Evidence map›Paper›PMID 30175484›Full record

ArticleInternational wound journal2019

A risk model for the prediction of skin tears in aged care residents: A prospective cohort study.

Robyn Rayner, Keryln Carville, Gavin Leslie, Satvinder S Dhaliwal

Abstract read
In one paragraph

Article in International wound journal, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 2 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

10 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Sampling the skin surface chemistry for diagnosis and prognosis.Wound repair and regeneration : official publication of the Wound Healing Society [and] the European Tissue Repair Society · 2022
    Pooled it
  3. Article
  4. Article
  5. Article
  6. Article
  7. Models for predicting skin tears: A comparison.International wound journal · 2020
    Article
  8. Observational
  9. Article
  10. 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

4 authors.

Robyn RaynerSchool of Nursing, Midwifery and Paramedicine, Curtin University, Perth, Western Australia, Australia.
Keryln CarvilleSchool of Nursing, Midwifery and Paramedicine, Curtin University, Perth, Western Australia, Australia.
Gavin LeslieSchool of Nursing, Midwifery and Paramedicine, Curtin University, Perth, Western Australia, Australia.
Satvinder S DhaliwalSchool of Public Health, Curtin University, Perth, Western Australia, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The objective of this study was to construct a predictive model to identify aged care residents at risk of future skin tears. Extensive data about individual characteristics, skin characteristics, and skin properties were gathered from 173 participants at baseline and at 6 months. A predictive model, developed using multivariable logistic regression, identified five variables that significantly predicted the risk of skin tear at 6 months. These included: a history of skin tears in the previous 12 months (OR 3.82 [1.64-8.90], P = 0.002), purpura ≤20 mm in size (OR 3.64 [1.42-9.35], P = 0.007), a history of falls in the previous 3 months (OR 3.37 [1.54-7.41], P = 0.002), clinical manifestations of elastosis (OR 3.19 [1.38-7.38], P = 0.007), and male gender (OR 3.08 [1.22-7.77], P = 0.017). The predictive model yielded an area under the receiver operating characteristic curve of 0.854 with an 81.7% sensitivity and an 81.4% specificity. This predictive model could inform a simple but promising bedside tool for identifying older individuals at risk of skin tears.

Indexed as

AgedAged, 80 and overCohort StudiesFemaleFollow-Up StudiesHumansLacerationsLogistic ModelsMaleMiddle AgedOdds RatioProportional Hazards ModelsProspective StudiesRisk AssessmentRisk FactorsSkinageing skinclinical skin manifestationscohort studypredictive modelskin tears

Identifiers

PMID30175484
PMCPMC7948554

What OpenQuestion holds

Textmetadata
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.