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Objective <p>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.</p> Methods <p>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.</p> Results <p>The incidence of SSI was 4.82% (63/1307). The preoperative risk factors for SSI included age (OR 4.442, <i>P</i> = 0.049), duration of surgery (OR 2.872, <i>P</i> = 0.029), multi-segment surgery (OR 3.463, <i>P</i> = 0.021), surgical approach (OR 8.223, <i>P</i> = 0.045), and FCSA (OR 2.152, <i>P</i> = 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.</p> Conclusion <p>The scoring system based on clinical parameters and Dixon MRI indicators is promising for predicting post-lumbar surgery SSI.</p>

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Development of a risk scoring system for surgical site infection after lumbar surgery using Dixon MRI and clinical parameters

  • 期刊论文

  • 发表日期：

    2025年6月5日

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  • † Yijin Wang,
  • † Qiyang Wang,
  • Huayan Zuo,
  • Xiarong Gong,
  • Yong Yang,
  • Guoli Bi,
  • Qiu Bi

摘要

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.