Care home staff interventions to optimise pain assessment and management in people with advanced dementia in long-term care settings: a systematic review
Notice bibliographique
Résumé
Abstract Introduction As a person’s dementia symptoms worsen, they become increasingly frail and dependent on others for support,[1] which often necessitates moving into long-term care (LTC). Pain is often poorly recognised and undertreated in people with advanced dementia.[2] Aim To identify and evaluate the effectiveness of care home staff interventions involving pain assessment and management in people with advanced dementia in LTC settings. Methods This systematic review was conducted and reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and registered with the International Prospective Register of Systematic Reviews (PROSPERO; CRD42022355487). Nine databases (Medline, Scopus, The Cochrane Library, EMBASE, Web of Science, International Pharmaceutical Abstracts, PsycINFO, CINAHL, World Health Organisation International Clinical Trials Registry Platform) were searched. The search strategy was developed in Medline and adapted as appropriate for the other databases; it comprised the following search terms: ‘dementia’, ‘Alzheimer’s disease’, ‘pain assessment’, ‘pain management’, ‘pain intervention’, ‘care home’, and ‘nursing home’. The selection of studies was limited to randomised controlled trials (RCTs) in which ≥50% of participants had advanced (moderate-severe) dementia and published in English. Electronic databases were searched from date of inception to May 2023. Two reviewers independently assessed potentially relevant articles for inclusion, completed data extraction and assessed risk of bias using the Cochrane Risk of Bias (RoB) 2.0 tool. A third reviewer was consulted when consensus could not be reached. A narrative analysis was undertaken due to the heterogeneity of outcome measures. Results In total, 3504 articles were identified, with 998 records remaining after duplicate removal. This left 2456 studies for title/abstract screening, and 22 full-text studies were assessed for eligibility; five studies met the inclusion criteria. A total of 1363 participants (mean age 83-88 years) and 123 care homes were included in the studies. The studies reported that these interventions were shown to be effective compared to control groups: the Pain recognition and Treatment (PRT) protocol in China, regular use of the PACSLAC pain assessment tool in Canada, the use of a Comprehensive Observational Pain Management Protocol in Hong Kong, the COSMOS intervention in Norway, and the STA OP stepwise multidisciplinary intervention in the Netherlands. Primary outcomes concerning pain scores measured by PACSLAC (n=2 studies), PAINAD (n=2 studies), and MOBID-2 (n=1 study) showed a statistically significant decrease. Secondary outcomes measured included several clinical outcomes associated with pain, depression, presence of neuropsychiatric symptoms and nursing staff stress. Four of the included studies were judged to be at ‘some concerns of bias’ according to RoB 2.0, due to unclear information in two or more domains (randomisation process, deviations from intended interventions, and measurement of the outcomes); one study was judged as having a ‘low risk of bias’. Conclusion The use of pain assessment tools decreased pain scores in people with advanced dementia in LTC. A limitation of this review is the exclusion of non-English language studies. Further, the clinical and methodological heterogeneity of included studies made a quantitative comparison difficult, thus, the findings are based on narrative analysis. References 1. Alzheimer’s Society. Understanding and supporting a person with dementia. 2023. Available from: https://www.alzheimers.org.uk/get-support/help-dementia-care/understanding-supporting-person-dementia 2. Williams A, Ackroyd R. Identifying and managing pain for patients with advanced dementia. GM: Midlife & Beyond. 2017;47(4):35–8.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».