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Simple Summary: This study investigated the impact of the general health status on the survival of patients with high-grade chondrosarcoma, a rare type of bone cancer. This systemic health status is often summarized using the American Society of Anesthesiologists (ASA) score. By analyzing data from 249 patients, the study found that a higher ASA score, older age, and more aggressive tumors were linked to poorer survival rates. This study highlights that a patient's general health, especially for those with severe systemic diseases, should be considered when determining treatment plans, alongside traditional factors like tumor grade and age. These findings can improve decision-making for complex surgeries in patients with high-grade chondrosarcoma. Background: Due to the relatively advanced age and high mortality rate of patients with high-grade chondrosarcoma (CS), it is important to holistically assess patient- and tumor characteristics in multidisciplinary team and shared decision-making with regard to treatment options. While current prognostic models include multiple tumor and treatment characteristics, the only patient characteristics that are commonly included are age and gender. Based on clinical experience, we believe that factors related to patient preoperative systemic health status such as the American Society of Anesthesiologists (ASA) score may be equally important prognostic factors for overall survival (OS). Methods: A retrospective nationwide cohort study was identified from four specialized bone sarcoma centers in The Netherlands. Patients with a primary CS grade II, III, and dedifferentiated CS were eligible. Prognostic factors including age at presentation, gender, ASA score, CVD, tobacco use, BMI, histological tumor grade, tumor size, pathological fracture, presentation after unplanned excision, type of surgery and surgical margin were evaluated. The outcome measure was OS at the time of surgery. The Kaplan–Meier methodology was employed to estimate OS; a log-rank test was used to assess the difference in survival. To study the impact of prognostic factors on OS, a multivariate Cox proportional hazard regression model was estimated. Results: In total, 249 patients were eligible for this study, and 89 were deceased at the end of follow-up. In multivariate analysis, histological grade (HR 2.247, 95% CI 1.334–3.783), ASA score III (HR 2.615, 95% CI 1.145–5.976, vs. ASA I), and age per year (HR: 1.025, 95% CI 1.004–1.045) were negatively associated with OS. No association was found between tobacco use, BMI, gender or cardiovascular disease and OS in this cohort. Pathological fracture and tumor size were only associated with OS in univariate analysis. Conclusions: This multicenter study is the first on sarcomas to include ASA in a prognostic model. Results show that ASA score as a proxy for patients' systemic health status should be included when providing a prognosis for patients with a high-grade primary CS, besides the conventional risk factors such as tumor grade and age. Specifically, severe systemic disease (ASA score III) is a strong negative predictor. Conversely, we found no difference in OS between ASA scores I and II. These findings aid multidisciplinary team and shared decision-making with regard to these complex sarcoma patients that often require life-changing surgeries. Level of Evidence: Prognostic level III. See the instructions for authors for the complete description of levels of evidence. [ABSTRACT FROM AUTHOR]

Purpose: To (1) evaluate surgeon agreement on plating features (position and screw length) in virtual 3D planning software, (2) describe outcomes (fracture reduction, plate position, malpositioning of calcar screws and screw lengths) of plate fixations planned with routine pre-operative assessment (2D- and 3D CT imaging) and those planned with dedicated virtual 3D software of the same proximal humerus fracture. Methods: Fourteen proximal humerus fractures were retrospectively reduced and fixed with virtual planning software by eight attending orthopaedic surgeons and compared to the true surgical fixation with post-operative computed tomography (CT) scans. Reduction differences were quantified using CT micromotion analysis. Results: Intraclass correlation for screw lengths was 0.97 (95% CI: 0.96–0.98) and 0.90 (95% CI: 0.79–0.96) for plate position. Mean difference in total fracture rotation of the head between the virtual and conventional group was 22.0°. Plate position in the virtual planning group was 3.2 mm more proximal. There were no differences in inferomedial quadrant calcar screw positioning and, apart from the superior posterior converging screw, no significant differences in screw lengths. Conclusion: Reproducibility on plate position and screw length with virtual planning software is adequate. Apart from fracture reduction, virtual planning yielded similar plate positions, screw malpositioning rates and lengths compared to routine pre-operative assessment. [ABSTRACT FROM AUTHOR]

Background: Estimating the risk of revision after arthroplasty could inform patient and surgeon decision-making. However, there is a lack of well-performing prediction models assisting in this task, which may be due to current conventional modeling approaches such as traditional survivorship estimators (such as Kaplan-Meier) or competing risk estimators. Recent advances in machine learning survival analysismight improve decision support tools in this setting. Therefore, this study aimed to assess the performance of machine learning compared with that of conventional modeling to predict revision after arthroplasty. Question/purpose: Does machine learning perform better than traditional regression models for estimating the risk of revision for patients undergoing hip or knee arthroplasty?. Methods: Eleven datasets from published studies from the Dutch Arthroplasty Register reporting on factors associated with revision or survival after partial or total knee and hip arthroplasty between 2018 and 2022 were included in our study. The 11 datasets were observational registry studies, with a sample size ranging from 3038 to 218,214 procedures. We developed a set of time-to-event models for each dataset, leading to 11 comparisons. A set of predictors (factors associated with revision surgery) was identified based on the variables that were selected in the included studies. We assessed the predictive performance of two state-of-the-art statistical time-to-event models for 1-, 2-, and 3-year follow-up: a Fine and Gray model (which models the cumulative incidence of revision) and a causespecific Cox model (which models the hazard of revision). These were compared with a machine-learning approach (a random survival forest model, which is a decision tree-based machine-learning algorithm for time-to-event analysis). Performance was assessed according to discriminative ability (time-dependent area under the receiver operating curve), calibration (slope and intercept), and overall prediction error (scaled Brier score). Discrimination, known as the area under the receiver operating characteristic curve, measures the model's ability to distinguish patients who achieved the outcomes from those who did not and ranges from 0.5 to 1.0, with 1.0 indicating the highest discrimination score and 0.50 the lowest. Calibration plots the predicted versus the observed probabilities; a perfect plot has an intercept of 0 and a slope of 1. The Brier score calculates a composite of discrimination and calibration, with 0 indicating perfect prediction and 1 the poorest. A scaled version of the Brier score, 1 - (model Brier score/null model Brier score), can be interpreted as the amount of overall prediction error. Results: Using machine learning survivorship analysis, we found no differences between the competing risks estimator and traditional regression models for patients undergoing arthroplasty in terms of discriminative ability (patients who received a revision compared with those who did not). We found no consistent differences between the validated performance (time-dependent area under the receiver operating characteristic curve) of different modeling approaches because these values ranged between -0.04 and 0.03 across the 11 datasets (the time-dependent area under the receiver operating characteristic curve of the models across 11 datasets ranged between 0.52 to 0.68). In addition, the calibration metrics and scaled Brier scores produced comparable estimates, showing no advantage of machine learning over traditional regression models. Conclusion: Machine learning did not outperform traditional regression models. Clinical Relevance: Neither machine learning modeling nor traditional regression methods were sufficiently accurate in order to offer prognostic information when predicting revision arthroplasty. The benefit of these modeling approaches may be limited in this context. [ABSTRACT FROM AUTHOR]