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Background: The benefits of surgical treatment of a metastasis of the extremities may be offset by drawbacks such as potential postoperative complications. For this group of patients, the primary goal of surgery is to improve quality of life in a palliative setting. A better comprehension of factors associated with complications and the impact of postoperative complications on mortality may prevent negative outcomes and help surgeons in surgical decision-making., Questions/purposes: (1) What is the risk of 30-day postoperative complications after surgical treatment of osseous metastatic disease of the extremities? (2) What predisposing factors are associated with a higher risk of 30-day complications? (3) Are minor and major 30-day complications associated with higher mortality at 1 year?, Methods: Between 1999 and 2016, 1090 patients with osseous metastatic disease of the long bones treated surgically at our institution were retrospectively included in the study. Surgery included intramedullary nailing (58%), endoprosthetic reconstruction (22%), plate-screw fixation (14%), dynamic hip screw fixation (2%), and combined approaches (4%). Surgery was performed if patients were deemed healthy enough to proceed to surgery and wished to undergo surgery. All data were retrieved by manually reviewing patients' records. The overall frequency of complications, which were defined using the Clavien-Dindo classification system, was calculated. We did not include Grade I complications as postoperative complications and complications were divided into minor (Grade II) and major (Grades III-V) complications. A multivariate logistic regression analysis was used to identify factors associated with 30-day postoperative complications. A Cox regression analysis was used to assess the association between postoperative complications and overall survival., Results: Overall, 31% of the patients (333 of 1090) had a postoperative complication within 30 days. The following factors were independently associated with 30-day postoperative complications: rapidly growing primary tumors classified according to the modified Katagiri classification (odds ratio 1.6; 95% confidence interval, 1.1-2.2; p = 0.011), multiple bone metastases (OR 1.6; 95% CI, 1.1-2.3; p = 0.008), pathologic fracture (OR 1.5; 95% CI, 1.1-2.0; p = 0.010), lower-extremity location (OR 2.2; 95% CI, 1.6-3.2; p < 0.001), hypoalbuminemia (OR 1.7; 95% CI, 1.2-2.4; p = 0.002), hyponatremia (OR 1.5; 95% CI, 1.0-2.2; p = 0.044), and elevated white blood cell count (OR 1.6; 95% CI, 1.1-2.4; p = 0.007). Minor and major postoperative complications within 30 days after surgery were both associated with greater 1-year mortality (hazard ratio 1.6; 95% CI, 1.3-1.8; p < 0.001 and HR 3.4; 95% CI, 2.8-4.2, respectively; p < 0.001)., Conclusion: Patients with metastatic disease in the long bones are vulnerable to postoperative adverse events. When selecting patients for surgery, surgeons should carefully assess a patient's cancer status, and several preoperative laboratory values should be part of the standard work-up before surgery. Furthermore, 30-day postoperative complications decrease survival within 1 year after surgery. Therefore, patients at a high risk of having postoperative complications are less likely to profit from surgery and should be considered for nonoperative treatment or be monitored closely after surgery., Level of Evidence: Level III, therapeutic study.

Background: A preoperative estimation of survival is critical for deciding on the operative management of metastatic bone disease of the extremities. Several tools have been developed for this purpose, but there is room for improvement. Machine learning is an increasingly popular and flexible method of prediction model building based on a data set. It raises some skepticism, however, because of the complex structure of these models., Questions/purposes: The purposes of this study were (1) to develop machine learning algorithms for 90-day and 1-year survival in patients who received surgical treatment for a bone metastasis of the extremity, and (2) to use these algorithms to identify those clinical factors (demographic, treatment related, or surgical) that are most closely associated with survival after surgery in these patients., Methods: All 1090 patients who underwent surgical treatment for a long-bone metastasis at two institutions between 1999 and 2017 were included in this retrospective study. The median age of the patients in the cohort was 63 years (interquartile range [IQR] 54 to 72 years), 56% of patients (610 of 1090) were female, and the median BMI was 27 kg/m (IQR 23 to 30 kg/m). The most affected location was the femur (70%), followed by the humerus (22%). The most common primary tumors were breast (24%) and lung (23%). Intramedullary nailing was the most commonly performed type of surgery (58%), followed by endoprosthetic reconstruction (22%), and plate screw fixation (14%). Missing data were imputed using the missForest methods. Features were selected by random forest algorithms, and five different models were developed on the training set (80% of the data): stochastic gradient boosting, random forest, support vector machine, neural network, and penalized logistic regression. These models were chosen as a result of their classification capability in binary datasets. Model performance was assessed on both the training set and the validation set (20% of the data) by discrimination, calibration, and overall performance., Results: We found no differences among the five models for discrimination, with an area under the curve ranging from 0.86 to 0.87. All models were well calibrated, with intercepts ranging from -0.03 to 0.08 and slopes ranging from 1.03 to 1.12. Brier scores ranged from 0.13 to 0.14. The stochastic gradient boosting model was chosen to be deployed as freely available web-based application and explanations on both a global and an individual level were provided. For 90-day survival, the three most important factors associated with poorer survivorship were lower albumin level, higher neutrophil-to-lymphocyte ratio, and rapid growth primary tumor. For 1-year survival, the three most important factors associated with poorer survivorship were lower albumin level, rapid growth primary tumor, and lower hemoglobin level., Conclusions: Although the final models must be externally validated, the algorithms showed good performance on internal validation. The final models have been incorporated into a freely accessible web application that can be found at https://sorg-apps.shinyapps.io/extremitymetssurvival/. Pending external validation, clinicians may use this tool to predict survival for their individual patients to help in shared treatment decision making., Level of Evidence: Level III, therapeutic study.

Background: We developed a machine learning algorithm to predict the survival of patients with chondrosarcoma. The algorithm demonstrated excellent discrimination and calibration on internal validation in a derivation cohort based on data from the Surveillance, Epidemiology, and End Results (SEER) registry. However, the algorithm has not been validated in an independent external dataset., Questions/purposes: Does the Skeletal Oncology Research Group (SORG) algorithm accurately predict 5-year survival in an independent patient population surgically treated for chondrosarcoma?, Methods: The SORG algorithm was developed using the SEER registry, which contains demographic data, tumor characteristics, treatment, and outcome values; and includes approximately 30% of the cancer patients in the United States. The SEER registry was ideal for creating the derivation cohort, and consequently the SORG algorithm, because of the high number of eligible patients and the availability of most (explanatory) variables of interest. Between 1992 to 2013, 326 patients were treated surgically for extracranial chondrosarcoma of the bone at two tertiary care referral centers. Of those, 179 were accounted for at a minimum of 5 years after diagnosis in a clinical note at one of the two institutions, unless they died earlier, and were included in the validation cohort. In all, 147 (45%) did not meet the minimum 5 years of followup at the institution and were not included in the validation of the SORG algorithm. The outcome (survival at 5 years) was checked for all 326 patients in the Social Security death index and were included in the supplemental validation cohort, to also ascertain validity for patients with less than 5 years of institutional followup. Variables used in the SORG algorithm to predict 5-year survival including sex, age, histologic subtype, tumor grade, tumor size, tumor extension, and tumor location were collected manually from medical records. The tumor characteristics were collected from the postoperative musculoskeletal pathology report. Predicted probabilities of 5-year survival were calculated for each patient in the validation cohort using the SORG algorithm, followed by an assessment of performance using the same metrics as used for internal validation, namely: discrimination, calibration, and overall performance. Discrimination was calculated using the concordance statistic (or the area under the Receiver Operating Characteristic (ROC) curve) to determine how well the algorithm discriminates between the outcome, which ranges from 0.5 (no better than a coin-toss) to 1.0 (perfect discrimination). Calibration was assessed using the calibration slope and intercept from a calibration plot to measure the agreement between predicted and observed outcomes. A perfect calibration plot should show a 45° upwards line. Overall performance was determined using the Brier score, ranging from 0 (excellent prediction) to 1 (worst prediction). The Brier score was compared with the null-model Brier score, which showed the performance of a model that ignored all the covariates. A Brier score lower than the null model Brier score indicated greater performance of the algorithm. For the external validation an F1-score was added to measure the overall accuracy of the algorithm, which ranges between 0 (total failure of an algorithm) and 1 (perfect algorithm).The 5-year survival was lower in the validation cohort than it was in the derivation cohort from SEER (61.5% [110 of 179] versus 76% [1131 of 1544] ; p < 0.001). This difference was driven by higher proportion of dedifferentiated chondrosarcoma in the institutional population than in the derivation cohort (27% [49 of 179] versus 9% [131 of 1544]; p < 0.001). Patients in the validation cohort also had larger tumor sizes, higher grades, and nonextremity tumor locations than did those in the derivation cohort. These differences between the study groups emphasize that the external validation is performed not only in a different patient cohort, but also in terms of disease characteristics. Five-year survival was not different for both patient groups between subpopulations of patients with conventional chondrosarcomas and those with dedifferentiated chondrosarcomas., Results: The concordance statistic for the validation cohort was 0.87 (95% CI, 0.80-0.91). Evaluation of the algorithm's calibration in the institutional population resulted in a calibration slope of 0.97 (95% CI, 0.68-1.3) and calibration intercept of -0.58 (95% CI, -0.20 to -0.97). Finally, on overall performance, the algorithm had a Brier score of 0.152 compared with a null-model Brier score of 0.237 for a high level of overall performance. The F1-score was 0.836. For the supplementary validation in the total of 326 patients, the SORG algorithm had a validation of 0.89 (95% CI, 0.85-0.93). The calibration slope was 1.13 (95% CI, 0.87-1.39) and the calibration intercept was -0.26 (95% CI, -0.57 to 0.06). The Brier score was 0.11, with a null-model Brier score of 0.19. The F1-score was 0.901., Conclusions: On external validation, the SORG algorithm retained good discriminative ability and overall performance but overestimated 5-year survival in patients surgically treated for chondrosarcoma. This internet-based tool can help guide patient counseling and shared decision making., Level of Evidence: Level III, prognostic study.

Background: Patient reported outcome data in bone metastatic disease are scarce and it would be useful to have normative data and understand what patients are at risk for poor function and more pain., Objectives: We aimed to assess what factors are independently associated with physical function and pain intensity in patients with bone metastasis., Methods: We included data from 211 patients with bone metastasis who completed a survey (2014-2016) including the PROMIS Physical Function Cancer and PROMIS Pain Intensity questionnaires., Results: Prostate (P < .001) and thyroid carcinoma (P = .007) were associated with better function and having other disabling conditions (P = 0.035) was associated with worse function. Prostate carcinoma (P = .001) and lymphoma (P = .007) were associated with less pain. There was a moderate correlation between pain and function (P < .001). Function was substantially worse as compared to a US reference population of patients with cancer (P < .001), whereas pain was slightly less compared to the US general population average (P < .001)., Conclusions: Patients with bone metastasis have a poor physical function. Physical function and pain intensity depend on tumor histology, but also on potentially modifiable factors such as other disabling conditions., Level of Evidence: Level III, prognostic study., (© 2019 Wiley Periodicals, Inc.)

Background: Several studies have identified prognostic factors for patients with chondrosarcoma, but there are few studies investigating the accuracy of computationally intensive methods such as machine learning. Machine learning is a type of artificial intelligence that enables computers to learn from data. Studies using machine learning are potentially appealing, because of its possibility to explore complex patterns in data and to improve its models over time., Questions/purposes: The purposes of this study were (1) to develop machine-learning algorithms for the prediction of 5-year survival in patients with chondrosarcoma; and (2) to deploy the best algorithm as an accessible web-based app for clinical use., Methods: All patients with a microscopically confirmed diagnosis of conventional or dedifferentiated chondrosarcoma were extracted from the Surveillance, Epidemiology, and End Results (SEER) Registry from 2000 to 2010. SEER covers approximately 30% of the US population and consists of demographic, tumor characteristic, treatment, and outcome data. In total, 1554 patients met the inclusion criteria. Mean age at diagnosis was 52 years (SD 17), ranging from 7 to 102 years; 813 of the 1554 patients were men (55%); and mean tumor size was 8 cm (SD 6), ranging from 0.1 cm to 50 cm. Exact size was missing in 340 of 1544 patients (22%), grade in 88 of 1544 (6%), tumor extension in 41 of 1544 (3%), and race in 16 of 1544 (1%). Data for 1-, 3-, 5-, and 10-year overall survival were available for 1533 (99%), 1512 (98%), 1487 (96%), and 977 (63%) patients, respectively. One-year survival was 92%, 3-year survival was 82%, 5-year survival was 76%, and 10-year survival was 54%. Missing data were imputed using the nonparametric missForest method. Boosted decision tree, support vector machine, Bayes point machine, and neural network models were developed for 5-year survival. These models were chosen as a result of their capability of predicting two outcomes based on prior work on machine-learning models for binary classification. The models were assessed by discrimination, calibration, and overall performance. The c-statistic is a measure of discrimination. It ranges from 0.5 to 1.0 with 1.0 being perfect discrimination and 0.5 that the model is no better than chance at making a prediction. The Brier score measures the squared difference between the predicted probability and the actual outcome. A Brier score of 0 indicates perfect prediction, whereas a Brier score of 1 indicates the poorest prediction. The Brier scores of the models are compared with the null model, which is calculated by assigning each patient a probability equal to the prevalence of the outcome., Results: Four models for 5-year survival were developed with c-statistics ranging from 0.846 to 0.868 and Brier scores ranging from 0.117 to 0.135 with a null model Brier score of 0.182. The Bayes point machine was incorporated into a freely available web-based application. This application can be accessed through https://sorg-apps.shinyapps.io/chondrosarcoma/., Conclusions: Although caution is warranted, because the prediction model has not been validated yet, healthcare providers could use the online prediction tool in daily practice when survival prediction of patients with chondrosarcoma is desired. Future studies should seek to validate the developed prediction model., Level of Evidence: Level III, prognostic study.

Background: Skeletal metastases are a common problem in patients with cancer, and surgical decision making depends on multiple factors including life expectancy. Identification of new prognostic factors can improve survival estimation and guide healthcare providers in surgical decision making. In this study, we aim to determine the prognostic value of neutrophil/lymphocyte ratio (NLR) and platelet/lymphocyte ratio (PLR) in patients with bone metastasis., Methods: One thousand and twelve patients from two tertiary referral centers between 2002 and 2014 met the inclusion criteria. Bivariate and multivariate Cox regression analyses were performed to determine the association of NLR and PLR with survival., Results: At 3 months, 84.0% of the patients with low NLR were alive versus 61.3% of the patients with a high NLR (p < 0.001), and 75.8% of the patients with a low PLR were alive versus 55.6% of the patients with a high PLR (p < 0.001). Both elevated NLR and elevated PLR were independently associated with worse survival (hazard ratio (HR): 1.311; 95% confidence interval (CI): 1.117-1.538; p = 0.001) and (HR: 1.358; 95% CI: 1.152-1.601; p < 0.001), respectively., Conclusion: This study showed both NLR and PLR to be independently associated with survival in patients who were treated for skeletal metastasis.