Periprosthetic joint infection (PJI) represents a complex challenge in orthopaedic surgery associated with substantial morbidity and healthcare expenditures. The debridement, antibiotics, and implant retention (DAIR) protocol is a viable treatment, offering several advantages over exchange arthroplasty. With the evolution of treatment strategies, considerable efforts have been directed towards enhancing the efficacy of DAIR, including the development of a phased debridement protocol for acute PJI management. This article provides an in-depth analysis of DAIR, presenting the outcomes of single-stage, two-stage, and repeated DAIR procedures. It delves into the challenges faced, including patient heterogeneity, pathogen identification, variability in surgical techniques, and antibiotics selection. Moreover, critical factors that influence the decision-making process between single- and two-stage DAIR protocols are addressed, including team composition, timing of the intervention, antibiotic regimens, and both anatomical and implant-related considerations. By providing a comprehensive overview of DAIR protocols and their clinical implications, this annotation aims to elucidate the advancements, challenges, and potential future directions in the application of DAIR for PJI management. It is intended to equip clinicians with the insights required to effectively navigate the complexities of implementing DAIR strategies, thereby facilitating informed decision-making for optimizing patient outcomes. Cite this article:
Prediction tools are instruments which are commonly used to estimate the prognosis in oncology and facilitate clinical decision-making in a more personalized manner. Their popularity is shown by the increasing numbers of prediction tools, which have been described in the medical literature. Many of these tools have been shown to be useful in the field of soft-tissue sarcoma of the extremities (eSTS). In this annotation, we aim to provide an overview of the available prediction tools for eSTS, provide an approach for clinicians to evaluate the performance and usefulness of the available tools for their own patients, and discuss their possible applications in the management of patients with an eSTS. 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.
