The Impact of Transitioning From International Classification of Diseases, Ninth Revision to International Classification of Diseases, Tenth Revision on Reported Complication Rates Following Primary Total Knee Arthroplasty

Diagnostic accuracy of algorithms to define incident and second hip fractures: A Taiwan validation study

BACKGROUND

Previous epidemiological researchers have used various algorithms to identify a second hip fracture; however, there has been no validation of these algorithms to date. This study aimed to verify existing algorithms for identifying second hip fracture under the International Classification of Diseases diagnostic coding systems. Furthermore, we examined the validity of two newly proposed algorithms that integrated the concept of periprosthetic fractures and laterality of the ICD-10 coding system.

METHODS

Claims data of patients hospitalized for hip fracture from National Taiwan University Hospitals between 2007 and 2020 were retrieved. Hip fracture was confirmed by 2 orthopaedic surgeons with medical records and imaging data as gold standards. The validity of 9 existing and 2 newly proposed algorithms for identifying second hip fracture was evaluated.

RESULTS

The positive predictive value (PPV) range between 84% and 90% in existing algorithms for identifying second hip fractures. Noteworthy, the longer time interval for discrimination resulted in slightly increased PPV (from 87% to 90%), while decreased sensitivity noticeably (from 87% to 72%). When considering the information about periprosthetic fracture, the PPV increased to 91% without diminished sensitivity. The PPV of the newly proposed ICD-10-specific algorithm was 100%.

CONCLUSION

Algorithms integrated clinical insights of periprosthetic fractures and laterality concept of ICD-10 coding system provided satisfactory validity and help precisely define second hip fracture in future database research.

The Rise of Medicare Advantage: Effects on Total Joint Arthroplasty Patient Care and Research

BACKGROUND

Medicare Advantage (MA) plans are popular among Medicare-eligible patients, but little is known about MA in lower-extremity total joint arthroplasty (TJA). The purpose of this study was to describe trends in MA utilization and analyze differences in patient characteristics and postoperative outcomes between patients undergoing primary TJA using traditional Medicare (TM) or MA plans.

METHODS

Patients ≥65 years of age who underwent primary total knee or total hip arthroplasty were identified using the Premier Healthcare Database. Patients were categorized into TM and MA cohorts. Data from 2004 to 2020 were used to describe trends in insurance coverage. Data from 2015 to 2020 were used to identify differences in patient characteristics and postoperative complications using ICD-10 codes. Multivariate analyses were performed using 2015 to 2020 data to account for potential confounders.

RESULTS

From 2004 to 2020, the proportion of patients with MA increased from 7.9% to 34.4%, while those with TM decreased from 83.7% to 54.0%. Of the 697,317 patients who underwent primary elective TJA from 2015 to 2020, 471,439 (67.6%) had TM coverage and 225,878 (32.4%) had MA coverage. The cohorts were similar in terms of age and sex. However, a higher proportion of Black patients (8.29% compared with 4.62%; p < 0.001) and a lower proportion of White patients (84.0% compared with 89.2%; p < 0.001) were enrolled in MA compared with TM. After controlling for confounders, patients with MA had higher odds of surgical site infection (adjusted odds ratio [aOR]: 1.15; 95% confidence interval [CI]: 1.04 to 1.47; p = 0.031), periprosthetic joint infection (aOR: 1.10; 95% CI: 1.03 to 1.18; p = 0.006), stroke (aOR: 1.15; 95% CI: 1.02 to 1.31; p = 0.026), and acute kidney injury (aOR: 1.08; 95% CI: 1.04 to 1.11; p < 0.001), but lower odds of urinary tract infection (aOR: 0.94; 95% CI: 0.90 to 0.98; p = 0.003).

CONCLUSIONS

From 2004 to 2020, the number of patients utilizing MA increased markedly such that 1 in 3 were covered by MA in 2020. From 2015 to 2020, patients who were non-White were more likely to have MA than TM, and the MA group had a higher rate of several postoperative complications compared with the TM group. As TM claims data inform health-care policy and clinical decisions, this change portends future challenges, including limitations in arthroplasty registry research, an increase in the administrative burden of surgeons, and a potential worsening of social disparities in health care.

Administrative Data Use in National Registry Efforts: Blessing or Curse?

"Big data" refers to a growing field of large database research. Administrative data, a subset of big data, includes information from insurance claims, electronic medical records, and registries that can be useful for investigating novel research questions. While its use provides salient advantages, potential researchers relying on big data would benefit from knowing about how these databases are coded, common errors they may encounter, and how to best use large data to address various research questions. In the first section of this paper, Dr. Nicholas A. Bedard addresses the four major pitfalls to avoid with diagnosis and procedure codes in administrative data. In the next section, Dr. Jeffrey N. Katz et al. focus on the strengths and limitations of administrative data, suggesting methods to mitigate these limitations. Lastly, Dr. Elena Losina et al. review the uses and misuses of large databases for cost-effectiveness research, detailing methods for careful economic evaluations.

Analysis of New Orthopaedic Technologies in Large Database Research

Improvements in orthopaedic surgery go hand in hand with technological advances. The present article outlines the historical and current uses of large databases and registries for the evaluation of new orthopaedic technologies, providing insights for future utilization, with robotic-assisted surgery as the example technology.

A new robotically assisted system for total knee arthroplasty: A sheep model study

BACKGROUND

We investigated the accuracy and safety of a new HURWA robotic-assisted total knee arthroplasty (TKA) system in a sheep model.

METHODS

Ten male small-tailed Han sheep were used in this study. Sheep were imaged by computed tomography scan before and after bone resection and the cutting errors between actual bone preparation and preoperative planning of the femur and tibia in three dimensions were measured.

RESULTS

The overall accuracies after surgery compared with that from preoperative surgical planning of the left and right femurs were 1.93 ± 1.02° and 1.93 ± 1.23°, respectively. Additionally, similarly high overall accuracies for the left and right tibia of 1.26 ± 1.04 and 1.68 ± 0.92°, respectively, were obtained. The gap distances of the distal cut, anterior chamfer, anterior cut, posterior chamfer and posterior cut on the medial side were 0.47 ± 0.35 mm, 0.41 ± 0.37 mm, 0.12 ± 0.26 mm, 0.41 ± 0.44 mm and 0.12 ± 0.23 mm, respectively. No intraoperative complications, such as intraoperative fracture, massive bleeding or death, occurred.

CONCLUSION

This new HURWA robotic-assisted TKA system is an accurate and safe tool for TKA surgery based on the sheep model.