Evidence map›Paper›PMID 29910632›Full record

ArticleJournal of pain research2018

Clinimetric properties of the electronic Pain Assessment Tool (ePAT) for aged-care residents with moderate to severe dementia.

Kreshnik Hoti, Mustafa Atee, Jeffery D Hughes

Registry-linked trialOpen access · goldAbstract read
In one paragraph

Article in Journal of pain research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06049732 (Psychometric Evaluation of the Electronic Pain Assessment Tool PainChek® Adult in Nursing Homes in the United States), which is not on this map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed
3.6field-weighted citation impact, top 7% 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.

NCT06049732 completednot on this mapstarted 2023, after this paper: background citation

Psychometric Evaluation of the Electronic Pain Assessment Tool PainChek® Adult in Nursing Homes in the United States

TypeobservationalSponsorPainChek LtdRan2023 to 2024Enrolled105ConditionsDementia, PainArmsPainChek®
3 · Its place in the literature

Who cites it

19 citing papers in PubMed, 32 citations in OpenAlex.

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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

3 authors at 1 institution in 2 countries.

Kreshnik HotiSchool of Pharmacy, Curtin University, Perth, Australia.
Mustafa AteeSchool of Pharmacy, Curtin University, Perth, Australia.
Jeffery D HughesSchool of Pharmacy, Curtin University, Perth, Australia.
Curtin University · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeAccurate pain assessment is critical to detect pain and facilitate effective pain management in dementia patients. The electronic Pain Assessment Tool (ePAT) is a point-of-care solution that uses automated facial analysis in conjunction with other clinical indicators to evaluate the presence and intensity of pain in patients with dementia. This study aimed to examine clini-metric properties (clinical utility and predictive validity) of the ePAT in this population group.

methodsData were extracted from a prospective validation (observational) study of the ePAT in dementia patients who were ≥65 years of age, living in a facility for ≥3 months, and had Psychogeriatric Assessment Scales - cognitive scores ≥10. The study was conducted in two residential aged-care facilities in Perth, Western Australia, where residents were sampled using purposive convenience strategy. Predictive validity was measured using accuracy statistics (sensitivity, specificity, positive predictive value, and negative predictive value). Positive and negative clinical utility index (CUI) scores were calculated using Mitchell's formula. Calculations were based on comparison with the Abbey Pain Scale, which was used as a criterion reference.

resultsA total of 400 paired pain assessments for 34 residents (mean age 85.5±6.3 years, range 68.0-93.2 years) with moderate-severe dementia (Psychogeriatric Assessment Scales - cognitive score 11-21) were included in the analysis. Of those, 303 episodes were classified as pain by the ePAT based on a cutoff score of 7. Unadjusted prevalence findings were sensitivity 96.1% (95% CI 93.9%-98.3%), specificity 91.4% (95% CI 85.7%-97.1%), accuracy 95.0% (95% CI 92.9%-97.1%), positive predictive value 97.4% (95% CI 95.6%-99.2%), negative predictive value 87.6% (95% CI 81.1%-94.2%), CUI

conclusionThe clinimetric properties demonstrated were excellent, thus supporting the clinical usefulness of the ePAT when identifying pain in patients with moderate-severe dementia.

Indexed as

automated facial analysisclinical utilitydementiaePATpain assessmentPainChek™predictive validity

Identifiers

PMID29910632
PMCPMC5989701
OpenAlexW2805128904

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Registered trials

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.