Real-World Evidence: A Review of Real-World Data Sources Used in Orthopaedic Research

The Impact of National Orthopaedic Fracture Registries: A Systematic Review

Purpose

Large-scale registry data efforts are common in orthopaedic surgery; however, there is wide variation between registries and little known about registry impact. The purpose of this study was to assess the publication or "scholarly" impact of current national trauma/fracture registries.

Methods

A search was executed to identify potential national and international trauma/fracture registries and create a comprehensive list of registries. Thirty-four relevant national registries were identified. Searches were executed to identify any published articles published by or affiliated with the selected registries. Over 13,000 abstracts were screened by at least 2 reviewers. The full text of 650 articles were screened by at least 2 reviewers, and data were extracted from 383 articles. Citations were excluded if they were reviews and meta-analyses; referencing non-trauma-specific, insurance, institutional, or state-wide databases; describing facial, spine, or rib trauma; reporting nonregistry data; and did not report on fracture patients. Data were collected at the article level and registry level.

Results

The median number of articles per registry was 3.5 (range: 1-66) with a median impact factor of 3.2 (range per registry: 1.4-11.0). The National Hip Fracture Database (United Kingdom) had the highest publication rate per year of any registry (3.9). Twenty-four percent of registries had a publication rate of greater than 1 article per year, whereas only 8.8% had a publication rate over 2 per year. Only 8 registries had 10 or more publications in total. The National Trauma Data Bank (United States) had the highest number of fracture-specific publications overall (66) and an average citation rate of 29 per publication. The National Hip Fracture Database had the second highest number of article (62) with an average citation per article of 23.6.

Conclusion

With the exception of a few registries, most national trauma/fracture registries have low yearly publication and citation rates. Researchers must consider the utility of resources needed to sustain registry efforts in the context of the impact of registry data. Future studies will seek to identify salient features of the highest impact registries.

The future outlook for data in orthopedic surgery: A new era of real-time innovation

The orthopedic field is on the brink of a significant transformation-a shift from retrospective analysis to real-time decision-making fueled by data. The dependence on historical trends or long-term studies is yielding to an era where data flows dynamically, allowing medical professionals to adjust protocols instantly. This isn't just an evolution; it's a revolution. Data is no longer a passive observer of outcomes-it's an active participant in shaping them.Imagine a future where wearable devices, artificial intelligence (AI) algorithms, and predictive analytics come together to guide surgeons in real time. For example, wearables monitor vital signs during surgery and oversee rehabilitation while AI analyzes data to predict complications. Postoperative protocols adapt to individual recovery journeys, not averages. Complication risks are flagged preemptively, and treatment plans evolve with patient progress. This shift empowers orthopedic professionals to respond and anticipate, creating a level of care precision that was once unimaginable.What if we viewed data not merely as a tool but as collaborators? With AI and machine learning, the surgical suite of tomorrow transforms into ecosystems where data communicates directly providing insights, suggesting strategies, and enhancing outcomes. This collaborative approach encourages our conventional medical mindset to prioritize adaptability and individualization.The provocative truth is that the game-changer in orthopedics isn't a new implant design or surgical technique-it's the mindset shift to trust real-time data as the foundation of every decision. Orthopedics is no longer about perfecting procedures but refining protocols for every patient consistently.As we race toward the future, equitable access becomes crucial. As William Gibson noted, "The future is already here - it's just not very evenly distributed." We must ensure these breakthroughs reach everyone, bridging the gap between potential and practice. The future of orthopedics isn't just a vision - it's a promise, and it's time to deliver.

Linking Hip Disability and Osteoarthritis Outcome Score-Physical Function Short Form and PROMIS Physical Function

INTRODUCTION

Linking scores on patient-reported outcome measures can enable data aggregation for research, clinical care, and quality. We aimed to link scores on the Hip Disability and Osteoarthritis Outcome Score-Physical Function Short Form (HOOS-PS) and the Patient-reported Outcomes Measurement Information System Physical Function (PROMIS PF).

METHODS

A retrospective study was conducted from 2017 to 2020 evaluating patients with hip osteoarthritis who received routine clinical care from an orthopaedic surgeon. Our sample included 3,382 unique patients with 7,369 pairs of HOOS-PS and PROMIS PF measures completed at a single nonsurgical, preoperative, or postoperative time point. We included one randomly selected time point of scores for each patient in our linking analysis sample. We compared the accuracy of linking using four methods, including equipercentile and item response theory-based approaches.

RESULTS

PROMIS PF and HOOS-PS scores were strongly correlated ( r = -0.827 for raw HOOS-PS scores and r = 0.820 for summary HOOS-PS scores). The assumptions were met for equipercentile and item response theory approaches to linking. We selected the item response theory-based Stocking-Lord approach as the optimal crosswalk and estimated item parameters for the HOOS-PS items on the PROMIS metric. A sensitivity analysis demonstrated overall robustness of the crosswalk estimates in nonsurgical, preoperative, and postoperative patients.

CONCLUSION

These crosswalks can be used to convert scores between HOOS-PS and PROMIS PF metric at the group level, which can be valuable for data aggregation. Conversion of individual patient-level data is not recommended secondary to increased risk of error.