Logistic regression model predicts early surgical site infection after spinal fusion: a retrospective cohort study

Z Ge; X Liu; X Jing; J Wang; Y Guo; H Yang; X Cui

doi:10.1016/j.jhin.2024.04.018

Logistic regression model predicts early surgical site infection after spinal fusion: a retrospective cohort study

J Hosp Infect. 2024 May 15:149:65-76. doi: 10.1016/j.jhin.2024.04.018. Online ahead of print.

Authors

Z Ge¹, X Liu¹, X Jing¹, J Wang², Y Guo¹, H Yang², X Cui³

Affiliations

¹ Department of Spine Surgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China.
² Department of Spine Surgery, Shandong Provincial Hospital, Shandong University, Jinan, China.
³ Department of Spine Surgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China; Department of Spine Surgery, Shandong Provincial Hospital, Shandong University, Jinan, China. Electronic address: cuixingang@sdfmu.edu.cn.

PMID: 38754784
DOI: 10.1016/j.jhin.2024.04.018

Abstract

Objective: This study aimed to develop a diagnostic model for predicting early surgical site infection (SSI) based on postoperative inflammatory markers after spinal fusion surgery.

Methods: In this retrospective study, we analysed the trends of inflammatory markers between SSI and non-SSI groups. The data were randomly divided into training cohort and validation cohort (ratio 7:3). The variables for SSI were analysed using stepwise logistic regression to develop the prediction model. To evaluate the model, we analysed its sensitivity, specificity, positive predictive value, negative predictive value, as well as the area under the curve in the validation cohort. Calibration plots and decision curve analysis were employed to assess the calibration and clinical usefulness of the model.

Findings: We observed significant changes in inflammatory markers on the seventh day after surgery. The prediction model included four variables on the seventh day after surgery: body temperature, C-reactive protein, erythrocyte sedimentation rate and neutrophil counts. After binary processing of these data, the simplified model achieved an area under the curve of 0.86 (95% confidence interval (CI): 0.81-0.92) in the training cohort and 0.9 (95% CI: 0.82-0.98) in the validation cohort. Calibration plots and decision curve analysis demonstrated that the proposed model was effective for the diagnosis of SSI.

Conclusion: We developed and validated a prediction model for diagnosing early infection after spinal fusion.

Keywords: Inflammatory marker; Prediction model; Spinal fusion surgery; Surgical site infection.