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  4. Vol 7, Issue 3, 2026

Development and validation of a machine learning-based prediction model for surgical site infection after spinal metastasis surgery

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DOI: 10.23977/medsc.2026.070308 | Downloads: 1 | Views: 26

Author(s)

Qiang Ren 1, Qichang Li 1

Affiliation(s)

1 Department of Bone and Soft Tissue Oncology, Chongqing University Cancer Hospital, Chongqing, 400030, China

Corresponding Author

Qichang Li

ABSTRACT

Surgical site infection (SSI) is a severe postoperative complication in patients with spinal metastases. This study retrospectively enrolled 460 patients with spinal metastases who underwent surgery at a single center. Stepwise variable screening using univariate analysis, LASSO regression, and multivariate Logistic regression identified seven independent risk factors for SSI: operative time, age, ECOG score, diabetes mellitus, open surgery, preoperative chemotherapy, and hypoproteinemia. Six machine learning models were constructed, and 10-fold cross-validation showed that the Logistic regression model achieved the best performance (AUC = 0.906), with an AUC of 0.866 in the test set. This study developed and validated a risk prediction model for SSI following spinal metastasis surgery with good discrimination and clinical interpretability, providing a reference for individualized perioperative infection risk assessment.

KEYWORDS

Spinal Metastases; Surgical Site Infection; Prediction Model; Machine Learning

CITE THIS PAPER

Qiang Ren, Qichang Li. Development and validation of a machine learning-based prediction model for surgical site infection after spinal metastasis surgery. MEDS Clinical Medicine (2026). Vol. 7, No. 3, 53-60. DOI: http://dx.doi.org/10.23977/medsc.2026.070308.

REFERENCES

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