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Development of a risk scoring system for surgical site infection after lumbar surgery using Dixon MRI and clinical
Yijin Wang1, Qiyang Wang2, Huayan Zuo1
1The Affiliated Hospital of Kunming University of Science and Technology, Department of MRI, the First People's Hospital of Yunnan Province, Kunming, China.
Objective:
To devise a scoring model that integrates clinical parameters and Dixon MRI markers to predict the probability of surgical site infections (SSI) occurrence after lumbar surgery.
Methods:
A retrospective analysis was conducted on 1307 patients who underwent lumbar surgery, with 63 SSI patients and 1244 non-SSI patients. Clinical characteristics and the quantitative parameters on Dixon MRI, such as fat fraction (FF), functional cross-sectional area (FCSA), and psoas to lumbar vertebral index (PLVI), were assessed for differences between the two groups. A multivariate logistic regression model was applied to identify independent predictors that could be utilized in developing of a scoring system, and the performance was assessed through the receiver operating characteristic (ROC) curve.
Results:
The incidence of SSI was 4.82% (63/1307). The preoperative risk factors for SSI included age (OR 4.442, P = 0.049), duration of surgery (OR 2.872, P = 0.029), multi-segment surgery (OR 3.463, P = 0.021), surgical approach (OR 8.223, P = 0.045), and FCSA (OR 2.152, P = 0.004). When the overall scores of these five predictors were greater than or equal to 3.5 points, the area under the curve (AUC) was 0.823, with sensitivity, specificity, positive predictive value, and negative predictive value of 56.6%, 91.9%, 26.1%, and 97.7%, respectively.
Conclusion:
The scoring system based on clinical parameters and Dixon MRI indicators is promising for predicting post-lumbar surgery SSI.
Insights
A new scoring model combining clinical factors and Dixon MRI can predict surgical site infections (SSI) after lumbar surgery. This tool helps identify patients at high risk for post-operative complications.
Area of Science:
- Orthopedic Surgery
- Radiology
- Infectious Disease Epidemiology
Background:
- Surgical site infections (SSI) are a significant complication following lumbar surgery.
- Accurate prediction of SSI risk is crucial for patient management and resource allocation.
Purpose of the Study:
- To develop and validate a predictive scoring model for SSI after lumbar surgery.
- To integrate clinical parameters with quantitative Dixon MRI markers for enhanced prediction accuracy.
Main Methods:
- Retrospective analysis of 1307 patients undergoing lumbar surgery.
- Assessment of clinical data and Dixon MRI parameters (fat fraction, functional cross-sectional area, psoas to lumbar vertebral index).
- Multivariate logistic regression and ROC curve analysis to identify predictors and evaluate model performance.
Main Results:
- SSI incidence was 4.82%.
- Independent predictors for SSI included age, surgery duration, multi-segment surgery, surgical approach, and functional cross-sectional area (FCSA).
- The developed scoring system achieved an AUC of 0.823, with high specificity (91.9%) and negative predictive value (97.7%).
Conclusions:
- A scoring system integrating clinical data and Dixon MRI markers effectively predicts SSI risk after lumbar surgery.
- This model shows promise for identifying high-risk patients, enabling targeted preventive strategies.
