Wei-Hsin Lin

Background: Vascular endothelial growth factor inhibitors (VEGFi) are widely used for metastatic cancer management but are known to impair wound-healing. In spinal metastasis surgery, postoperative wound complications remain a clinical challenge, and the perioperative management of systemic antineoplastic therapies remains poorly defined. Current recommendations regarding postoperative VEGFi reinitiation are largely consensus-based and range from 28 to 40 days. The aims of this study were to evaluate the association between perioperative antineoplastic therapies and wound complications and to define the optimal timing of postoperative VEGFi reinitiation. Methods: This retrospective cohort study included 1,164 Taiwanese patients (684 male and 480 female; median age, 60.7 years [interquartile range, 51.5 to 68.8 years]) who underwent spinal metastasis surgery at a single tertiary center between 2010 and 2022. The outcome was postoperative wound complications within 90 days. Multivariable logistic regression was used to identify independent risk factors. The association between the timing of postoperative VEGFi reinitiation and wound complications was assessed using receiver operating characteristic curve analysis and the Fisher exact test. Results: Wound complications occurred in 301 patients (25.9%). Multivariable analysis identified diabetes mellitus (odds ratio [OR] = 4.58; p < 0.001), poor performance status (Eastern Cooperative Oncology Group score ≥2; OR = 3.07; p = 0.002), perioperative use of VEGFi (OR = 4.12; p < 0.001), and perioperative use of antimetabolites (OR = 2.24; p = 0.04) as independent risk factors. Patients reinitiating VEGFi within 40 days had higher complication rates than both nonusers and those reinitiating VEGFi after 40 days, with the former comparison reaching statistical significance. Conclusions: One-quarter of the patients undergoing spinal metastasis surgery experienced postoperative wound complications. Perioperative use of VEGFi was an independent predictor, and our results suggest that there should be a postoperative cessation interval of 40 days to reduce wound-related risk. Clinicians should balance the oncologic benefits of early VEGFi reinitiation against the heightened risk of complications. Level of Evidence: Therapeutic Level III . See Instructions for Authors for a complete description of levels of evidence.

Background: Patients undergoing surgery for bone metastases typically have advanced disease, and postoperative survival varies substantially. Accurate survival estimation is important for surgical decision-making and patient counseling. Several prognostic models have been externally validated in East Asian populations, but these tools were originally developed in Western cohorts and do not incorporate region-specific epidemiology or treatment patterns. Questions/purposes: (1) To develop, internally evaluate, and select a machine learning–based survival prediction model for patients undergoing surgery for nonspinal bone metastases using a multinational East Asian cohort. (2) To compare the performance of the selected model with that of an established Western prognostic tool developed by the Skeletal Oncology Research Group (SORG). (3) To identify which clinical features carried the greatest importance in the new model that we developed. Methods: All patients who underwent surgery for nonspinal bone metastases at three tertiary referral centers in the Republic of Korea, Taiwan, and Japan between January 2009 and December 2022 were included. In total, 1045 patients met the inclusion criteria. The median (range) age at surgery was 64 years (19 to 96), 46% (478 of 1045) of patients were female, and the femur was the most common metastatic site (66% [690]). Data for 3-month, 6-month, 1-year, 3-year, and 5-year overall survival were available for 82% (854), 68% (709), 51% (529), 23% (243), and 15% (160) of patients, respectively. The corresponding survival proportions were 84%, 71%, 56%, 36%, and 31%. Data on routinely available clinical, functional, and laboratory variables were collected, and candidate predictors were predefined based on clinical relevance and data availability across institutions. Missing data were < 4% for all variables in each institution and were handled by multivariate imputation by chained equations. We trained four models using different machine-learning algorithms, and the performance of each model was evaluated using leave-one-site-out validation, in which models were trained on data from two institutions and tested on the remaining institution to ensure separation between training and testing data sets. Model performance was assessed using the Concordance Index (C-index; the ability of the model to correctly rank patients according to their expected survival), Brier score (overall prediction error), time-dependent area under the curve (tdAUC; how well the model distinguishes patients with different survival outcomes at specific time points), calibration slope and intercept (agreement between predicted and observed survival), and decision curve analysis (the potential clinical benefit of using the model to guide treatment decisions). The best-performing model was designated as the East Asian Survival Tool for Bone Metastasis Surgery (EAST-BMS) and was compared with the SORG model. To allow a fair comparison, the performance of the SORG model was evaluated on the same held-out test data sets in each iteration of the leave-one-site-out validation, applying the same performance metrics used to select the final model. Results: Gradient boosting survival analysis demonstrated the most favorable overall performance and was selected as the EAST-BMS. The number of outcome events used for model evaluation was 170 at 3 months and 447 at 12 months. The EAST-BMS achieved tdAUC values of 0.81 (95% confidence interval [CI] 0.78 to 0.85) at 3 months and 0.78 (95% CI 0.70 to 0.84) at 12 months, compared with 0.81 (95% CI 0.74 to 0.86) and 0.76 (95% CI 0.67 to 0.83), respectively, for the SORG model, indicating comparable ability to distinguish patients with different survival outcomes. Brier scores were 0.12 (95% CI 0.09 to 0.15) and 0.23 (95% CI 0.17 to 0.28) for EAST-BMS versus 0.14 (95% CI 0.12 to 0.16) and 0.25 (95% CI 0.15 to 0.34) for SORG, indicating lower prediction error in EAST-BMS. Calibration intercepts were -0.08 (95% CI -0.25 to 0.09) versus -1.06 (95% CI -1.26 to -0.86) at 3 months and -0.35 (95% CI -0.49 to -0.22) versus -1.23 (95% CI -1.37 to -1.08) at 12 months, indicating better agreement between predicted and observed survival in EAST-BMS. Decision curve analysis showed wider threshold probability ranges with positive net clinical benefit for EAST-BMS (0.04 to 0.96 versus 0.05 to 0.66 at 3 months; 0.17 to 0.77 versus 0.08 to 0.67 at 12 months), which means that using the EAST-BMS to guide treatment decisions may provide greater clinical benefit than the SORG model. Albumin, Karnofsky performance status, percentage of lymphocytes, and C-reactive protein level were among the most influential predictors. Conclusion: The EAST-BMS, the first multinational machine-learning survival model for patients from East Asia undergoing surgery for nonspinal bone metastases of which we are aware, demonstrated favorable predictive accuracy and clinical utility. This web-based tool may support personalized prognostic assessment and surgical decision-making. It is freely available as a web-based tool at https://bms.east-mskonco.org. Level of Evidence: Level III, therapeutic study.

Aims: Advances in treatment have extended the life expectancy of patients with metastatic bone disease (MBD). Patients could experience more skeletal-related events (SREs) as a result of this progress. Those who have already experienced a SRE could encounter another local management for a subsequent SRE, which is not part of the treatment for the initial SRE. However, there is a noted gap in research on the rate and characteristics of subsequent SREs requiring further localized treatment, obligating clinicians to extrapolate from experiences with initial SREs when confronting subsequent ones. This study aimed to investigate the proportion of MBD patients developing subsequent SREs requiring local treatment, examine if there are prognostic differences at the initial treatment between those with single versus subsequent SREs, and determine if clinical, oncological, and prognostic features differ between initial and subsequent SRE treatments.Methods: This retrospective study included 3,814 adult patients who received local treatment – surgery and/or radiotherapy – for bone metastasis between 1 January 2010 and 31 December 2019. All included patients had at least one SRE requiring local treatment. A subsequent SRE was defined as a second SRE requiring local treatment. Clinical, oncological, and prognostic features were compared between single SREs and subsequent SREs using Mann-Whitney U test, Fisher’s exact test, and Kaplan–Meier curve.Results: Of the 3,814 patients with SREs, 3,159 (83%) patients had a single SRE and 655 (17%) patients developed a subsequent SRE. Patients who developed subsequent SREs generally had characteristics that favoured longer survival, such as higher BMI, higher albumin levels, fewer comorbidities, or lower neutrophil count. Once the patient got to the point of subsequent SRE, their clinical and oncological characteristics and one-year survival (28%) were not as good as those with only a single SRE (35%; p < 0.001), indicating that clinicians’ experiences when treating the initial SRE are not similar when treating a subsequent SRE.Conclusion: This study found that 17% of patients required treatments for a second, subsequent SRE, and the current clinical guideline did not provide a specific approach to this clinical condition. We observed that referencing the initial treatment, patients in the subsequent SRE group had longer six-week, 90-day, and one-year median survival than patients in the single SRE group. Once patients develop a subsequent SRE, they have a worse one-year survival rate than those who receive treatment for a single SRE. Future research should identify prognostic factors and assess the applicability of existing survival prediction models for better management of subsequent SREs.Cite this article: Bone Joint Res 2024;13(9):497–506.

Background: Survival estimation for patients with symptomatic skeletal metastases ideally should be made before a type of local treatment has already been determined. Currently available survival prediction tools, however, were generated using data from patients treated either operatively or with local radiation alone, raising concerns about whether they would generalize well to all patients presenting for assessment. The Skeletal Oncology Research Group machine-learning algorithm (SORG-MLA), trained with institution-based data of surgically treated patients, and the Metastases location, Elderly, Tumor primary, Sex, Sickness/comorbidity, and Site of radiotherapy model (METSSS), trained with registry-based data of patients treated with radiotherapy alone, are two of the most recently developed survival prediction models, but they have not been tested on patients whose local treatment strategy is not yet decided. Questions/purposes: (1) Which of these two survival prediction models performed better in a mixed cohort made up both of patients who received local treatment with surgery followed by radiotherapy and who had radiation alone for symptomatic bone metastases? (2) Which model performed better among patients whose local treatment consisted of only palliative radiotherapy? (3) Are laboratory values used by SORG-MLA, which are not included in METSSS, independently associated with survival after controlling for predictions made by METSSS? Methods: Between 2010 and 2018, we provided local treatment for 2113 adult patients with skeletal metastases in the extremities at an urban tertiary referral academic medical center using one of two strategies: (1) surgery followed by postoperative radiotherapy or (2) palliative radiotherapy alone. Every patient’s survivorship status was ascertained either by their medical records or the national death registry from the Taiwanese National Health Insurance Administration. After applying a priori designated exclusion criteria, 91% (1920) were analyzed here. Among them, 48% (920) of the patients were female, and the median (IQR) age was 62 years (53 to 70 years). Lung was the most common primary tumor site (41% [782]), and 59% (1128) of patients had other skeletal metastases in addition to the treated lesion(s). In general, the indications for surgery were the presence of a complete pathologic fracture or an impending pathologic fracture, defined as having a Mirels score of ≥ 9, in patients with an American Society of Anesthesiologists (ASA) classification of less than or equal to IV and who were considered fit for surgery. The indications for radiotherapy were relief of pain, local tumor control, prevention of skeletal-related events, and any combination of the above. In all, 84% (1610) of the patients received palliative radiotherapy alone as local treatment for the target lesion(s), and 16% (310) underwent surgery followed by postoperative radiotherapy. Neither METSSS nor SORG-MLA was used at the point of care to aid clinical decision-making during the treatment period. Survival was retrospectively estimated by these two models to test their potential for providing survival probabilities. We first compared SORG to METSSS in the entire population. Then, we repeated the comparison in patients who received local treatment with palliative radiation alone. We assessed model performance by area under the receiver operating characteristic curve (AUROC), calibration analysis, Brier score, and decision curve analysis (DCA). The AUROC measures discrimination, which is the ability to distinguish patients with the event of interest (such as death at a particular time point) from those without. AUROC typically ranges from 0.5 to 1.0, with 0.5 indicating random guessing and 1.0 a perfect prediction, and in general, an AUROC of ≥ 0.7 indicates adequate discrimination for clinical use. Calibration refers to the agreement between the predicted outcomes (in this case, survival probabilities) and the actual outcomes, with a perfect calibration curve having an intercept of 0 and a slope of 1. A positive intercept indicates that the actual survival is generally underestimated by the prediction model, and a negative intercept suggests the opposite (overestimation). When comparing models, an intercept closer to 0 typically indicates better calibration. Calibration can also be summarized as log(O:E), the logarithm scale of the ratio of observed (O) to expected (E) survivors. A log(O:E) > 0 signals an underestimation (the observed survival is greater than the predicted survival); and a log(O:E) < 0 indicates the opposite (the observed survival is lower than the predicted survival). A model with a log(O:E) closer to 0 is generally considered better calibrated. The Brier score is the mean squared difference between the model predictions and the observed outcomes, and it ranges from 0 (best prediction) to 1 (worst prediction). The Brier score captures both discrimination and calibration, and it is considered a measure of overall model performance. In Brier score analysis, the “null model” assigns a predicted probability equal to the prevalence of the outcome and represents a model that adds no new information. A prediction model should achieve a Brier score at least lower than the null-model Brier score to be considered as useful. The DCA was developed as a method to determine whether using a model to inform treatment decisions would do more good than harm. It plots the net benefit of making decisions based on the model’s predictions across all possible risk thresholds (or cost-to-benefit ratios) in relation to the two default strategies of treating all or no patients. The care provider can decide on an acceptable risk threshold for the proposed treatment in an individual and assess the corresponding net benefit to determine whether consulting with the model is superior to adopting the default strategies. Finally, we examined whether...