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.