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Record W4393345133 · doi:10.1111/ans.18984

Addressing important evidence gaps in the management of prosthetic joint infection: clinician attitudes and equipoise

2024· article· en· W4393345133 on OpenAlexaffabout
Burcu Isler, Natalie M. Niessen, David G. Campbell, Andrew D. Toms, Nick Daneman, Laurens Manning, Joshua S. Davis

Bibliographic record

VenueANZ Journal of Surgery · 2024
Typearticle
Languageen
FieldMedicine
TopicOrthopedic Infections and Treatments
Canadian institutionsHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsJoint (building)Clinical equipoisePsychologyMedicineIntensive care medicineEngineeringPathologyClinical trial

Abstract

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Hip or knee replacement is complicated by deep infection (Prosthetic joint infection [PJI]) in 1%–2% of cases, causing morbidity, high healthcare costs, and productivity loss.1, 2 In 2020, 112 000 knee and hip replacements were done in Australia.3 Treatment typically requires lengthy antibiotic courses and one or more operations, with variable cure rates (30%–70%, mean 50% after 24 months).4 There is a pressing need to establish more effective surgical and antibiotic strategies. In a previous survey, we identified key clinician priorities for future randomized controlled trials (RCTs) of PJI.5 A critical obstacle in researching PJI treatments is the lack of equipoise among experts. Hence, we designed a survey to assess current practices and equipoise in PJI treatment among physicians and surgeons, aiming to identify variations and inform future RCTs. The survey consisted of four clinical scenarios and 25 questions (Table S1) and was distributed to infectious diseases physicians and/or clinical microbiologists (IDP/CM) and orthopaedic surgeons via mailing lists of Australasian Society of Infectious Diseases (n = 800), and arthroplasty societies in Australia (n = 127), UK (n = 162) and Canada (n = 130) in July 2022. Respondents who managed fewer than 10 PJIs annually were excluded. A total of 294 clinicians responded. After excluding 14% for managing fewer than 10 cases yearly, 68% were IDP/CM and 31% orthopaedic surgeons (Table S2). Overall, 45% worked in Australia, 20% in the UK, 10% in Canada, 10% in the USA, 7% in NZ, and the remaining 9% in other countries. The preferred surgical strategy for the management of late acute PJI was DAIR (67%), followed by two-stage (28%) and one-stage revision arthroplasty (5%) (Table S2). This differed depending on the specialty of the respondent: 85% of orthopaedic surgeons preferred DAIR as compared to 59% of the IDP/CM, and 36% of the IDP/CM preferred two-stage revision versus 9% of orthopaedic surgeons (P < 0.001 for both). DAIR remained the most preferred method for late acute PJI by Australian IDP/CM (61%), but a greater proportion of Australian respondents preferred two-stage revision as compared to their peers from the UK (35% vs. 7%, respectively, P = 0.07). For the chronic PJI scenario, the most preferred surgical intervention strategy was two-stage revision arthroplasty (62%), followed by one-stage revision arthroplasty (22%), ‘Kiwi procedure’ (a loosely cemented one-stage leaving the possibility open for a second stage in the future – 10%), and DAIR (2%). The proportion of IDP/CM who preferred two-stage revision (73%) was higher than that of orthopaedic surgeons (41%) (Table S3). Opinions of surgeons varied by country: 68% of Australian surgeons preferred two-stage revision as compared to 15% of UK surgeons (P = 0.001). Similarly, 81% of Australian IDP/CM preferred two-stage revision as compared to 29% of the UK IDP/CM (P = 0.001). For early post-operative staphylococcal PJI managed with DAIR, 36% preferred to switch to oral antibiotics after 6 weeks of IV antibiotics, 33% after 2 weeks of IV antibiotics, and 27% opted to switch at the time of clinical stability. Canada was the country with the highest proportion of respondents who preferred 6 weeks of IV prior to oral switch (70%), followed by the USA (63%) and Australia (42%). For a similar clinical scenario, 67% of the respondents would include rifampicin as part of the antibiotic treatment regimen (74% of IDP/CM and 56% of orthopaedic surgeons). ‘Other countries’ was the group with the highest proportion of respondents recommending rifampicin (93%), versus 60% of Australian respondents. Regarding the preferred antibiotic duration post second stage, 42%, 27% and 14% of the respondents stated that they would continue antibiotics for 72 h, 7 days and 12 weeks, respectively. A greater proportion of the orthopaedic surgeons continued antibiotics for 12 weeks as compared to IDP/CM (22% vs. 9%). Regarding equipoise for future randomized trials, the majority (79%) were comfortable assigning late acute PJI to either DAIR or revision (Table S3). Similarly, 91% were comfortable assigning chronic PJI to an initial management strategy of one versus two-stage revision arthroplasty as part of an approved RCT, and 86% were comfortable assigning acute staphylococcal PJI treated with DAIR to regimens with or without rifampicin. Regarding the antibiotic treatment duration after second stage for chronic PJI, 84% would be comfortable assigning the patient to either of the ≤7 days or 12 weeks of antibiotic treatment arms. This study revealed significant variation in PJI treatment practices across countries and specialties, yet a majority supported randomizing PJI patients in RCTs to identify optimal management strategies. Practice variation in PJI treatment was more evident in antibiotic use than in surgical approaches, despite OVIVA and PIANOFORTE trials.6, 7 While these studies suggested early oral switch could be effective, two-thirds of respondents still preferred a minimum two-week course of IV antibiotics for acute staphylococcal PJI treated with DAIR. The reasons behind the low uptake of trial results are not elaborated in this study, but previous work has shown there is a 15–20 year gap between the publication of high quality studies and change in clinical practice.8 The use of rifampicin in PJI treatments varied globally, with European respondents often considering it standard despite conflicting evidence from previous research.9, 10 IDP/CM generally advocated for more aggressive surgical methods, such as two-stage revisions for PJI, unlike orthopaedic surgeons who prefer single-stage revisions, reflecting variations influenced by regional practices, notably in the UK where two-stage revisions are less common.11 Most respondents were open to randomizing late acute PJI patients to DAIR or revision surgery but emphasized the need for clear DAIR definitions in RCTs. In summary, there is large practice variation in PJI management, and strong equipoise for the conduct of RCTs addressing surgical and antibiotic management strategies. We thank respondents of this survey for their time. Open access publishing facilitated by The University of Newcastle, as part of the Wiley - The University of Newcastle agreement via the Council of Australian University Librarians. Burcu Isler: Formal analysis; writing – original draft; writing – review and editing. Natalie Niessen: Data curation; formal analysis; methodology; project administration; writing – review and editing. David Campbell: Data curation; writing – review and editing. Andrew D. Toms: Data curation; writing – review and editing. Nick Daneman: Data curation; writing – review and editing. Laurens Manning: Conceptualization; data curation; investigation; methodology; supervision; writing – review and editing. Joshua S. Davis: Conceptualization; data curation; formal analysis; investigation; methodology; supervision; writing – original draft; writing – review and editing. Table S1. Summary of the clinical scenarios included in the survey and associated questions Table S2. Characteristics and responses of survey participants, n (%) Table S3. Equipoise of respondents regarding proposed RCTs (n [%]) Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.041
Threshold uncertainty score0.183

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.171
GPT teacher head0.396
Teacher spread0.225 · 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.

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
Published2024
Admission routes2
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