Focus areas
The American Joint Replacement Research-Collaborative (AJRR-C) is focused on several research areas with significant methodology gaps in total joint replacement research. Some of these areas of ongoing research include:
- Application of natural language processing informatics tools in joint replacement research. AJRR-C investigators develop natural language processing tools for automated extraction of surgical data from electronic health records.
- Computer vision methods for automated radiographic grading. AJRR-C investigators develop deep learning computer vision algorithms for automated grading of total joint replacement radiographs. Automated radiographic measures have huge potential as objective outcome measures in studies that assess progression of complications.
- Clinical and surgical trials. Multicenter total joint arthroplasty clinical trials are widely needed but very rare — particularly, pragmatic trials of interventions in real-world settings. The collaborative's research addresses technical and methodological issues with various total joint arthroplasty clinical trial designs as well as intervention-specific issues, to make multicenter trials more feasible and successful.
- Confounding and bias in total joint replacement research. AJRR-C investigators are working to describe the types of confounding and bias that arise in observational and interventional studies in total joint arthroplasty using real-world examples.
- Implementation research. Translating findings on effective total joint replacement practices into patient care can be improved by embedding dissemination and implementation principles into clinical research projects.
- Patient-reported outcomes. Patient-reported outcomes are increasingly used in total joint arthroplasty. The collaborative is using real-life data from both the AJRR and Mayo Total Joint Registry to study various aspects of patient-reported outcomes in total joint replacement surgery research.
Projects
Researchers in the American Joint Replacement Research-Collaborative (AJRR-C) support numerous total joint replacement research projects.
Epidemiological research
There are few reliable estimates of the true population impact of total joint replacements. AJRR-C researchers have pooled data from local and national databases to estimate the total number of Americans living with hip and knee arthroplasty — 2.5 million and 4.7 million, respectively. This work provides the most robust estimates of total joint arthroplasty prevalence. Investigators in the collaborative continue to study methodological issues and mortality trends after both primary and revision total joint replacement procedures.
Long-term safety of total joint arthroplasty
Although total joint arthroplasty is generally a safe procedure, there is growing concern about the long-term systemic effects of artificial implants due to metal and plastic debris. AJRR-C researchers are performing a series of observational, clinical and pathological studies examining the neurotoxicity and cardiotoxicity risks in patients undergoing total joint replacement surgery.
Visit the clinical trial page to learn more.
Patient-reported outcomes
Collection of patient-reported outcome measures from patients undergoing total joint replacement surgery at Mayo Clinic dates back to the 1970s. Using historical data, AJRR-C researchers demonstrated that patient-reported outcomes can be used to identify high-risk patients. They can also be used to individualize follow-up after surgery, which remains a major challenge for many orthopedic centers. The collaborative is studying the implementation of patient-reported outcome measures that meet different purposes without overburdening patients.
Health services research
AJRR-C researchers in the American Joint Replacement Research-Collaborative have performed several studies and identified patient-level risk factors associated with health care utilization and costs in total joint arthroplasty. Furthermore, upon request from the Centers for Disease Control and Prevention, the collaborative validated the National Healthcare Safety Network risk models for surgical site infections using linked data from multiple databases and electronic health records.