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Record W3191972360 · doi:10.2106/jbjs.21.00497

Central Sensitization Is an Important Factor in the Outcome of Patients Undergoing Total Knee Arthroplasty

2021· article· en· W3191972360 on OpenAlexaboutno aff
Nicholas A. Bedard

Bibliographic record

VenueJournal of Bone and Joint Surgery · 2021
Typearticle
Languageen
FieldMedicine
TopicTotal Knee Arthroplasty Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsMinimal clinically important differenceMedicineWOMACPhysical therapyReceiver operating characteristicArthroplastyTotal knee arthroplastyOsteoarthritisPatient-reported outcomeQuality of life (healthcare)SurgeryInternal medicineRandomized controlled trialAlternative medicine

Abstract

fetched live from OpenAlex

Commentary It is well known that as many as 20% of patients are not satisfied after total knee arthroplasty (TKA), with such patients often reporting persistent pain or dysfunction1. Although there are many possible causes for dissatisfaction after TKA, it has become increasingly recognized in recent years that there are a multitude of factors outside of the surgical procedure that contribute to the overall success of TKA. An emerging area of research has sought to identify the role of central sensitization (CS) on the outcomes of TKA. Prior literature has demonstrated that patients with CS have worse quality of life, functional disability, and dissatisfaction than patients without CS after TKA despite achieving comparable clinical improvements after the surgical procedure2. This begs the question of whether CS impacts the calculation of the minimal clinically important difference (MCID) for outcome measures commonly utilized to evaluate patients who undergo TKA. Kim et al. attempted to answer this question by retrospectively evaluating 422 patients who underwent primary TKA. The authors preoperatively evaluated patients for CS utilizing a validated self-reported inventory for CS syndrome and obtained preoperative and 2-year postoperative Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) scores for all patients. The MCID was then calculated for patients with and without CS via both anchor-based and distribution methodology, utilizing both the change difference method and the receiver operating characteristic (ROC) curve method for anchor-based analyses. The authors should be commended for utilizing multiple methods for calculating the MCID, as this provides a much more inclusive evaluation of this important end point. One of the more interesting findings of this article was that 1 in 4 patients who underwent TKA in this series met the criteria for CS syndrome. Although this is not a new finding, it further highlights that CS is likely to be commonly encountered by orthopaedic providers caring for patients who undergo TKA. This study also demonstrated that the calculated MCIDs for patients with CS were significantly larger than those for patients without CS. Although the clinical importance of a 2 to 3-point difference in the MCID for the ROC curve and distribution-based methods of calculation has yet to be determined, the MCID was almost 9 points larger for patients with CS (23.4 points) compared with patients without CS (14.7 points) when calculated with the change difference method. This is a quite large and likely clinically important difference in MCID values. The trends from the data presented in the article are evident, and this study clearly demonstrated that patients with CS required larger improvements in clinical outcomes compared with patients without CS to reach a level that was considered a clinically meaningful improvement. This study further highlighted the complexity of caring for patients who underwent TKA and have CS by demonstrating that patients with CS were much less likely than patients without CS to even achieve an MCID after TKA. Depending upon the method of MCID determination, patients with CS achieved an MCID for the total WOMAC score at a rate of 13% to 15% less than patients without CS. Thus, not only do patients with CS require larger improvements in function to reach the MCID, but they are also significantly less likely to reach this outcome threshold. Although this article brings to light the importance of considering CS when evaluating MCIDs after TKA, it would have been helpful if the authors would have also evaluated the impact of CS on other defined clinical outcome thresholds such as the patient acceptable symptom state (PASS) and substantial clinical benefit (SCB), rather than focusing solely on the minimum improvement necessary to define success. This tiered approach to evaluating clinical outcomes scores (MCID, PASS, and SCB) provides a much more comprehensive view of patient outcomes3, and I suspect that the impact of CS would be even more magnified when analyzing higher definitions of clinical success. Nonetheless, Kim et al. demonstrate that CS is commonly encountered in patients who undergo TKA and is an important factor in their outcomes following the surgical procedure. CS should be considered when utilizing an MCID to compare interventions or outcomes between cohorts undergoing TKA going forward. Additionally, future research is now needed to identify ways in which we can intervene for patients with CS to bring their outcomes more closely in line with those of patients without CS.

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)0.003
metaresearch head score (Gemma)0.037
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.037
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.000
Research integrity0.0050.003
Insufficient payload (model declined to judge)0.0080.001

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 head0.027
GPT teacher head0.251
Teacher spread0.223 · 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
Published2021
Admission routes1
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