Construction and validation of a nomogram predictive model for assessing the risk of surgical site infections following posterior lumbar fusion surgery - Jin-Zhou Luo; Jie-Zhao Lin; Qi-Fan Chen; Chang-Jian Yang; Chu-Song Zhou (Scientific Reports)
Abstract Surgical site infections (SSIs) are a significant concern following posterior lumbar fusion surgery, leading to increased morbidity and healthcare costs. Accurate prediction of SSI risk is crucial for implementing preventive measures and improving patient outcomes. This study aimed to construct and validate a nomogram predictive model for assessing the risk of SSIs following posterior lumbar fusion surgery. A retrospective study was conducted on 1015 patients who underwent posterior lumbar fusion surgery at our hospital from January 2019 to December 2022. Clinical data, including patient demographics, comorbidities, surgical details, and postoperative outcomes, were collected. SSIs were defined based on the Centers for Disease Control and Prevention (CDC) criteria. Univariate analysis identified significant risk factors, which were then included in a binary logistic regression to develop the nomogram. The model’s performance was evaluated using the concordance index (C-index), calibration curves, and receiver operating characteristic (ROC) curves. The incidence of SSIs was 5.02% (51/1015). The most common pathogens were Staphylococcus aureus and Escherichia coli. Significant risk factors for SSIs included smoking history, diabetes, surgery duration ≥ 3 h, intraoperative blood loss ≥ 300 ml, ASA classification ≥ 3, postoperative closed drainage duration ≥ 5 days, incision length ≥ 10 cm, BMI ≥ 30 kg/m2, and the presence of internal fixation. The nomogram demonstrated a C-index of 0.779 and an AUC of 0.845, indicating high predictive accuracy. The calibration curve closely matched the ideal curve, confirming the model’s reliability. The constructed nomogram predictive model demonstrated high accuracy in predicting SSI risk following posterior lumbar fusion surgery. This model can aid clinicians in identifying high-risk patients and implementing targeted preventive measures to improve surgical outcomes.