Evidence map›Paper›PMID 36474948›Full record

ArticleOsteoarthritis and cartilage open2022

Developing and internally validating a prediction model for total knee replacement surgery in patients with osteoarthritis.

Sharmala Thuraisingam, Patty Chondros, Jo-Anne Manski-Nankervis, Tim Spelman, Peter F Choong, Jane Gunn, Michelle M Dowsey

Abstract read
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In one paragraph

Article in Osteoarthritis and cartilage open, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 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

4 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Article
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

7 authors.

Sharmala ThuraisingamDepartment of Surgery, Melbourne Medical School, University of Melbourne, 29 Regent Street, Fitzroy, Victoria, 3065, Australia.
Patty ChondrosDepartment of General Practice, Melbourne Medical School, University of Melbourne, 780 Elizabeth Street, Parkville, Victoria, 3010, Australia.
Jo-Anne Manski-NankervisDepartment of General Practice, Melbourne Medical School, University of Melbourne, 780 Elizabeth Street, Parkville, Victoria, 3010, Australia.
Tim SpelmanDepartment of Surgery, Melbourne Medical School, University of Melbourne, 29 Regent Street, Fitzroy, Victoria, 3065, Australia.
Peter F ChoongDepartment of Surgery, Melbourne Medical School, University of Melbourne, 29 Regent Street, Fitzroy, Victoria, 3065, Australia.
Jane GunnDepartment of General Practice, Melbourne Medical School, University of Melbourne, 780 Elizabeth Street, Parkville, Victoria, 3010, Australia.
Michelle M DowseyDepartment of Surgery, Melbourne Medical School, University of Melbourne, 29 Regent Street, Fitzroy, Victoria, 3065, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: The objective of this study was to develop and internally validate a clinical algorithm for use in general practice that predicts the probability of total knee replacement (TKR) surgery within the next five years for patients with osteoarthritis. The purpose of the model is to encourage early uptake of first-line treatment strategies in patients likely to undergo TKR and to provide a cohort for the development and testing of novel interventions that prevent or delay the progression to TKR. Method: Electronic health records (EHRs) from 201,462 patients with osteoarthritis aged 45 years and over from 483 general practices across Australia were linked with records from the Australian Orthopaedic Association National Joint Replacement Registry and the National Death Index. A Fine and Gray competing risk prediction model was developed using these data to predict the risk of TKR within the next five years. Results: During a follow-up time of 5 years, 15,979 (7.9%) patients underwent TKR and 13,873 (6.9%) died. Predictors included in the final algorithm were age, previous knee replacement, knee surgery (other than TKR), prescribing of osteoarthritis medication in the 12 months prior, comorbidity count and diagnosis of a mental health condition. Optimism corrected model discrimination was 0.67 (95% CI: 0.66 to 0.67) and model calibration acceptable. Conclusion: The model has the potential to reduce some of the economic burden associated with TKR in Australia. External validation and further optimisation of the algorithm will be carried out prior to implementation within Australian general practice EHR systems.

Indexed as

Clinical prediction toolCompeting risk modelDecision support toolElectronic health recordElectronic medical recordTotal knee replacement

Identifiers

PMID36474948
PMCPMC9718161

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Registered trials

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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.