Evidence map›Paper›PMID 34979512›Full record

ArticleGerontology2022

A Prediction Model for Falls in Community-Dwelling Older Adults in Podiatry Practices.

Danique J J van Gulick, Sander I B Perry, Marike van der Leeden, Jolan G M van Beek, Cees Lucas, Martijn M Stuiver

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

Article in Gerontology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 pooled it
4.3field-weighted citation impact, top 6% 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

6 citing papers in PubMed, 1 synthesis or guideline pooled it, 10 citations in OpenAlex.

  1. Pooled it
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  3. Article
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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

6 authors at 3 institutions in 1 country.

Danique J J van GulickDepartment of Science and Data-analysis, RondOm Podotherapeuten, Podiatric Primary Care Center, Leusden, The Netherlands.
Sander I B PerryDepartment of Epidemiology and Data Science, University Medical Center Amsterdam, University of Amsterdam, Amsterdam, The Netherlands.
Marike van der LeedenAmsterdam Rehabilitation Research Centre, Reade, Amsterdam, The Netherlands.
Jolan G M van BeekDepartment of Science and Data-analysis, RondOm Podotherapeuten, Podiatric Primary Care Center, Leusden, The Netherlands.
Cees LucasDepartment of Epidemiology and Data Science, University Medical Center Amsterdam, University of Amsterdam, Amsterdam, The Netherlands.
Martijn M StuiverDepartment of Epidemiology and Data Science, University Medical Center Amsterdam, University of Amsterdam, Amsterdam, The Netherlands.
Amsterdam University Medical Centers · NLReade · NLUniversity of Amsterdam · NL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionFalls are a worldwide health problem among community-dwelling older adults. Emerging evidence suggests that foot problems increase the risk of falling, so the podiatrist may be crucial in detecting foot-related fall risk. However, there is no screening tool available which can be used in podiatry practice. The predictive value of existing tools is limited, and the implementation is poor. The development of risk models for specific clinical populations might increase the prediction accuracy and implementation. Therefore, the aim of this study was to develop and internally validate an easily applicable clinical prediction model (CPM) that can be used in podiatry practice to predict falls in community-dwelling older adults with foot (-related) problems.

methodsThis was a prospective study including community-dwelling older adults (≥65 years) visiting podiatry practices. General fall-risk variables, and foot-related and function-related variables were considered as predictors for the occurrence of falls during the 12-month follow-up. Logistic regression analysis was used for model building, and internal validation was done by bootstrap resampling.

results407 participants were analyzed; the event rate was 33.4%. The final model included fall history in the previous year, unsteady while standing and walking, plantarflexor strength of the lesser toes, and gait speed. The area under the receiver operating characteristic curve was 0.71 (95% CI: 0.66-0.76) in the sample and estimated as 0.65 after shrinkage.

conclusionA CPM based on fall history in the previous year, feeling unsteady while standing and walking, decreased plantarflexor strength of the lesser toes, and reduced gait speed has acceptable accuracy to predict falls in our sample of podiatry community-dwelling older adults and is easily applicable in this setting. The accuracy of the model in clinical practice should be demonstrated through external validation of the model in a next study.

Indexed as

Independent LivingPodiatryAgedHumansModels, StatisticalPrognosisProspective StudiesAssessment toolFall riskFootPodiatrist

Identifiers

PMID34979512
PMCPMC9677838
OpenAlexW4206548121

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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.