Tech-trends in orthopedics 2018

Forty-Six Years of Technological Innovation in Musculoskeletal Medicine: An Analysis of a Patent Database (1973 to 2018)

BACKGROUND

Technological innovation in orthopaedics is key to advancing patient care. As emerging technologies near maturity, clinicians must be able to objectively assess where and when these technologies can be implemented. Patent databases are an underappreciated resource for quantifying innovation, especially within orthopaedic surgery. This study used a patent database to assess patent activity and relative growth of technologies in musculoskeletal medicine and orthopaedics over a period of 46 years.

METHODS

A total of 121,471 patent records were indexed from Lens.org , a patent database. These patents were grouped into subspecialty clusters and technology clusters using patent codes. Five-year (2014 to 2018), 10-year (2009 to 2018), and 30-year (1989 to 2018) compound annual growth rates were calculated and compared for each cluster.

RESULTS

Annual patent activity increased from one patent in 1973 to 4,866 patents in 2018. Of the eight subspecialty clusters, the largest number of patents were related to 'Inflammation' (n = 63,128; 40.57%). The 'Elbow', 'Shoulder', and 'Knee' clusters experienced increased annual patent activity since 2000. Of the 12 technological clusters, the largest number of patents were related to 'Drugs' (n = 55,324; 39.75%). The 'Custom/patient-specific instrumentation, 'Computer Modeling', 'Robotics', and 'Navigation' clusters saw growth in the average annual patent activity since 2000.

DISCUSSION

Innovation, as measured by patent activity in musculoskeletal medicine and orthopaedics, has seen notable growth since 1973. The 'Robotics' cluster seems poised to experience exponential growth in industry investment and technological developments over the next 5 to 10 years. The 'Diagnostics', 'Computer Modeling', 'Navigation', and 'Design and Manufacturing' clusters demonstrate potential for exponential growth in industry investment and technological developments within the next 10 to 20 years.

New versus old: 95 degree angled blade plate versus distal femoral locking plate. A randomized clinical trial

INTRODUCTION

The purpose was to compare the locking condylar plate (LCP) with the 95° angled blade plate (ABP) for distal femoral fracture patterns amenable to either device. Our hypothesis was that LCP would outperform ABP with primary outcome of nonunion and secondary outcomes of reoperation and malunion.

METHODS

78 adult patients with 79 eligible fractures and mean age of 60 years were prospectively randomized to LCP (n = 45) or ABP (n = 34). Mean ISS was 16. 22% fractures were open. Patient-reported functional outcomes were assessed with the Musculoskeletal Function Assessment (MFA).

RESULTS

All patients were followed to union, and mean follow-up was 25 months. There were no differences in age, sex, fracture pattern, co-morbidities or mechanism for the two groups. Three patients had infections, one superficial after LCP and one deep infection each after ABP and LCP. Three patients developed nonunion after LCP versus none after ABP (p = 0.06). Nine patients healed with minor deformity after LCP versus 5 following ABP. One fracture had major deformity after ABP versus two after LCP. Eight patients had a secondary procedure following LCP versus two after ABP (p = 0.05).

CONCLUSIONS

The investigation failed to support newer technology being better. Evidence did not indicate the LCP was superior to the ABP. Trends for primary union and fewer secondary procedures suggest that ABP may have superior performance to LCP for fracture patterns which may be treated with either implant.

Is There an Association Between Authors' Conflicts of Interest and Outcomes in Clinical Studies Involving Autologous Chondrocyte Implantation?

BACKGROUND

Autologous chondrocyte implantation (ACI) is an increasingly popular technique for the treatment of articular cartilage defects. Because several companies have financial interests in ACI, it is important to consider possible conflicts of interest when evaluating studies reporting outcomes of ACI.

PURPOSE

To determine whether there is an association between authors' financial conflicts of interest and the outcomes of ACI studies.

STUDY DESIGN

Cross-sectional study.

METHODS

A search of PubMed and MEDLINE databases for "autologous chondrocyte implantation" was performed. Clinical studies published after 2012 through May 15, 2019, and in English were included. Studies were determined to have financial conflicts of interest if any contributing author had relevant conflicts, either self-reported in the published study's disclosures section or reported online in the American Academy of Orthopaedic Surgeons Disclosure database or the Centers for Medicare & Medicaid Services Open Payments database. The outcomes of each study were rated as favorable, equivocal, or unfavorable based on predefined criteria and then tested for association with conflicts of interest through use of the Fisher exact test.

RESULTS

A total of 79 studies met the inclusion criteria. Nearly all studies were of level 3 or 4 evidence. Conflicts of interest were established in 51.90% of studies (n = 41). Conflicts that were not self-reported by the authors were discovered in 18% of studies. The level of evidence was not associated with conflict of interest. No statistically significant difference was found in the rate of favorable outcomes between studies with conflicts (92.68%) and those with no conflicts (81.58%) (P = .126). Publications by US authors were more likely to have financial conflicts of interest (P = .003).

CONCLUSION

Favorable results were reported in a majority of studies involving ACI. No statistical association was found between the frequency of favorable outcomes and the presence of financial conflicts of interest, country of authorship, or level of evidence. There was a trend toward more favorable outcomes in studies with conflicts of interest. Additionally, nearly 20% of publications had possible conflicts found online that were not self-reported. It is critical for orthopaedic surgeons to judiciously evaluate published studies and consider financial conflicts of interest before performing ACI techniques on patients.

Measuring adverse events following hip arthroplasty surgery using administrative data without relying on ICD-codes

Introduction

Measure and monitor adverse events (AEs) following hip arthroplasty is challenging. The aim of this study was to create a model for measuring AEs after hip arthroplasty using administrative data, such as length of stay and readmissions, with equal or better precision than an ICD-code based model.

Materials and methods

This study included 1 998 patients operated with an acute or elective hip arthroplasty in a national multi-centre study. We collected AEs within 90 days following surgery with retrospective record review. Additional data came from the Swedish Hip Arthroplasty Register, the Swedish National Patient Register and the Swedish National Board of Health and Welfare. We made a 2:1 split of the data into a training and a holdout set. We used the training set to train different machine learning models to predict if a patient had sustained an AE or not. After training and cross-validation we tested the best performing model on the holdout-set. We compared the results with an established ICD-code based measure for AEs.

Results

The best performing model was a logistic regression model with four natural age splines. The variables included in the model were as follows: length of stay at the orthopaedic department, discharge to acute care, age, number of readmissions and ED visits. The sensitivity and specificity for the new model was 23 and 90% for AE within 30 days, compared with 5 and 94% for the ICD-code based model. For AEs within 90 days the sensitivity and specificity were 31% and 89% compared with 16% and 92% for the ICD-code based model.

Conclusion

We conclude that a prediction model for AEs following hip arthroplasty surgery, relying on administrative data without ICD-codes is more accurate than a model based on ICD-codes.