Clinimetric properties of the electronic Pain Assessment Tool (ePAT) for aged-care residents with moderate to severe dementia
Received 4 December 2017
Accepted for publication 20 February 2018
Published 1 June 2018 Volume 2018:11 Pages 1037—1044
Checked for plagiarism Yes
Review by Single-blind
Peer reviewers approved by Dr Colin Mak
Peer reviewer comments 2
Editor who approved publication: Dr Erica Wegrzyn
Kreshnik Hoti,1,2 Mustafa Atee,1 Jeffery D Hughes1
1School of Pharmacy, Curtin University, Perth, Australia; 2Division of Pharmacy, Faculty of Medicine, University of Prishtina, Pristina, Kosovo
Purpose: Accurate 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 clinimetric properties (clinical utility and predictive validity) of the ePAT in this population group.
Methods: Data 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.
Results: A 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+ 0.936 (95% CI 0.911–0.960), CUI– 0.801 (95% CI 0.748–0.854).
Conclusion: The clinimetric properties demonstrated were excellent, thus supporting the clinical usefulness of the ePAT when identifying pain in patients with moderate–severe dementia.
Keywords: ePAT, PainChekTM, pain assessment, dementia, predictive validity, clinical utility, automated facial analysis
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