Clinical predictors for possible failure after total hip arthroplasty

Do 'Surgeon Champions' and High-Volume Surgeons Have Lower Rates of Periprosthetic Femur Fracture? Perspective from a State-wide Quality Improvement Registry

BACKGROUND

Recently, a state-wide registry identified fracture as a major cause of total hip arthroplasty revision. There were 52.8% of revisions occurring within 6 months (fracture leading cause). Registry sites have a 'Surgeon Champion' who acts as liaison and advocate. This study evaluated the effect of surgeon volume and role of 'surgeon champion' on fracture rates.

METHODS

There were 95,948 cases from 2012 to 2019 queried with peri-implant femoral fractures identified (within 6 months). Funnel plots were generated to compare individual surgeon-specific fracture rates. Surgeons who had a fracture rate below the confidence interval were labeled 'green' (lower than mean), within were 'yellow' (no difference), and above were 'red' (significantly higher).

RESULTS

For all surgeons, 19.6% were red, 72.1% yellow, and 8.3% green. There were 17.2% 'Surgeon Champions' and 6.2% 'Non-Champions' that were green (P=0.01), while 20.7 and 19.3% were red (P=0.82). There was a significant association between volume and performance (P<0.01). No surgeons in the lower two quartiles (<84; 84 to 180 cases), while 4 and 29% of higher-volume surgeons (181 to 404; >404 cases) were green. There was no statistical difference in red status by volume (P=0.53).

CONCLUSION

'Surgeon champions' and high-volume surgeons were more likely to be high performers, but not less-likely to be low performers. Active involvement in quality improvement and/or high-volume was associated with better outcomes, but did not impart complication immunity. 'Green' surgeons should mentor colleagues to help reduce fractures by re-evaluating modifiable factors. Analyzing outcomes to promote quality and decrease complications is paramount.

Causes of Early Hip Revision Vary by Age and Gender: Analysis of Data From a Statewide Quality Registry

BACKGROUND

While total hip arthroplasty (THA) is extremely successful, early failures do occur. The purpose of this study was to determine the cause of revision in specific patient demographic groups at 3 time points to potentially help decrease the revision risk.

METHODS

Data for cases performed between 2012 and 2018 from a statewide, quality improvement arthroplasty registry were used. The database included 79,205 THA cases and 1,433 revisions with identified etiology (1,584 in total). All revisions performed at <5 years from the primary THA were reviewed. Six groups, men/women, <65, 65-75, and >75 years, were compared at revision time points <6 months, <1 year, and <5 years.

RESULTS

There were obvious and significant differences between subgroups based on demographics and time points (P < .0001). Seven hundred and fifty-six (53%) of all revisions occurred within 6 months. The most common etiologies within 6 months (756 revisions) were fracture (316, 41.8%), dislocation/instability (194, 25.7%), and infection (98, 12.9%). At this early time point, the most common revision cause was fracture for all age/gender-stratified groups, ranging from 27.6% in young men to 60% in older women. Joint instability became the leading cause for revision after 1 year in all groups.

CONCLUSION

This quality improvement project demonstrated clinically meaningful differences in the reason for THA revision between gender, age, and time from surgery. Strategies based on these data should be employed by surgeons to minimize the factors that lead to revision.

Medical device surveillance with electronic health records

Post-market medical device surveillance is a challenge facing manufacturers, regulatory agencies, and health care providers. Electronic health records are valuable sources of real-world evidence for assessing device safety and tracking device-related patient outcomes over time. However, distilling this evidence remains challenging, as information is fractured across clinical notes and structured records. Modern machine learning methods for machine reading promise to unlock increasingly complex information from text, but face barriers due to their reliance on large and expensive hand-labeled training sets. To address these challenges, we developed and validated state-of-the-art deep learning methods that identify patient outcomes from clinical notes without requiring hand-labeled training data. Using hip replacements-one of the most common implantable devices-as a test case, our methods accurately extracted implant details and reports of complications and pain from electronic health records with up to 96.3% precision, 98.5% recall, and 97.4% F1, improved classification performance by 12.8-53.9% over rule-based methods, and detected over six times as many complication events compared to using structured data alone. Using these additional events to assess complication-free survivorship of different implant systems, we found significant variation between implants, including for risk of revision surgery, which could not be detected using coded data alone. Patients with revision surgeries had more hip pain mentions in the post-hip replacement, pre-revision period compared to patients with no evidence of revision surgery (mean hip pain mentions 4.97 vs. 3.23; t = 5.14; p < 0.001). Some implant models were associated with higher or lower rates of hip pain mentions. Our methods complement existing surveillance mechanisms by requiring orders of magnitude less hand-labeled training data, offering a scalable solution for national medical device surveillance using electronic health records.

Current Epidemiology of Revision Total Hip Arthroplasty in the United States: National Inpatient Sample 2009 to 2013

BACKGROUND

Despite the excellent outcomes associated with primary total hip arthroplasty (THA), implant failure and revision continues to burden the healthcare system. THA failure has evolved and displays variability throughout the literature. In order to understand how THAs are failing and how to reduce this burden, it is essential to assess modes of implant failure on a large scale. Thus, we report: (1) etiologies for revision THA; (2) frequencies of revision THA procedures; (3) patient demographics, payor type, and US Census region of revision THA patients; and (4) the length of stay and total costs based on the type of revision THA procedure.

METHODS

We queried the National Inpatient Sample database for all revision THA procedures performed between January 1, 2009 and December 31, 2013. This yielded 258,461 revision THAs. Patients specific demographics were identified in order to determine the prevalence of revision procedure performed.

RESULTS

Dislocation was the main indication for revision THA (17.3%), followed by mechanical loosening (16.8%). All-component revision was the most common procedure performed (41.8%). Patients were most commonly white (77.4%), aged 75 years and older (31.6%), and resided in the South US Census region (37.0%). The average length of stay for all procedures was 5.29 days. The mean total charge for revision THA procedures was $77,851.24.

CONCLUSION

Dislocation and mechanical loosening is the predominant indication for revision THA in the United States. With the frequency of revision THAs projected to double in the next decade, orthopedists must take steps to mitigate this potentially devastating complication.