The effect of addition of 2DCT scans and 3DCT scans for the classification of tibial plateau fractures: a systematic review.

PURPOSE: In this systematic review, we evaluate the effect of radiographs and 2D and 3D imaging techniques on the interobserver agreement of six commonly used classification systems for tibial plateau fractures. METHODS: In accordance with PRISMA guidelines, PubMed, Cochrane, Embase and Web of Science were searched for studies regarding the effect of 2D and 3D imaging techniques on the interobserver agreement of tibial plateau classification systems. Studies validating new classification systems, not providing own data or only providing information on the interobserver agreement for radiographs were excluded. Studies were scored based on the ROBINS-I risk of bias tool. RESULTS: Our review analysed 14 studies on different classification systems used for tibial plateau fractures in clinical practice, with the Schatzker classification being the most commonly used classification system. The results showed that the addition of 2D CT led to a significant improvement of interobserver agreement for one study. However, other included studies showed varying levels of interobserver agreement, ranging from fair to substantial according to the interpretation by Landis and Koch. The addition of 3D CT resulted in a significant deterioration in one study for the Schatzker classification. Similar to the addition of 2D CT, the interobserver agreement for the Schatzker classification with the addition of 3D CT were heterogeneous ranging from fair to almost perfect according to the interpretation by Landis and Koch. CONCLUSIONS: The use of 2D CT can be recommended for classifying tibial plateau fractures with the Schatzker classification, AO/OTA classification and Hohl classification. The value of 3D CT on the interobserver agreement of commonly used classification systems remains uncertain and unproven. Therefore, we do not recommend the use of 3D CT for the classification of tibial plateau fractures. Overall, the advancement of imaging techniques is not in line with the advancement in interobserver agreement on fracture classification.

Development and internal validation of a clinical prediction model using machine learning algorithms for 90 day and 2 year mortality in femoral neck fracture patients aged 65 years or above.

PURPOSE: Preoperative prediction of mortality in femoral neck fracture patients aged 65 years or above may be valuable in the treatment decision-making. A preoperative clinical prediction model can aid surgeons and patients in the shared decision-making process, and optimize care for elderly femoral neck fracture patients. This study aimed to develop and internally validate a clinical prediction model using machine learning (ML) algorithms for 90 day and 2 year mortality in femoral neck fracture patients aged 65 years or above. METHODS: A retrospective cohort study at two trauma level I centers and three (non-level I) community hospitals was conducted to identify patients undergoing surgical fixation for a femoral neck fracture. Five different ML algorithms were developed and internally validated and assessed by discrimination, calibration, Brier score and decision curve analysis. RESULTS: In total, 2478 patients were included with 90 day and 2 year mortality rates of 9.1% (n = 225) and 23.5% (n = 582) respectively. The models included patient characteristics, comorbidities and laboratory values. The stochastic gradient boosting algorithm had the best performance for 90 day mortality prediction, with good discrimination (c-statistic = 0.74), calibration (intercept = - 0.05, slope = 1.11) and Brier score (0.078). The elastic-net penalized logistic regression algorithm had the best performance for 2 year mortality prediction, with good discrimination (c-statistic = 0.70), calibration (intercept = - 0.03, slope = 0.89) and Brier score (0.16). The models were incorporated into a freely available web-based application, including individual patient explanations for interpretation of the model to understand the reasoning how the model made a certain prediction: https://sorg-apps.shinyapps.io/hipfracturemortality/ CONCLUSIONS: The clinical prediction models show promise in estimating mortality prediction in elderly femoral neck fracture patients. External and prospective validation of the models may improve surgeon ability when faced with the treatment decision-making. LEVEL OF EVIDENCE: Prognostic Level II.