A Combined Diagnosis and Treatment Algorithm for Spine Infection Management: A Single-Center Experience.

Background and objective Spinal infection (SI) is an infectious disease affecting the vertebral column, spinal cord, and adjacent structures. The infection can occur following interventions or spontaneously. The aim of this study was to highlight the importance of employing a methodological approach for the accurate and rapid diagnosis of SI and to share information on the most effective treatment method, which involves using a diagnostic-treatment algorithm that can help with SI management. Methodology This study included 50 patients diagnosed with SI between 2016 and 2020. The treatment follow-up period was limited to six months, and the study was conducted as a retrospective cohort analysis. The sample consisted of 22 female patients and 28 male patients, and the mean age of the patients was 50.2 years. All patients received diagnosis and treatment according to the algorithm described in this article. Results In the study group, 60% of patients had an infection in the lumbar spine, 4% in the thoracal spine, 12% in the cervical spine, and 8% in the sacral spine. Previously operated patients were diagnosed on the 30.16th day on average. A total of 19 patients (38%) had no history of undergoing surgery. Radiologically, the most common finding was spondylodiscitis/discitis (32%). Osteomyelitis was detected in one (2%) patient. Methicillin-sensitive Staphylococcus aureus (MSSA) was the most commonly isolated organism in culture results and was detected in 13 patients (26%). The culture results of 12 patients (24%) were negative. The number of patients with active SI who were unstable and stabilized at the time of diagnosis was 11 (22%), and stabilization materials were removed in two patients (4%). In the 6th month of control, the patients did not have any complaints, signs of an infection, or unstable vertebral column. Conclusions We conclude that the combined algorithm we recommend for the diagnosis and treatment of patients with SI can prevent negative deviation and is an effective treatment for this condition.