Evidence map›Paper›PMID 35284926›Full record

ArticleAge and ageing2022

A clinical tool to identify older women with back pain at high risk of osteoporotic vertebral fractures (Vfrac): a population-based cohort study with exploratory economic evaluation.

Tarnjit K Khera, Linda P Hunt, Sarah Davis, Rachael Gooberman-Hill, Howard Thom, Yixin Xu, Zoe Paskins, Tim J Peters, Jon H Tobias, Emma M Clark

Abstract read
In one paragraph

Article in Age and ageing, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
–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

9 citing papers in PubMed.

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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

10 authors.

Tarnjit K KheraTranslational Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
Linda P HuntTranslational Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
Sarah DavisSchool of Health & Related Research, University of Sheffield, Sheffield, UK.
Rachael Gooberman-HillNIHR Bristol Biomedical Research Centre, Bristol, UK.
Howard ThomPopulation Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
Yixin XuPopulation Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
Zoe PaskinsSchool of Medicine, Keele University, Staffordshire, UK.
Tim J PetersTranslational Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
Jon H TobiasTranslational Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
Emma M ClarkTranslational Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.

Funding

Arthritis Research UKDepartment of Health CS-2018-18-ST2-010Medical Research Council MR/S036709/1
6 · The paper itself

Abstract

backgroundosteoporotic vertebral fractures (OVFs) identify people at high risk of future fractures, but despite this, less than a third come to clinical attention. The objective of this study was to develop a clinical tool to aid health care professionals decide which older women with back pain should have a spinal radiograph.

methodsa population-based cohort of 1,635 women aged 65+ years with self-reported back pain in the previous 4 months were recruited from primary care. Exposure data were collected through self-completion questionnaires and physical examination, including descriptions of back pain and traditional risk factors for osteoporosis. Outcome was the presence/absence of OVFs on spinal radiographs. Logistic regression models identified independent predictors of OVFs, with the area under the (receiver operating) curve calculated for the final model, and a cut-point was identified.

resultsmean age was 73.9 years and 209 (12.8%) had OVFs. The final Vfrac model comprised 15 predictors of OVF, with an AUC of 0.802 (95% CI: 0.764-0.840). Sensitivity was 72.4% and specificity was 72.9%. Vfrac identified 93% of those with more than one OVF and two-thirds of those with one OVF. Performance was enhanced by inclusion of self-reported back pain descriptors, removal of which reduced AUC to 0.742 (95% CI: 0.696-0.788) and sensitivity to 66.5%. Health economic modelling to support a future trial was favourable.

conclusionsthe Vfrac clinical tool appears to be valid and is improved by the addition of self-reported back pain symptoms. The tool now requires testing to establish real-world clinical and cost-effectiveness.

Indexed as

Osteoporotic FracturesSpinal FracturesAgedBack PainCohort StudiesCost-Benefit AnalysisFemaleHumansback paincohort studyolder peopleOsteoporosisvertebral fracturesVfrac

Identifiers

PMID35284926
PMCPMC8918203

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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.