Patients undergoing total hip and knee arthroplasty are at high risk for venous thromboembolism (VTE) with an incidence of approximately 0.6–1.5%. Given the high volume of these operations, with approximately one million performed annually in the U.S., the rate of VTE represents a large absolute number of patients. The rate of VTE after total hip arthroplasty has been stable over the past decade, although there has been a slight reduction in the rate of deep venous thrombosis (DVT), but not pulmonary embolism (PE), after total knee arthroplasty. Over this time, there has been significant research into the optimal choice of pharmacologic VTE prophylaxis for individual patients, with the objective to reduce the rate of VTE while minimizing adverse side effects such as bleeding. Recently, aspirin has emerged as a promising prophylactic agent for patients undergoing arthroplasty due to its similar efficacy and good safety profile compared to other pharmacologic agents. However, there is no evidence to date that clearly demonstrates the superiority of any given prophylactic agent. Therefore, this review discusses (1) the current prevalence and trends in VTE after total hip and knee arthroplasty and (2) provides an update on pharmacologic VTE prophylaxis in regard to aspirin usage.

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.