Evidence map›Paper›PMID 35948989›Full record

ArticleSystematic reviews2022

Evaluating methodological quality of prognostic prediction models on patient reported outcome measurements after total hip replacement and total knee replacement surgery: a systematic review protocol.

Wei-Ju Chang, Justine Naylor, Pragadesh Natarajan, Victor Liu, Sam Adie

Open access · goldAbstract read
In one paragraph

Article in Systematic reviews, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 2 pooled it
1.1field-weighted citation impact, top 23% of its field
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, 2 syntheses or guidelines pooled it, 7 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  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

5 authors at 3 institutions in 1 country.

Wei-Ju ChangCentre for Pain IMPACT, Neuroscience Research Australia (NeuRA), 139 Barker St, Randwick, NSW, 2031, Australia. w.chang@neura.edu.au.ORCID 0000-0003-0524-4883
Justine NaylorSchool of Clinical Medicine, UNSW Medicine & Health, South West Clinical Campuses, Discipline of Surgery, Faculty of Medicine and Health, UNSW, Sydney, NSW, Australia.
Pragadesh NatarajanSt George and Sutherland Clinical School, University of New South Wales, Clinical Sciences (WRPitney) Building, Short Street, St George Hospital, Kogarah, NSW, 2217, Australia.
Victor LiuSt George and Sutherland Clinical School, University of New South Wales, Clinical Sciences (WRPitney) Building, Short Street, St George Hospital, Kogarah, NSW, 2217, Australia.
Sam AdieSt George and Sutherland Clinical School, University of New South Wales, Clinical Sciences (WRPitney) Building, Short Street, St George Hospital, Kogarah, NSW, 2217, Australia.
UNSW Sydney · AUSutherland Hospital · AUUniversity of Newcastle Australia · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPrediction models for poor patient-reported surgical outcomes after total hip replacement (THR) and total knee replacement (TKR) may provide a method for improving appropriate surgical care for hip and knee osteoarthritis. There are concerns about methodological issues and the risk of bias of studies producing prediction models. A critical evaluation of the methodological quality of prediction modelling studies in THR and TKR is needed to ensure their clinical usefulness. This systematic review aims to (1) evaluate and report the quality of risk stratification and prediction modelling studies that predict patient-reported outcomes after THR and TKR; (2) identify areas of methodological deficit and provide recommendations for future research; and (3) synthesise the evidence on prediction models associated with post-operative patient-reported outcomes after THR and TKR surgeries.

methodsMEDLINE, EMBASE, and CINAHL electronic databases will be searched to identify relevant studies. Title and abstract and full-text screening will be performed by two independent reviewers. We will include (1) prediction model development studies without external validation; (2) prediction model development studies with external validation of independent data; (3) external model validation studies; and (4) studies updating a previously developed prediction model. Data extraction spreadsheets will be developed based on the CHARMS checklist and TRIPOD statement and piloted on two relevant studies. Study quality and risk of bias will be assessed using the PROBAST tool. Prediction models will be summarised qualitatively. Meta-analyses on the predictive performance of included models will be conducted if appropriate. A narrative review will be used to synthesis the evidence if there are insufficient data to perform meta-analyses. DISCUSSION: This systematic review will evaluate the methodological quality and usefulness of prediction models for poor outcomes after THR or TKR. This information is essential to provide evidence-based healthcare for end-stage hip and knee osteoarthritis. Findings of this review will contribute to the identification of key areas for improvement in conducting prognostic research in this field and facilitate the progress in evidence-based tailored treatments for hip and knee osteoarthritis. SYSTEMATIC REVIEW REGISTRATION: PROSPERO registration number CRD42021271828.

Indexed as

Arthroplasty, Replacement, HipArthroplasty, Replacement, KneeOsteoarthritis, HipOsteoarthritis, KneeHumansPatient Reported Outcome MeasuresPrognosisSystematic Reviews as TopicMeta-analysisPrediction modelSystematic reviewTotal hip replacementTotal knee replacement

Identifiers

PMID35948989
PMCPMC9364604
OpenAlexW4291020104

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.