Artificial intelligence-based prediction model for surgical site infection in metastatic spinal disease: a...

Treatment of metastatic spinal disease often involves surgical intervention; however, surgical site infections (SSIs) pose a great challenge for spine surgeons. At present, there is an absence of reliable clinical tools for predicting SSI, which can adversely affect treatment decisions and overall patient management. This study aims to construct and validate an application to stratify the patients at high risk of SSI among those with metastatic spinal disease using an artificial intelligence (AI) approach.

A total of 667 patients diagnosed with metastatic spinal disease were enrolled in this study to train and validate models. Patients in the model-derivation cohort ( n = 485) from two tertiary medical institutions were randomly divided into two groups at a ratio of 8:2, with the most patients belonging to the model-training group and the remaining patients classified into the model-validation group. External validation was conducted among patients ( n = 182) from another tertiary medical institution. Logistic Regression and five machine learning algorithms, including support vector machine, gradient boosting machine (GBM), K-nearest neighbor (KNN), neural network (NN), and decision tree, were used to train and optimize models. The predictive performance of the models was assessed through both discrimination and calibration. The model demonstrating the best prediction accuracy was selected as the AI platform for assessing the risk of SSI in patients with metastatic spinal disease. To evaluate the clinical utility of our AI model, we conducted a comparative study involving 100 patients undergoing surgery for metastatic spinal disease at one tertiary medical institution.

The incidence of SSI in spinal metastasis surgeries was 6.4% in the model derivation cohort and 7.7% in the external validation cohort. Among all models, the GBM model had the highest area under the curve (AUC) value (0.986, 95% confidence interval [CI]: 0.972-1.000), followed by the KNN model (0.962, 95% CI: 0.933-0.991), and the NN model (0.944, 95% CI: 0.914-0.974). The GBM model also had the best prediction performance in terms of accuracy, precision, recall, F1 score, Brier score, and log loss. The calibration curve revealed the GBM model had favorable calibration ability, and decision curve analysis showed the GBM model had significant net clinical benefits in various risk thresholds. External validation generated an AUC value of the model of 0.848 (95% CI: 0.806-0.890). Surgery time, tumor type, and number of comorbidities were identified as the most three influential factors for postoperative SSI. The AI application achieved significantly higher accuracy than clinician assessments (AUC: 0.986 vs. 0.572-0.627, P < 0.001). Sensitivity analysis confirmed robustness across subgroups (e.g. diabetes, visceral metastases).

This study develops and validates an AI tool with strong predictive performance in identifying patients at a high risk for SSI. By facilitating personalized treatment based on risk classification, this advancement has the potential to significantly enhance surgical care for patients with metastatic spinal disease. Future research should focus on integrating this predictive tool into clinical practice and exploring its applicability across diverse patient populations.