Development and validation of a nomogram for predicting multidrug-resistant bacteria in spontaneous spondylodiscitis

Background: Spontaneous spondylodiscitis is a serious disease, and multidrug-resistant bacterial (MDRB) infection significantly increases the difficulty of treatment and the risk of a poor prognosis. Early identification of the risk of MDRB infection is crucial for optimizing treatment plans, but currently, there is no MDRB prediction model specifically for spontaneous spondylodiscitis. Methods: This study retrospectively included 223 patients with spontaneous spondylodiscitis admitted to the Spine Infection Surgery Department at our hospital between January 2023 and December 2024, and collected clinical, laboratory, and microbiological data. The least absolute shrinkage and selection operator regression analysis and univariate logistic regression analysis were performed to select the most suitable diagnostic index. A nomogram model was built based on the filtered indicators, and the diagnostic performance and clinical usability of the nomogram were assessed using receiver operating characteristic curves, calibration curves, and decision curve analysis (DCA). Results: Eight variables were selected to construct a nomogram for MDRB in spontaneous spondylodiscitis, including interleukin-6 (IL-6), number of infected vertebrae, localized kyphosis, plasma fibrinogen, D-dimer, degree of intervertebral space height loss, fever, and history of intensive care unit admission. The predictive model yielded an area under the curve (AUC) of 0.842 [95% confidence interval (CI), 0.779–0.904], and, in the internal validation, the AUC was 0.840 (95% CI, 0.770–0.890). The predictive model was well-calibrated, and DCA showed that if the threshold probability of MDRB was between 2% and 99%, using the proposed nomogram to predict MDRB would yield a net benefit. Conclusion: This study successfully identified risk factors for MDRB in spontaneous spondylodiscitis and developed a nomogram that provides personalized diagnosis, interventions, and risk assessment, which is helpful for the early identification of high-risk patients and the precise guidance of antibacterial treatment.