Optimizing Outcomes after Operative Treatment Bicondylar Tibial Plateau Fractures - Time for Innovation?

Bicondylar tibial plateau fractures are technically demanding fractures that have a high complication rate. We sought to review the recent literature with the aim to summarize the development of new classification systems that may enhance the surgeon's understanding of the fracture pattern and injury. We highlight the best methods for infection control and touch on new innovative solutions using 3D printer models and augmented mixed reality to provide potentially personalized solutions for each specific fracture configuration.

Outcomes after ORIF of Bicondylar Schatzker VI (AO type C) Tibial Plateau Fractures in an Elderly Population.

BACKGROUND: The surgical management of bicondylar tibial plateau (BTP) fractures in elderly patients aims to restore knee stability while minimizing soft tissue complications. The purpose of this study was to compare injury characteristics and surgical outcomes after ORIF of BTP fractures (AO/OTA 41-C (Schatzker VI)) in young (< 50 years) versus elderly (> 65 years) patients. METHODS: A retrospective cohort study was conducted using data from two American College of Surgeons (ACS) level I trauma centers. Inclusion criteria were: (1) age 18 years or older, (2) bicondylar tibial plateau fracture (AO/OTA 41-C or Schatzker VI), (3) treatment with ORIF, and (4) minimum of 6 months follow-up. Patients between 50 and 65 years of age were excluded. Data collection was performed by reviewing electronic medical records, operative reports, and radiology reports. RESULTS: We identified 323 patients (61% male) with 327 BTP fractures and a median follow-up of 685 days. There were 230 young patients (71%) < 50 years and 93 elderly patients (29%) >6 5 years at time of presentation. Elderly patients were significantly more likely to have a low energy mechanism of injury (44.6 vs. 16.2%, p < 0.001), and present with diabetes (19.4 vs. 4.4%, p < 0.001) or coronary artery disease (12.9 vs. 1.3%, p < 0.001). Elderly patients were also significantly less likely to undergo staged management with initial knee-spanning external fixation followed by delayed ORIF (19.2 vs. 33.9%, p = 0.008). Elderly patients had a lower arc of motion at final follow-up (105 vs. 113°, p < 0.001) and reduced PROMIS-10 function scores (43.8 vs. 49.8, p = 0.013). No differences were observed in rates of superficial infection, deep infection, reoperation, or EQ-5D scores between age groups. CONCLUSIONS: This is the largest study to compare injury characteristics and outcomes after ORIF of BTP fractures according to age. Elderly patients (age > 65 years) sustained BTP fractures by lower energy mechanisms than their younger counterparts with similar fracture patterns and were often managed with ORIF. The results of this study suggest that ORIF of BTP fractures in elderly patients is associated with similar complication rates and outcomes as in younger patients despite higher comorbidities and poorer bone quality in the elderly population.

Development and comparison of 1-year survival models in patients with primary bone sarcomas: External validation of a Bayesian belief network model and creation and external validation of a new gradient boosting machine model.

BACKGROUND: Bone sarcomas often present late with advanced stage at diagnosis and an according, varying short-term survival. In 2016, Nandra et al. generated a Bayesian belief network model for 1-year survival in patients with bone sarcomas. The purpose of this study is: (1) to externally validate the prior 1-year Bayesian belief network prediction model for survival in patients with bone sarcomas and (2) to develop a gradient boosting machine model using Nandra et al.'s cohort and evaluate whether the gradient boosting machine model outperforms the Bayesian belief network model when externally validated in an independent Danish population cohort. MATERIAL AND METHODS: The training cohort comprised 3493 patients newly diagnosed with bone sarcoma from the institutional prospectively maintained database at the Royal Orthopaedic Hospital, Birmingham, UK. The validation cohort comprised 771 patients with newly diagnosed bone sarcoma included from the Danish Sarcoma Registry during January 1, 2000-June 22, 2016. We performed area under receiver operator characteristic curve analysis, Brier score and decision curve analysis to evaluate the predictive performance of the models. RESULTS: External validation of the Bayesian belief network 1-year prediction model demonstrated an area under receiver operator characteristic curve of 68% (95% confidence interval, 62%-73%). Area under receiver operator characteristic curve of the gradient boosting machine model demonstrated: 75% (95% confidence interval: 70%-80%), overall model performance by the Brier score was 0.09 (95% confidence interval: 0.077-0.11) and decision curve analysis demonstrated a positive net benefit for threshold probabilities above 0.5. External validation of the developed gradient boosting machine model demonstrated an area under receiver operator characteristic curve of 63% (95% confidence interval: 57%-68%), and the Brier score was 0.14 (95% confidence interval: 0.12-0.16). CONCLUSION: External validation of the 1-year Bayesian belief network survival model yielded a poor outcome based on a Danish population cohort validation. We successfully developed a gradient boosting machine 1-year survival model. The gradient boosting machine did not outperform the Bayesian belief network model based on external validation in a Danish population-based cohort.