Anterior cruciate ligament (ACL) graft failure from rupture, attenuation, or malposition may cause recurrent subjective instability and objective laxity, and occurs in 3% to 22% of ACL reconstruction (ACLr) procedures. Revision ACLr is often indicated to restore knee stability, improve knee function, and facilitate return to cutting and pivoting activities. Prior to reconstruction, a thorough clinical and diagnostic evaluation is required to identify factors that may have predisposed an individual to recurrent ACL injury, appreciate concurrent intra-articular pathology, and select the optimal graft for revision reconstruction. Single-stage revision can be successful, although a staged approach may be used when optimal tunnel placement is not possible due to the position and/or widening of previous tunnels. Revision ACLr often involves concomitant procedures such as meniscal/chondral treatment, lateral extra-articular augmentation, and/or osteotomy. Although revision ACLr reliably restores knee stability and function, clinical outcomes and reoperation rates are worse than for primary ACLr.

Cite this article: Bone Joint J 2023;105-B(5):474–480.

Aims. To determine the major risk factors for unplanned reoperations (UROs) following corrective surgery for adult spinal deformity (ASD) and their interactions, using machine learning-based prediction algorithms and game theory. Methods. Patients who underwent surgery for ASD, with a minimum of two-year follow-up, were retrospectively reviewed. In total, 210 patients were included and randomly allocated into training (70% of the sample size) and test (the remaining 30%) sets to develop the machine learning algorithm. Risk factors were included in the analysis, along with clinical characteristics and parameters acquired through diagnostic radiology. Results. Overall, 152 patients without and 58 with a history of surgical revision following surgery for ASD were observed; the mean age was 68.9 years (SD 8.7) and 66.9 years (SD 6.6), respectively. On implementing a random forest model, the classification of URO events resulted in a balanced accuracy of 86.8%. Among machine learning-extracted risk factors, URO, proximal junction failure (PJF), and postoperative distance from the posterosuperior corner of C7 and the vertical axis from the centroid of C2 (SVA) were significant upon Kaplan-Meier survival analysis. Conclusion. The major risk factors for URO following surgery for ASD, i.e. postoperative SVA and PJF, and their interactions were identified using a machine learning algorithm and game theory. Clinical benefits will depend on patient risk profiles. Cite this article: Bone Joint Res 2023;12(4):245–255

Aims

The primary aim of this study was to determine the ten-year outcome following surgical treatment for femoroacetabular impingement (FAI). We assessed whether the evolution of practice from open to arthroscopic techniques influenced outcomes and tested whether any patient, radiological, or surgical factors were associated with outcome.

There is increasing popularity in the use of artificial intelligence and machine-learning techniques to provide diagnostic and prognostic models for various aspects of Trauma & Orthopaedic surgery. However, correct interpretation of these models is difficult for those without specific knowledge of computing or health data science methodology. Lack of current reporting standards leads to the potential for significant heterogeneity in the design and quality of published studies. We provide an overview of machine-learning techniques for the lay individual, including key terminology and best practice reporting guidelines.

Cite this article: Bone Joint J 2021;103-B(12):1754–1758.

This review describes the development of arthroscopy of the hip over the past 15 years with reference to patient assessment and selection, the technique, the conditions for which it is likely to prove useful, the contraindications and complications related to the procedure and, finally, to discuss possible developments in the future.