Background: Identification of patients with high-risk of experiencing inability to walk after surgery is important for surgeons to make therapeutic strategies for patients with metastatic spinal disease. However, there is a lack of clinical tool to assess postoperative ambulatory status for those patients. The emergence of artificial intelligence (AI) brings a promising opportunity to develop accurate prediction models. Methods: This study collected 455 patients with metastatic spinal disease who underwent posterior decompressive surgery at three tertiary medical institutions. Of these, 220 patients were collected from one medical institution to form the model derivation cohort, while 89 and 146 patients were collected from two other medical institutions to form the external validation cohorts 1 and 2, respectively. Patients in the model derivation cohort were used to develop and internally validate models. To establish the interactive AI platform, machine learning techniques were used to develop prediction models, including logistic regression (LR), decision tree (DT), random forest (RF), extreme gradient boosting machine (eXGBM), support vector machine (SVM), and neural network (NN). Furthermore, to enhance the resilience of the study’s model, an ensemble machine learning approach was employed using a soft-voting method by combining the results of the above six algorithms. A scoring system incorporating 10 evaluation metrics was used to comprehensively assess the prediction performance of the developed models. The scoring system had a total score of 0 to 60, with higher scores denoting better prediction performance. An interactive AI platform was further deployed via Streamlit. The prediction performance was compared between medical experts and the AI platform in assessing the risk of experiencing postoperative inability to walk among patients with metastatic spinal disease. Results: Among all developed models, the ensemble model outperformed the six other models with the highest score of 57, followed by the eXGBM model (54), SVM model (50), and NN model (50). The ensemble model had the best performance in accuracy and calibration slope, and the second-best performance in precise, recall, specificity, area under the curve (AUC), Brier score, and log loss. The scores of the LR model, RF model, and DT model were 39, 46, and 26, respectively. External validation demonstrated that the ensemble model had an AUC value of 0.873 (95% CI: 0.809–0.936) in the external validation cohort 1 and 0.924 (95% CI: 0.890–0.959) in the external validation cohort 2. In the new ensemble machine learning model excluding the feature of the number of comorbidities, the AUC value was still as high as 0.916 (95% CI: 0.863–0.969). In addition, the AUC values of the new model were 0.880 (95% CI: 0.819–0.940) in the external validation cohort 1 and 0.922 (95% CI: 0.887–0.958) in the external validation cohort 2, indicating favorable generalization of the model. The interactive AI platform was further deployed online based on the final machine learning model, and it was available at https://postoperativeambulatory-izpdr6gsxxwhitr8fubutd.streamlit.app/. By using the AI platform, researchers were able to obtain the individual predicted risk of postoperative inability to walk, gain insights into the key factors influencing the outcome, and find the stratified therapeutic recommendations. The AUC value obtained from the AI platform was significantly higher than the average AUC value achieved by the medical experts (P<0.001), denoting that the AI platform obviously outperformed the individual medical experts. Conclusions: The study successfully develops and validates an interactive AI platform for evaluating the risk of postoperative loss of ambulatory ability in patients with metastatic spinal disease. This AI platform has the potential to serve as a valuable model for guiding healthcare professionals in implementing surgical plans and ultimately enhancing patient outcomes.

Background: 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. Methods: 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. Results: 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). Conclusions: 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.

Background: This study aims to evaluate the perioperative safety and efficacy of minimally-invasive tubular surgery for spinal metastasis with different blood supply.Methods: 72 patients with spinal metastasis between January 2011 to June 2020 were retrospectively reviewed. 14 patients underwent minimally-invasive tubular surgery (Mini-invasive group), and 58 patients underwent conventional surgery (Conventional group). T-test and Mann–Whitney U test was used to evaluate the difference in demographic and perioperative data between the two groups.Results: Baseline characteristics did not differ significantly between the two groups except for the Tokuhashi score (p=0.036). Overall, conventional group had significantly more blood loss (P=0.001), blood transfused(P=0.027), drainage(P<0.001), and longer time of drainage tube(P<0.001), postoperative hospitalization(P=0.002) compared with the mini-invasive group. Sub-analysis showed that for patients with hypo-vascular tumor, trans-channel decompression surgery had all advantages mentioned above. For patients with hyper-vascular tumor, trans-channel decompression surgery only had advantages on the drainage related events. Patients with hyper-vascular tumor had significantly more blood loss compared with patients with hypo-vascular tumor among mini-invasive group.Conclusion: In selected cases with spinal metastasis, minimally-invasive tubular surgery is safe and effective for patients with spinal metastasis. Patients with hypo-vascular tumor were more suitable for this technique with less blood loss, less blood transfused, less drainage and shorter postoperative hospitalization.