古矢 丈雄

STUDY DESIGN: Prospective multicenter study. OBJECTIVE: To investigate risk factors for 3-month postoperative mortality in metastatic spinal tumor surgery, focusing on nutritional biomarkers and prognostic scores alongside clinical indicators. SUMMARY OF BACKGROUND DATA: Metastatic spinal tumors affect patient morbidity and mortality. Although prognostic tools exist, they have limitations, particularly in emergency situations requiring rapid assessment. Nutritional biomarkers and prognostic scores may influence outcomes, but their role in predicting early postoperative mortality after spinal tumor surgery, particularly in prospective, multicenter studies, warrants investigation. METHODS: Data from 336 patients undergoing palliative surgery for metastatic spinal tumors were collected from 35 centers. The primary outcome was 3-month postoperative mortality. Univariate and multivariate logistic regression analyses with bootstrapping were performed to identify predictors of early mortality, including demographics, prognostic scores (revised Tokuhashi, Tomita, modified Glasgow Prognostic Score [mGPS], and the New England Spinal Metastasis Score [NESMS]), and nutritional biomarkers. The discriminative ability of these factors was evaluated using the receiver operating characteristic curve analysis. RESULTS: Results: The 3-month postoperative mortality rate was 15.5%, with primary cancer progression accounting for 54% of the deaths. Multivariate analysis revealed that high mGPS (OR=1.989, P=0.008) and low preoperative performance status (PS) (OR=1.412, P=0.034) were significant independent predictors of early mortality, the Tomita score showed a trend towards significance (OR=1.234, P=0.050). The mGPS demonstrated a high discriminative ability, with an area under the curve of 0.716. CONCLUSION: High mGPS and low preoperative PS are significant predictors of 3-month postoperative mortality in patients undergoing surgery for metastatic spinal tumors. Incorporating the mGPS, which reflects nutritional and inflammatory status, into preoperative risk stratification is crucial for optimizing surgical decision-making. LEVEL OF EVIDENCE: 2.

STUDY DESIGN: Retrospective analysis of data collected across multiple centers. OBJECTIVE: To develop machine learning models for predicting neurological outcomes one month postoperatively in patients with metastatic spinal tumors undergoing surgery, and to identify key factors influencing neurological recovery. SUMMARY OF BACKGROUND DATA: The increasing prevalence of spinal metastases has led to a growing need for surgical intervention to address mechanical instability and neurological deficits. Predicting postoperative neurological status, as assessed by the Frankel classification, can provide valuable insights for surgical planning and patient counseling. Traditional prognostic models have shown limitations in capturing the complexity of neurological recovery patterns. METHODS: We analyzed data from 244 patients who underwent spinal surgery for metastatic disease across 38 institutions. The primary outcome was functional ambulation, defined as Frankel grades D or E at one month postoperatively. Four machine learning algorithms (Random Forest, XGBoost, LightGBM, and CatBoost) were used to build predictive models. Feature selection employed the Boruta algorithm and Variance Inflation Factor analysis to reduce multicollinearity. RESULTS: Among the 244 patients, the proportion of ambulatory patients (Frankel grades D or E) increased from 36.8% preoperatively to 63.1% at one month postoperatively. The Random Forest model achieved the highest area under the receiver operating characteristic curve (AUC-ROC) of 0.8516, followed by XGBoost (0.8351), CatBoost (0.8331), and LightGBM (0.8098). SHapley Additive exPlanations analysis identified preoperative Frankel classification, transfer ability, inflammatory markers (C-reactive protein, white blood cell-lymphocyte), and surgical timing as the most important predictors of postoperative outcomes. CONCLUSIONS: Machine learning models showed strong predictive performance in assessing postoperative neurological status for patients with metastatic spinal tumors. Key factors including preoperative neurological function, functional ability, and inflammation markers significantly influenced outcomes. These findings could inform surgical decision-making and help set realistic postoperative expectations while potentially improving patient care through more accurate outcome prediction.

PURPOSE: Cervical spine injuries in the elderly (defined as individuals aged 65 years and older) are increasing, often resulting from falls and minor trauma. Prognosis varies widely, influenced by multiple factors. This study aimed to develop a deep-learning-based predictive model for post-injury outcomes. METHODS: This study analyzed a nationwide dataset from the Japan Association of Spine Surgeons with Ambition, comprising 1512 elderly patients (aged 65 years and older) with cervical spine injuries from 2010 to 2020. Deep learning predictive models were constructed for residence, mobility, and the American Spinal Injury Association Impairment Scale (AIS). The model's performance was compared with that of a traditional statistical analysis. RESULTS: The deep-learning model predicted the residence and AIS outcomes with varying accuracies. The highest accuracy was observed in predicting residence one year post-injury. The model also identified that the AIS score at discharge was significantly predicted by upper extremity trauma, mobility, and elbow extension strength. The deep learning model highlighted factors, such as upper extremity trauma, that were not considered significant in the traditional statistical analysis. CONCLUSION: Our deep learning-based model offers a novel method for predicting outcomes following cervical spine injuries in the elderly population. The model is highly accurate and provides additional insights into potential prognostic factors. Such models can improve patient care and individualize future interventions.