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Record W4416209151 · doi:10.1302/1358-992x.2025.13.008

EFFECT OF PREOPERATIVE SELF-REPORTED ANXIETY AND/OR DEPRESSION ON PATIENT-REPORTED OUTCOMES IN HIP AND KNEE ARTHROPLASTY PATIENTS

2025· article· en· W4416209151 on OpenAlexaff
Shannon Weir-Seeley, James P. Waddell, Clarissa Encisa, Ricardo Antonio Escamilla-Santiago, Michael Terner

Bibliographic record

VenueOrthopaedic Proceedings · 2025
Typearticle
Languageen
FieldMedicine
TopicTotal Knee Arthroplasty Outcomes
Canadian institutionsCanadian Institute for Health Information
Fundersnot available
KeywordsAnxietyDepression (economics)ArthroplastyKnee replacementMental healthHip arthroplastyJoint replacementOrthopedic surgery

Abstract

fetched live from OpenAlex

Hip and knee replacements are among the most effective treatments for degenerative joint conditions. Patient-reported outcome measures (PROMs) are essential to a person-centred approach to health care, providing the patients’ perspective on aspects of their health status. Pain, anxiety and depression are important to consider as they are associated with poorer outcomes yet are potentially modifiable. This work aims to examine the relationship of self-reported pre-operative anxiety and/or depression with PROMs one year after a hip or knee replacement. PROMs, including Oxford Hip/Knee Scores (OHS/OKS), EQ-5D-5L and post-operative satisfaction, were completed by patients who had a primary, elective, unilateral hip or knee replacement between 2018 and 2022. Demographic characteristics and self-reported outcomes at 12 months (functional status, pain, and satisfaction) were compared between patients reporting any problems with anxiety and/or depression before surgery versus no problems as measured by the EQ-5D-5L. Differences in baseline characteristics (age, sex) were accounted for when comparing the effect of mental health concerns on outcomes. A total of 3,326 hip and 3,730 knee replacement patients completed PROMs before and one year after surgery. Before surgery, 60% and 55% of hip and knee replacement patients reported some level of anxiety and/or depression. We observe that patients with anxiety and/or depression are at a higher risk of poorer self-reported functional status before and after surgery, but the average improvement in their Oxford score is slightly higher than those without anxiety and/or depression. The proportion of patients who reached a minimal clinically important difference (based on Deckey et al.) are also similar between patients with mental health concerns versus without (hip: 92% vs. 91%; knee 88% vs. 86%). Patients with anxiety and/or depression are also at a higher risk of having problems with pain a year after surgery than patients without mental health concerns (hip OR: 1.82, p=< .0001; knee OR: 1.55, p=< .0001). Five percent or less of patients reported worsened pain for both groups. Finally, anxiety and/or depression is also a predictor of feeling neutral or dissatisfied with a patient's surgical results (hip OR: 1.51, p=0.003; knee OR: 1.56, p=< .0001). These relationships were observed in both hip and knee patient groups. We found that patients who reported feeling anxious and/or depressed before surgery are likely to attain similar improvement in self-reported functional outcomes compared to patients without psychological concerns. Further investigation is needed to understand why patients having anxiety and/or depression were more likely to be dissatisfied with their surgical results. A limitation of this analysis is the lack of data on patient expectations, which has been shown to be a predictor of satisfaction. Selection bias cannot be discounted as it may be possible that patients who attain better functional outcomes are more likely to complete PROMs post-operatively. These results demonstrate the need to address mental health concerns for these patients (e.g., pre-operative counselling on expectation setting and the impact of anxiety and/or depression on perceived functional gains) to improve satisfaction with surgery and outcomes.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.036
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.006
GPT teacher head0.255
Teacher spread0.249 · 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 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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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