Can bone SPECT/CT determine optimal sites for microbiological identification in post-traumatic or chronic osteomyelitis of extremities?

Introduction: Accurate microbiological identification is crucial when managing chronic osteomyelitis (COM) and post-traumatic osteomyelitis (PTO). Although bone single photon emission computed tomography/computed tomography (SPECT/CT) has helped in localizing osteomyelitis lesions, its effectiveness in guiding microbiological sampling remains unclear. This study aimed to determine whether bone SPECT/CT can improve microbiological identification rates in COM or PTO of the extremities.

Patients and methods: From February 2020 to August 2024, 53 patients with suspected COM or PTO in the extremities were retrospectively analyzed. All patients underwent bone SPECT/CT, followed by microbiological sampling during surgery. Tissue samples were taken from the areas of high SPECT/CT uptake or based on intraoperative findings where no uptake was observed. Microorganism identification rates were analyzed, including a sub-group analysis based on antibiotic discontinuation.

Results: Of the 53 patients, 42 had positive bone SPECT/CT scan findings, with pathogen identification in 30 patients (71.4 %). In contrast, pathogen identification occurred in one out of twelve patients (9.1 %) with negative findings (odds ratio 25, p< 0.001). Bone SPECT/CT demonstrated a sensitivity of 96.8 % and an overall accuracy of 75.5 %. When antibiotics had been discontinued for ≥2 weeks, the pathogen identification rate increased to 90 %, compared with 50 % for <2 weeks of discontinuation (odds ratio 10.0, p= 0.006). In a sub-group of 30 patients with adequate antibiotic discontinuation duration, a positive bone SPECT/CT scan yielded a pathogen identification rate of 90.1 % (odds ratio 60.0, p= 0.001).

Conclusion: Bone SPECT/CT effectively identifies optimal sites for microbiological sampling in COM and PTO of the extremities, particularly when antibiotics have been discontinued for ≥2 weeks, enhancing pathogen detection rates.