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Validation of a clinical decision-support algorithm for chronic wound classification and treatment: an expert consensus

2026-06-01Artigo de investigação Acesso aberto
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dc.contributor.authorMarques, Raquel
dc.contributor.authorPais-Vieira, Carla
dc.contributor.authorLopes, Marcos
dc.contributor.authorNeves-Amado, João
dc.contributor.authorAlves, Paulo
dc.date.accessioned2026-06-16T15:28:48Z
dc.date.available2026-06-16T15:28:48Z
dc.date.issued2026-06-01
dc.description.abstractAccurate chronic wound classification is essential for appropriate management, yet diagnostic variability persists in routine practice. Transparent, rule-based decision-support tools may improve standardisation but require validation against expert judgement under clearly defined conditions. To evaluate inter-expert agreement, agreement between a rule-based algorithm and an expert-consensus reference standard, diagnostic accuracy as a complementary measure, exploratory comparison with a non-expert nurse, and expert agreement with algorithm-generated therapeutic recommendations. Thirty anonymised standardised clinical cases were classified by the algorithm and one non-expert nurse. Thirty wound-care experts, including 26 nurses, three physicians, and one researcher, were organised into six independent panels of five and classified case subsets, yielding 150 ratings. A consensus reference diagnosis was defined a priori as agreement by at least 3/5 experts. The primary outcome was algorithm–consensus agreement using Cohen's κ. Expert reliability was assessed using Krippendorff's α and Fleiss' κ. Recommendation agreement was dichotomised and analysed exploratorily. Expert agreement was low to moderate (Krippendorff's α = 0.26–0.60), highest for pressure ulcers/injuries and venous leg ulcers, and lowest for mixed or unknown leg ulcers and diabetic foot ulcers. Consensus was reached in 29 of 30 cases. The algorithm achieved 86.2% accuracy (25/29) and substantial agreement (κ = 0.70, 95% CI 0.46–0.94). Nurse accuracy was 72.4% (21/29, p = 0.219). Experts endorsed 85.2% of therapeutic recommendations. The algorithm showed promising agreement under controlled conditions, supporting further prospective validation in larger, balanced real-world datasets.eng
dc.identifier.doi10.1111/iwj.70952
dc.identifier.eid105041235151
dc.identifier.other53b3e52b-7bfa-4d97-8ae9-a27b30e946a7
dc.identifier.pmcPMC13249526
dc.identifier.pmid42265049
dc.identifier.urihttp://hdl.handle.net/10400.14/58133
dc.identifier.wos001788172200001
dc.language.isoeng
dc.peerreviewedyes
dc.publisherJohn Wiley and Sons Inc.
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/
dc.subjectClinical decision support systemeng
dc.subjectConsensuseng
dc.subjectDiagnosiseng
dc.subjectObserver variationeng
dc.subjectWounds and injurieseng
dc.titleValidation of a clinical decision-support algorithm for chronic wound classification and treatment: an expert consensus
dc.typeresearch article
dspace.entity.typePublication
oaire.citation.issue6
oaire.citation.volume23
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85

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