Evidence map›Paper›PMID 36047015›Full record

ArticleThe bone & joint journal2022

Estimating incidence rates of periprosthetic joint infection after hip and knee arthroplasty for osteoarthritis using linked registry and administrative health data.

Xingzhong Jin, Blanca Gallego Luxan, Mark Hanly, Nicole L Pratt, Ian Harris, Richard de Steiger, Stephen E Graves, Louisa Jorm

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in The bone & joint journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
40citing papers in PubMed, 4 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

40 citing papers in PubMed, 4 syntheses or guidelines pooled it.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Xingzhong JinCentre for Big Data Research in Health, University of New South Wales, Sydney, Australia.ORCID 0000-0003-4293-8665
Blanca Gallego LuxanCentre for Big Data Research in Health, University of New South Wales, Sydney, Australia.ORCID 0000-0002-3704-7975
Mark HanlyCentre for Big Data Research in Health, University of New South Wales, Sydney, Australia.ORCID 0000-0002-9279-7453
Nicole L PrattQuality Use of Medicines and Pharmacy Research Centre, Clinical and Health Sciences, University of South Australia, Adelaide, Australia.ORCID 0000-0001-8730-8910
Ian HarrisAustralian Orthopaedic Association National Joint Replacement Registry, South Australian Health and Medical Research Institute, Adelaide, Australia.ORCID 0000-0003-0887-7627
Richard de SteigerAustralian Orthopaedic Association National Joint Replacement Registry, South Australian Health and Medical Research Institute, Adelaide, Australia.ORCID 0000-0002-1276-2040
Stephen E GravesQuality Use of Medicines and Pharmacy Research Centre, Clinical and Health Sciences, University of South Australia, Adelaide, Australia.ORCID 0000-0002-1629-319X
Louisa JormCentre for Big Data Research in Health, University of New South Wales, Sydney, Australia.ORCID 0000-0003-0390-661X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

aimsThe aim of this study was to estimate the 90-day periprosthetic joint infection (PJI) rates following total knee arthroplasty (TKA) and total hip arthroplasty (THA) for osteoarthritis (OA).

methodsThis was a data linkage study using the New South Wales (NSW) Admitted Patient Data Collection (APDC) and the Australian Orthopaedic Association National Joint Replacement Registry (AOANJRR), which collect data from all public and private hospitals in NSW, Australia. Patients who underwent a TKA or THA for OA between 1 January 2002 and 31 December 2017 were included. The main outcome measures were 90-day incidence rates of hospital readmission for: revision arthroplasty for PJI as recorded in the AOANJRR; conservative definition of PJI, defined by T84.5, the PJI diagnosis code in the APDC; and extended definition of PJI, defined by the presence of either T84.5, or combinations of diagnosis and procedure code groups derived from recursive binary partitioning in the APDC.

resultsThe mean 90-day revision rate for infection was 0.1% (0.1% to 0.2%) for TKA and 0.3% (0.1% to 0.5%) for THA. The mean 90-day PJI rates defined by T84.5 were 1.3% (1.1% to 1.7%) for TKA and 1.1% (0.8% to 1.3%) for THA. The mean 90-day PJI rates using the extended definition were 1.9% (1.5% to 2.2%) and 1.5% (1.3% to 1.7%) following TKA and THA, respectively.

conclusionWhen reporting the revision arthroplasty for infection, the AOANJRR substantially underestimates the rate of PJI at 90 days. Using combinations of infection codes and PJI-related surgical procedure codes in linked hospital administrative databases could be an alternative way to monitor PJI rates.Cite this article:

Indexed as

Arthritis, InfectiousArthroplasty, Replacement, HipArthroplasty, Replacement, KneeOsteoarthritisProsthesis-Related InfectionsAustraliaHumansIncidenceRegistriesReoperationRetrospective StudiesArthroplastyarthroplasty registriesAustralian Orthopaedic Association National Joint Replacement Registry (AOANJRR)hip and knee arthroplastyInfectioninfectionsOsteoarthritisosteoarthritis (OA)periprosthetic joint infection (PJI)revision arthroplastiestotal knee and total hip arthroplasty

Identifiers

PMID36047015
PMCPMC9948458

What OpenQuestion holds

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LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.