osteomyelitis and 11 as having soft-tissue infections. Four patients (16.7%) with osteomyelitis had reinfection based on bone biopsy. The success of osteomyelitis treatment varied based on the surrogate marker used to define remission: osteomyelitis infection (16.7%), failed wound healing (8.3%), reulceration (20.8%), readmission (16.7%), amputation (12.5%). There was no difference in outcomes among patients who were initially diagnosed as having osteomyelitis versus soft-tissue infections. There were no differences in osteomyelitis reinfection (16.7% versus 45.5%; P = .07), wounds that failed to heal (8.3% versus 9.1%; P = .94), reulceration (20.8% versus 27.3%; P = .67), readmission for DFIs at the same site (16.7% versus 36.4%; P = .20), amputation at the same site after discharge (12.5% versus 36.4%; P = .10). Osteomyelitis at the index site based on bone biopsy indicated that failed therapy was 16.7%. Indirect markers demonstrated a failure rate of 8.3% to 20.8%.
Most osteomyelitis markers were similar to markers in soft-tissue infection. Commonly reported surrogate markers were not shown to be specific to identify patients who failed osteomyelitis treatment compared with patients with soft-tissue infections. Given this, these surrogate markers are not reliable for use in practice to identify osteomyelitis treatment failure.
Background: To evaluate complications and risk factors for nonunion in patients with diabetes after ankle fracture.
Methods: We conducted a retrospective study of 139 patients with diabetes and ankle fractures followed for 1 year. We evaluated the incidence of wounds, infections, nonunions, Charcot’s arthropathy, and amputations. We determined Fracture severity (unimalleolar, bimalleolar, trimalleolar), nonunion, and Charcot’s arthropathy from radiographs. Nonunion was defined as a fracture that did not heal within 6 months of fracture. Analysis of variance was used to compare continuous variables, and χ2 tests to compare dichotomous variables, with α = 0.05. Logistic regression was performed with a binary variable representing nonunions as the dependent variable.
Results: Complications were common: nonunion (24.5%), Charcot’s arthropathy (7.9%), wounds (5.2%), wound site infection (17.3%), and leg amputation (2.2%). Patients with nonunions were more likely to be male (55.9% versus 29.5%; P = .005), have sensory neuropathy (76.5% versus 32.4%; P < .001), have end-stage renal disease (17.6% versus 2.9%; P < .001), and use insulin (73.5% versus 40.1%; P < .001), β-blockers (58.8% versus 39.0%; P = .049), and corticosteroids (26.5% versus 9.5%; P = .02). Among patients with nonunion, there was an increased risk of wounds (odds ratio [OR], 3.3; 95% confidence interval [CI], 1.46–7.73), infection (OR, 2.04; 95% CI, 0.72–5.61), amputation (OR, 7.74; 95% CI, 1.01–100.23), and long-term bracing (OR, 9.51; 95% CI, 3.8–23.8). In the logistic regression analysis, four factors were associated with fracture nonunion: dialysis (OR, 7.7; 95% CI, 1.7–35.2), insulin use (OR, 3.3; 95% CI, 1.5–7.4), corticosteroid use (OR, 4.9; 95% CI, 1.4–18.0), and ankle fracture severity (bimalleolar or trimalleolar fracture) (OR, 2.5; 95% CI, 1.1–5.4).
Conclusions: These results demonstrate risk factors for nonunions: dialysis, insulin use, and fracture severity after ankle fracture in patients with diabetes.
Background: To evaluate clinicians' compliance to follow national guidelines for tetanus vaccination prophylaxis in high-risk foot patients. Methods: We retrospectively evaluated 114 consecutive patients between June 2011 and March 2019 who presented with a foot infection resulting from a puncture injury through the emergency department. Eighty-three patients had diabetes mellitus and 31 patients did not have diabetes mellitus. Electronic medical records were used to collect a broad range of study data on patient demographics, previous medical history, previous tetanus immunization history and tetanus status upon presentation to the emergency department (ED), peripheral arterial disease, sensory neuropathy, laboratory values, and clinical / surgical outcomes. Results: 46.5% of the patients who presented to the ED with a puncture wound did not have up-to-date tetanus immunization. Of those patients, 79.2% received a tetanus-containing vaccine booster, 3.8% received intramuscular tetanus immunoglobulins (TIG), 3.8% received both tetanus-containing vaccine booster and TIG, and 20.8% received no form of tetanus prophylaxis. When comparing data between patients with and without diabetes, there were no statistical significant differences in tetanus prophylaxis. Conclusion: Guidelines for tetanus prophylaxis amongst high-risk foot patients in this study center are not followed in all patients. Patients with DM are at high risks of exposure to tetanus, we recommend physicians to take a detailed tetanus immunization history and vaccinate them if tetanus history is unclear.
ObjectiveTo compare pathogens involved in skin and soft tissue infection (SSTI) and pedal osteomyelitis (OM) in patients with and without diabetes with puncture wounds to the foot. MethodsWe evaluated 113 consecutive patients between June 2011 and March 2019 with foot infection (SSTI and OM) from a puncture injury sustained to the foot. Eighty-three patients had diabetes (DM) and 30 did not (NDM). We evaluated the bacterial pathogens in patients with skin and soft tissue infections (SSTI) and pedal osteomyelitis (OM). ResultsPolymicrobial infection were more common in patients with diabetes mellitus (83.1% vs 53.3%, p=.001). The most common pathogen for SSTI and OM in DM was s. aureus (SSTI 50.7%, OM 32.3%), whereas in NDM patients it was Pseudomonas (25%) for SSTI. Anaerobes (9.4%) and fungal (3.1%) infection were uncommon. Pseudomonas aeruginosa was only identified in 5.8% of people with diabetes. ConclusionsThe most common bacterial pathogen in both SSTIs and pedal OM was staphylococcus aureus in patients with DM. Pseudomonas spp., was the most common pathogen in people without diabetes with SSTIs.
Objective: To investigate the predictive value of erythrocyte sedimentation rate (ESR) and C-reactive protein (CRP) in persons with and without diabetes with osteomyelitis (OM).
Methods: We evaluated 455 patients in a retrospective cohort study of patients admitted to the hospital with diabetic foot OM (n = 177), diabetic foot soft-tissue infections (STIs) (n = 176), nondiabetic OM (n = 51), and nondiabetic STIs (n = 51). Infection diagnosis was determined through bone culture, histopathologic examination for OM, and/or imaging (magnetic resonance imaging/single-photon emission computed tomography) for STI. The optimal cutoff values of ESR and CRP in predicting OM were determined by receiver operating characteristic curve analysis. Sensitivity, specificity, positive and negative predictive values, and positive and negative likelihood ratios were determined through contingency tables.
Results: In persons without diabetes with STI or OM, the mean ESR and CRP differences were 10.0 mm/h and 2.6 mg/dL, respectively. In contrast, persons with diabetes had higher levels of each: 24.8 mm/h and 6.8 mg/dL, respectively. As a result, ESR and CRP predicted OM better in patients with diabetes. However, when patients were stratified by neuropathy status, ESR remained predictive of OM in diabetic patients with neuropathy (75% sensitivity, 58% specificity) but not in diabetic patients without neuropathy (50% sensitivity, 44% specificity). Also, CRP remained predictive irrespective of neuropathy status. A similar trend was observed in patients without diabetes.
Conclusions: Previous studies have reported that ESR and CRP are predictive of OM. However, this study suggests that neuropathy influences the predictive value of inflammatory biomarkers. The underlying mechanisms require further study.