Aims. Frailty has been gathering attention as a factor to predict surgical outcomes. However, the association of frailty with postoperative complications remains controversial in spinal metastases surgery. We therefore designed a prospective study to elucidate risk factors for postoperative complications with a focus on frailty. Methods. We prospectively analyzed 241 patients with spinal metastasis who underwent palliative surgery from June 2015 to December 2021. Postoperative complications were assessed by the Clavien-Dindo classification; scores of ≥ Grade II were defined as complications. Data were collected regarding demographics (age, sex, BMI, and primary cancer) and preoperative clinical factors (new Katagiri score, Frankel grade, performance status, radiotherapy, chemotherapy, spinal instability neoplastic score, modified Frailty Index-11 (mFI), diabetes, and serum albumin levels). Univariate and multivariate analyses were developed to identify risk factors for postoperative complications (p < 0.05). Results. Overall, 57 postoperative complications occurred in 47 of 241 (19.5%) patients. The most common complications were wound infection/dehiscence, urinary tract infection, and pneumonia. Univariate analysis identified preoperative radiotherapy (p = 0.028), mFI (p < 0.001), blood loss ≥ 500 ml (p = 0.016), and preoperative molecular targeted drugs (p = 0.030) as potential risk factors. From the receiver operating characteristic curve, the clinically optimal cut-off value of mFI was 0.27 (sensitivity, 46.8%; specificity, 79.9%). Multivariate analysis identified mFI ≥ 0.27 (odds ratio (OR) 2.94 (95% CI 1.44 to 5.98); p = 0.003) and preoperative radiotherapy (OR 2.11 (95% CI 1.00 to 4.46); p = 0.049) as significant risk factors. In particular, urinary tract infection (p = 0.012) and pneumonia (p = 0.037) were associated with mFI ≥ 0.27. Furthermore, the severity of postoperative complications was positively correlated with mFI (p < 0.001). Conclusion. The mFI is a useful tool to predict the incidence and the severity of postoperative complications in spinal metastases surgery. Cite this article: Bone Joint J 2024;106-B(12):1469–1476

Aims. Adult spinal deformity (ASD) surgery can reduce pain and disability. However, the actual surgical efficacy of ASD in doing so is far from desirable, with frequent complications and limited improvement in quality of life. The accurate prediction of surgical outcome is crucial to the process of clinical decision-making. Consequently, the aim of this study was to develop and validate a model for predicting an ideal surgical outcome (ISO) two years after ASD surgery. Methods. We conducted a retrospective analysis of 458 consecutive patients who had undergone spinal fusion surgery for ASD between January 2016 and June 2022. The outcome of interest was achievement of the ISO, defined as an improvement in patient-reported outcomes exceeding the minimal clinically important difference, with no postoperative complications. Three machine-learning (ML) algorithms – LASSO, RFE, and Boruta – were used to identify key variables from the collected data. The dataset was randomly split into training (60%) and test (40%) sets. Five different ML models were trained, including logistic regression, random forest, XGBoost, LightGBM, and multilayer perceptron. The primary model evaluation metric was area under the receiver operating characteristic curve (AUROC). Results. The analysis included 208 patients (mean age 64.62 years (SD 8.21); 48 male (23.1%), 160 female (76.9%)). Overall, 42.8% of patients (89/208) achieved the ideal surgical outcome. Eight features were identified as key variables affecting prognosis: depression, osteoporosis, frailty, failure of pelvic compensation, relative functional cross-sectional area of the paraspinal muscles, postoperative sacral slope, pelvic tilt match, and sagittal age-adjusted score match. The best prediction model was LightGBM, achieving the following performance metrics: AUROC 0.888 (95% CI 0.810 to 0.966); accuracy 0.843; sensitivity 0.829; specificity 0.854; positive predictive value 0.806; and negative predictive value 0.872. Conclusion. In this prognostic study, we developed a machine-learning model that accurately predicted outcome after surgery for ASD. The model is built on routinely modifiable indicators, thereby facilitating its integration into clinical practice to promote optimized decision-making. Cite this article: Bone Joint J 2025;107-B(3):337–345

Aims

Gram-negative infections are associated with comorbid patients, but outcomes are less well understood. This study reviewed diagnosis, management, and treatment for a cohort treated in a tertiary spinal centre.