Achilles tendon re-rupture (ATRR) poses a significant risk of postoperative complication, even after a successful initial surgical repair. This study aimed to identify risk factors associated with Achilles tendon re-rupture following operative fixation. This retrospective cohort study analyzed a total of 43,287 patients from national health claims data spanning 2008 to 2018, focusing on patients who underwent surgical treatment for primary Achilles tendon rupture. Short-term ATRR was defined as cases that required revision surgery occurring between six weeks and one year after the initial surgical repair, while omitting cases with simultaneous infection or skin necrosis. Variables such as age, sex, the presence of Achilles tendinopathy, and comorbidities were systematically collected for the analysis. We employed multivariate stepwise logistic regression to identify potential risk factors associated with short-term ATRR.Aims
Methods
The use of artificial intelligence (AI) is rapidly growing across many domains, of which the medical field is no exception. AI is an umbrella term defining the practical application of algorithms to generate useful output, without the need of human cognition. Owing to the expanding volume of patient information collected, known as ‘big data’, AI is showing promise as a useful tool in healthcare research and across all aspects of patient care pathways. Practical applications in orthopaedic surgery include: diagnostics, such as fracture recognition and tumour detection; predictive models of clinical and patient-reported outcome measures, such as calculating mortality rates and length of hospital stay; and real-time rehabilitation monitoring and surgical training. However, clinicians should remain cognizant of AI’s limitations, as the development of robust reporting and validation frameworks is of paramount importance to prevent avoidable errors and biases. The aim of this review article is to provide a comprehensive understanding of AI and its subfields, as well as to delineate its existing clinical applications in trauma and orthopaedic surgery. Furthermore, this narrative review expands upon the limitations of AI and future direction. Cite this article:
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.
Joint registries classify all further arthroplasty procedures to a knee with an existing partial arthroplasty as revision surgery, regardless of the actual procedure performed. Relatively minor procedures, including bearing exchanges, are classified in the same way as major operations requiring augments and stems. A new classification system is proposed to acknowledge and describe the detail of these procedures, which has implications for risk, recovery, and health economics. Classification categories were proposed by a surgical consensus group, then ranked by patients, according to perceived invasiveness and implications for recovery. In round one, 26 revision cases were classified by the consensus group. Results were tested for inter-rater reliability. In round two, four additional cases were added for clarity. Round three repeated the survey one month later, subject to inter- and intrarater reliability testing. In round four, five additional expert partial knee arthroplasty surgeons were asked to classify the 30 cases according to the proposed revision partial knee classification (RPKC) system.Aims
Methods
The aim of this study was to systematically compare the safety and accuracy of robot-assisted (RA) technique with conventional freehand with/without fluoroscopy-assisted (CT) pedicle screw insertion for spine disease. A systematic search was performed on PubMed, EMBASE, the Cochrane Library, MEDLINE, China National Knowledge Infrastructure (CNKI), and WANFANG for randomized controlled trials (RCTs) that investigated the safety and accuracy of RA compared with conventional freehand with/without fluoroscopy-assisted pedicle screw insertion for spine disease from 2012 to 2019. This meta-analysis used Mantel-Haenszel or inverse variance method with mixed-effects model for heterogeneity, calculating the odds ratio (OR), mean difference (MD), standardized mean difference (SMD), and 95% confidence intervals (CIs). The results of heterogeneity, subgroup analysis, and risk of bias were analyzed.Aims
Methods
Total knee arthroplasty (TKA) is a major orthopaedic
intervention. The length of a patient's stay has been progressively
reduced with the introduction of enhanced recovery protocols: day-case
surgery has become the ultimate challenge. This narrative review shows the potential limitations of day-case
TKA. These constraints may be social, linked to patient’s comorbidities,
or due to surgery-related adverse events (e.g. pain, post-operative
nausea and vomiting, etc.). Using patient stratification, tailored surgical techniques and
multimodal opioid-sparing analgesia, day-case TKA might be achievable
in a limited group of patients. The younger, male patient without
comorbidities and with an excellent social network around him might
be a candidate. Demographic changes, effective recovery programmes and less invasive
surgical techniques such as unicondylar knee arthroplasty, may increase
the size of the group of potential day-case patients. The cost reduction achieved by day-case TKA needs to be balanced
against any increase in morbidity and mortality and the cost of
advanced follow-up at a distance with new technology. These factors
need to be evaluated before adopting this ultimate ‘fast-track’
approach. Cite this article:
