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Record W4385412462 · doi:10.1177/2325967123s00338

Poster 375: Incidence of Tendon Ruptures in the US: Comparison of Differences Across Racial Groups

2023· article· en· W4385412462 on OpenAlexaboutno aff
Fehmi Berkay, Arjun Minhas, Anil Krishnamurthy, Joseph G. Lyons

Bibliographic record

VenueOrthopaedic Journal of Sports Medicine · 2023
Typearticle
Languageen
FieldMedicine
TopicOrthopedic Surgery and Rehabilitation
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineIncidence (geometry)EpidemiologyPacific islandersPopulationDemographyTendonAchilles tendonRace (biology)SurgeryPathologyEnvironmental health

Abstract

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Objectives: Tendon ruptures affect quality of life, participation in competitive athletics and increase healthcare costs. Previous literature has focused on diagnostic evaluation and management of tendon ruptures and there is scarce epidemiological data on this topic. The epidemiological literature that does exist regarding this topic is often limited to the achilles and patellar tendons. Furthermore, to our knowledge these studies are largely based out of Europe or Canada and prior to 2010. The purpose of this study was to provide a U.S population-based study using the NEISS database, on the incidence of tendon ruptures and describe whether racial differences exist in the incidence of tendon ruptures. Methods: The NEISS, a nationally representative sample of US hospital emergency departments, was used to ascertain the incidence rates of various types of tendon ruptures in the United States. All cases from 2001-2020 were reviewed for case narratives to select only those cases with a clear diagnosis of an acute primary tendon rupture. After exclusions, a total of n=5,765 unweighted cases were identified, representing a national estimate of N=235,189 tendon ruptures (95% CI=186,833-283,546). The NEISS database contains patient demographic information including age, sex, and race. Race categories were regrouped as follows: American Indian/Alaska Native (AI/AN) individuals were added to the category “Other,” and Native Hawaiian(NH)/Pacific Islander (PI) individuals were added to the category “Asian” due to the small number of cases involving these groups. Thus, the race categories for final analysis included (1) White, (2) Black/African American, (3) Other (including “Other” and “AI/AN”), and (4) Asian/Pacific Islander (“Asian,” including “Asian” and “NH/PI”). Statistical analysis was performed using the survey data commands in Stata/IC, version 17.0 (StataCorp, College Station, TX, US), accounting for sample weights and the complex survey designs. The incidence rate (IR) of tendon ruptures and mechanism were calculated and chi-square tests were used to compare estimated IRs between groups. Incidence rate ratios (IRRs) are reported and represent unitless expressions of risk used for the comparison of IRs between two distinct subgroups, with the IR of an identified referent subgroup serving as the denominator. IRRs among race, sex, and age groups are reported. Student t-test/analysis of variance and design-adjusted Rao-Scott chi-square analysis was used for direct comparisons of means for continuous variables and proportions for categorical variables, respectively. Temporal trends and annual percentage changes were assessed via regression analysis. Results: Race was specified in 71.4% of cases. Race data was unavailable for the remaining 28.6% of cases. Unweighted case numbers (n) and national estimates (N) for each racial category, prior to regrouping, are displayed in Table 1. Due to the small number of cases involving AI/AN and NH/PI individuals, these were regrouped for further analysis, as described in the Methods. The largest total number of injuries occurred in White patients, with an estimated N=115,905 tendon ruptures (95% CI=83,461-148,348) in this group for an overall IR of 24.0 PYR (95% CI=17.3-30.7). As seen in Table 2, Black patients had the highest overall IR, with an estimated N=38,679 ruptures (95% CI=21,552-55,807) occurring in this group for an overall IR of 47.8 PYR (95% CI=26.6-68.9). An estimated N=9,186 ruptures (95% CI=5,881-12,491) occurred in patients of Other race for an overall IR of 43.5 PYR (95% CI=27.8- 59.1). Finally, N=4,151 ruptures (95% CI=777-7,524) occurred in Asian patients for an overall IR of 12.9 PYR (95% CI=2.4-23.2). When compared with White patients, the overall injury rate was significantly higher among Black patients (IRR=1.99, 95% CI=1.06-3.76; p<0.05) and Other patients (IRR=1.81, 95% CI=1.09-3.03; p<0.05), and was similar in Asian patients (IRR=0.53, 95% CI=0.27-1.06; p>0.05). There was no statistically significant difference in overall incidence among Black and Other patients (with Other race as the referent, IRR=1.10, 95% CI=0.62-1.95; p>0.05). Figure 1 illustrates the rising annual incidence of tendon ruptures overall and the differences between racial groups between 2001-2020. Conclusions: To our knowledge, no other study analyzes racial differences in tendon injuries in the general US population. Our study showed that patients who identified within the "Black" race had a significantly higher IR of tendon rupture than “White” and “Asian” patients but no statistically significant difference compared to “Other” races. Higher incidence rate of tendon rupture seen in Black individuals is likely multifaceted and further studies would be required to ascertain specific differences. Tendon injuries are often due to acute sudden contractile forces that overcome the ultimate strength of the specific tendon. Therefore, differences seen in our study most likely arise from differences in recreational activity/ sports participation. However, overuse, age-related degeneration, genetics, corticosteroids have all been described as risk factors for tendon injury. The purpose of this study was to identify that there is indeed a difference in incidence rate of tendon rupture between racial groups in the US. The limitation of our study is that the NEISS database is composed entirely of ED encounters and the accuracy of the IR reported in this study are also highly reliant upon accuracy of the case narrative descriptions, which are prone to reporter bias. Despite these limitations, the NEISS database is an established model for epidemiological surveillance of musculoskeletal injuries. Its strengths include its large scale, heterogeneous patient population, and substantial external validity. [Table: see text][Table: see text]

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.080

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0240.004

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.026
GPT teacher head0.336
Teacher spread0.310 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2023
Admission routes1
Has abstractyes

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