Patient dissatisfaction following primary total knee arthroplasty (TKA) with manual jig-based instruments has been reported to be as high as 30%. Robotic-assisted total knee arthroplasty (RA-TKA) has been increasingly used in an effort to improve patient outcomes, however there is a paucity of literature examining patient satisfaction after RA-TKA. This study aims to identify the incidence of patients who were not satisfied following RA-TKA and to determine factors associated with higher levels of dissatisfaction. This was a retrospective review of 674 patients who underwent primary TKA between October 2016 and September 2020 with a minimum two-year follow-up. A five-point Likert satisfaction score was used to place patients into two groups: Group A were those who were very dissatisfied, dissatisfied, or neutral (Likert score 1 to 3) and Group B were those who were satisfied or very satisfied (Likert score 4 to 5). Patient demographic data, as well as preoperative and postoperative patient-reported outcome measures, were compared between groups.Aims
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Anterior cruciate ligament (ACL) injuries are among the most common and debilitating knee injuries in professional athletes with an incidence in females up to eight-times higher than their male counterparts. ACL injuries can be career-threatening and are associated with increased risk of developing knee osteoarthritis in future life. The increased risk of ACL injury in females has been attributed to various anatomical, developmental, neuromuscular, and hormonal factors. Anatomical and hormonal factors have been identified and investigated as significant contributors including osseous anatomy, ligament laxity, and hamstring muscular recruitment. Postural stability and impact absorption are associated with the stabilizing effort and stress on the ACL during sport activity, increasing the risk of noncontact pivot injury. Female patients have smaller diameter hamstring autografts than males, which may predispose to increased risk of re-rupture following ACL reconstruction and to an increased risk of chondral and meniscal injuries. The addition of an extra-articular tenodesis can reduce the risk of failure; therefore, it should routinely be considered in young elite athletes. Prevention programs target key aspects of training including plyometrics, strengthening, balance, endurance and stability, and neuromuscular training, reducing the risk of ACL injuries in female athletes by up to 90%. Sex disparities in access to training facilities may also play an important role in the risk of ACL injuries between males and females. Similarly, football boots, pitches quality, and football size and weight should be considered and tailored around females’ characteristics. Finally, high levels of personal and sport-related stress have been shown to increase the risk of ACL injury which may be related to alterations in attention and coordination, together with increased muscular tension, and compromise the return to sport after ACL injury. Further investigations are still necessary to better understand and address the risk factors involved in ACL injuries in female athletes. Cite this article:
Machine-learning (ML) prediction models in orthopaedic trauma hold great promise in assisting clinicians in various tasks, such as personalized risk stratification. However, an overview of current applications and critical appraisal to peer-reviewed guidelines is lacking. The objectives of this study are to 1) provide an overview of current ML prediction models in orthopaedic trauma; 2) evaluate the completeness of reporting following the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement; and 3) assess the risk of bias following the Prediction model Risk Of Bias Assessment Tool (PROBAST) tool. A systematic search screening 3,252 studies identified 45 ML-based prediction models in orthopaedic trauma up to January 2023. The TRIPOD statement assessed transparent reporting and the PROBAST tool the risk of bias.Aims
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The aim of this study was to compare the preinjury functional scores with the postinjury preoperative score and postoperative outcome scores following anterior cruciate ligament (ACL) reconstruction surgery (ACLR). We performed a prospective study on patients who underwent primary ACLR by a single surgeon at a single centre between October 2010 and January 2018. Preoperative preinjury scores were collected at time of first assessment after the index injury. Preoperative (pre- and post-injury), one-year, and two-year postoperative functional outcomes were assessed by using the Knee injury and Osteoarthritis Outcome Score (KOOS), Lysholm Knee Score, and Tegner Activity Scale.Aims
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Artificial intelligence and machine-learning analytics have gained extensive popularity in recent years due to their clinically relevant applications. A wide range of proof-of-concept studies have demonstrated the ability of these analyses to personalize risk prediction, detect implant specifics from imaging, and monitor and assess patient movement and recovery. Though these applications are exciting and could potentially influence practice, it is imperative to understand when these analyses are indicated and where the data are derived from, prior to investing resources and confidence into the results and conclusions. In this article, we review the current benefits and potential limitations of machine-learning for the orthopaedic surgeon with a specific emphasis on data quality.
The aim of this study was to screen the entire genome for genetic markers associated with risk for anterior cruciate ligament (ACL) and posterior cruciate ligament (PCL) injury. Genome-wide association (GWA) analyses were performed using data from the Kaiser Permanente Research Board (KPRB) and the UK Biobank. ACL and PCL injury cases were identified based on electronic health records from KPRB and the UK Biobank. GWA analyses from both cohorts were tested for ACL and PCL injury using a logistic regression model adjusting for sex, height, weight, age at enrolment, and race/ethnicity using allele counts for single nucleotide polymorphisms (SNPs). The data from the two GWA studies were combined in a meta-analysis. Candidate genes previously reported to show an association with ACL injury in athletes were also tested for association from the meta-analysis data from the KPRB and the UK Biobank GWA studies.Aims
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