Evidence map›Paper›PMID 35668349›Full record

ArticleBMC medical research methodology2022

A comparison of survival models for prediction of eight-year revision risk following total knee and hip arthroplasty.

Alana R Cuthbert, Lynne C Giles, Gary Glonek, Lisa M Kalisch Ellett, Nicole L Pratt

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC medical research methodology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
2.0field-weighted citation impact, top 14% 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

5 citing papers in PubMed, 1 synthesis or guideline pooled it, 11 citations in OpenAlex.

  1. Optimal inputs for machine learning models in predicting total joint arthroplasty outcomes: a systematic review.European journal of orthopaedic surgery & traumatology : orthopedie traumatologie · 2024
    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

5 authors at 2 institutions in 1 country.

Alana R CuthbertQuality Use of Medicines and Pharmacy Research Centre, Clinical and Health Sciences, University of South Australia, PO Box 11060, Adelaide, SA, 5001, Australia. alana.cuthbert@sahmri.com.
Lynne C GilesSchool of Public Health, The University of Adelaide, Adelaide, SA, 5005, Australia.
Gary GlonekSchool of Mathematical Sciences, The University of Adelaide, Adelaide, SA, 5005, Australia.
Lisa M Kalisch EllettQuality Use of Medicines and Pharmacy Research Centre, Clinical and Health Sciences, University of South Australia, PO Box 11060, Adelaide, SA, 5001, Australia.
Nicole L PrattQuality Use of Medicines and Pharmacy Research Centre, Clinical and Health Sciences, University of South Australia, PO Box 11060, Adelaide, SA, 5001, Australia.
University of Adelaide · AUUniversity of South Australia · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThere is increasing interest in the development and use of clinical prediction models, but a lack of evidence-supported guidance on the merits of different modelling approaches. This is especially true for time-to-event outcomes, where limited studies have compared the vast number of modelling approaches available. This study compares prediction accuracy and variable importance measures for four modelling approaches in prediction of time-to-revision surgery following total knee arthroplasty (TKA) and total hip arthroplasty (THA).

methodsThe study included 321,945 TKA and 151,113 THA procedures performed between 1 January 2003 and 31 December 2017. Accuracy of the Cox model, Weibull parametric model, flexible parametric model, and random survival forest were compared, with patient age, sex, comorbidities, and prosthesis characteristics considered as predictors. Prediction accuracy was assessed using the Index of Prediction Accuracy (IPA), c-index, and smoothed calibration curves. Variable importance rankings from the Cox model and random survival forest were also compared.

resultsOverall, the Cox and flexible parametric survival models performed best for prediction of both TKA (integrated IPA 0.056 (95% CI [0.054, 0.057]) compared to 0.054 (95% CI [0.053, 0.056]) for the Weibull parametric model), and THA revision. (0.029 95% CI [0.027, 0.030] compared to 0.027 (95% CI [0.025, 0.028]) for the random survival forest). The c-index showed broadly similar discrimination between all modelling approaches. Models were generally well calibrated, but random survival forest underfitted the predicted risk of TKA revision compared to regression approaches. The most important predictors of revision were similar in the Cox model and random survival forest for TKA (age, opioid use, and patella resurfacing) and THA (femoral cement, depression, and opioid use).

conclusionThe Cox and flexible parametric models had superior overall performance, although all approaches performed similarly. Notably, this study showed no benefit of a tuned random survival forest over regression models in this setting.

Indexed as

Arthroplasty, Replacement, HipArthroplasty, Replacement, KneeAnalgesics, OpioidHumansProportional Hazards ModelsReoperationAnalgesics, OpioidFlexible parametric survival modelHip replacementKnee replacementMachine learningParametric survival modelPrediction modelRandom survival forestTime-to-event data

Identifiers

PMID35668349
PMCPMC9172144
OpenAlexW4281637675

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.