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Record W3136489843 · doi:10.1177/1179544121994092

Delay to TKA and Costs Associated with Knee Osteoarthritis Care Using Intra-Articular Hyaluronic Acid: Analysis of an Administrative Database

2021· article· en· W3136489843 on OpenAlexaff
Andrew L. Concoff, Faizan Niazi, Forough Farrokhyar, Akram Alyass, Jeffrey Rosen, Mathew Nicholls

Bibliographic record

VenueClinical Medicine Insights Arthritis and Musculoskeletal Disorders · 2021
Typearticle
Languageen
FieldMedicine
TopicOsteoarthritis Treatment and Mechanisms
Canadian institutionsMcMaster UniversityImpact
Fundersnot available
KeywordsMedicineOsteoarthritisTotal knee arthroplastyHyaluronic acidViscosupplementationArthroplastyRetrospective cohort studyInternal medicineSurgeryPhysical therapyIntra articularAlternative medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Total knee arthroplasty (TKA) is a surgical treatment for patients with knee osteoarthritis (KOA) that no longer experience symptom relief from non-operative or pharmacologic treatments. Non-operative KOA management aims to address patient symptoms and improve function, as well as forestall or mitigate the large costs associated with TKA. The primary objective of this study was to examine the relationship between intra-articular hyaluronic acid (IA-HA) treatment and delaying TKA in patients with KOA compared to patients not receiving IA-HA, as well as to identify differences in KOA-related costs incurred among patients who received or did not receive IA-HA. METHODS: This was a retrospective analysis of an administrative claims database from October 1st, 2010 through September 30th, 2015. Kaplan-Meier survival analysis was conducted to determine the TKA-free survival of patients who received IA-HA, stratified by the number of injection courses received versus those who did not receive any IA-HA. Median KOA-related costs per year were calculated for 2 comparisons: (1) patients who received IA-HA versus patients who did not receive IA-HA, among patients who eventually had TKA, and (2) patients who received IA-HA versus patients who did not receive IA-HA, among patients who did not have TKA. RESULTS: A total of 744 734 patients were included in the analysis. A delay to TKA was observed after IA-HA treatment for patients treated with IA-HA compared to those who did not receive IA-HA. At 1 year, the TKA-free survival was 85.8% (95% CI: 85.6%-86.0%) for patients who received IA-HA and 74.1% (95% CI: 74.0%-74.3%) for those who did not receive IA-HA. At 2 years, the TKA free survival was 70.8% (70.5%-71.1%) and 63.7% (63.5%-63.9%) in the 2 groups, respectively. Patients treated with multiple courses of IA-HA demonstrated an incremental increase in delay to TKA with more courses of IA-HA, suggesting that the risk of TKA over the study time period is reduced with additional IA-HA courses. The hazard ratio for the need of TKA was 0.85 (95% CI 0.84-0.86) for a single course and 0.27 (95% CI 0.25-0.28) for ⩾5 courses, both compared to the no IA-HA group. In patients that eventually had TKA, the median KOA-related costs were lower among those who received IA-HA before their TKA ($860.24, 95% CI: 446.65-1722.20), compared to those who did not receive IA-HA ($2659.49, 95% CI: 891.04-7480.38). For patients who did not have TKA, the median and interquartile range (IQR) KOA-related costs per year were similar for patients who received IA-HA compared with those who did not. CONCLUSION: These results demonstrate that within a large cohort of KOA patients, individuals who received multiple courses of IA-HA had a progressively greater delay to TKA compared to patients who did not receive IA-HA treatment. Also, for patients who progressed to TKA, IA-HA treatment was associated with a large reduction in KOA-related healthcare costs. Based on these results, multiple, repeat courses of IA-HA may be beneficial in substantially delaying TKA in KOA patients, as well as minimizing KOA-related healthcare costs.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.961
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.025
GPT teacher head0.338
Teacher spread0.313 · 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 teacher head, not a consensus.

Study designOther design
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".

Quick stats

Citations29
Published2021
Admission routes1
Has abstractyes

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