The EPIQ evidence reviews – practical tools for an integrated approach to knowledge translation
Notice bibliographique
Résumé
In this era of evidence-based medicine, the need for systematic reviews of the literature to facilitate knowledge translation is increasingly more evident (1,2). With vast quantities of new information being constantly published, clinicians with limited available time require comprehensive but succinct summaries of the literature to help guide patient management. Although there are many forms of systematic reviews, perhaps the best known is the Cochrane Collaboration and Library (3), which has become an invaluable and indispensible resource for health care practitioners worldwide. Cochrane reviews include rigorously performed clinical trials and are considered by many to be the ‘gold standard’ for systematic reviews. Members of the Canadian Neonatal Network recently developed the Evidence-based Practice for Improving Quality (EPIQ) method to provide high-quality health care for neonates (4,5). EPIQ is an evidence-based multidimensional approach to quality improvement aimed at changing organizational culture and sustaining behavioural change. It builds on traditional continuous quality improvement techniques by selectively targeting hospital-specific practices for intervention, thus, reducing the reliance on intuition and anecdotes that are associated with existing quality improvement methods. EPIQ is based on three pillars: use of evidence from the published literature; use of data from participating hospitals to target hospital-specific practices for intervention; and use of a national network to share expertise (5). To evaluate evidence from the published literature (Pillar 1), EPIQ used the International Liaison Committee on Resuscitation (ILCOR) approach (6). The ILCOR approach differs from the Cochrane approach because it adopts a broader systematic approach to the evaluation of evidence and evaluates all research related to a specific question, whether from clinical trials, observational data or animal studies. A worksheet is used to summarize the information from multiple sources according to level of evidence (Table 1) and their direction (supporting, opposing or neutral to the question) (7). This process significantly reduces the time required to conduct a critical review and negates the need for previous training in quantitative methods such as meta-analysis. The worksheet conclusions, the consensus on science and the treatment recommendations can be debated by experts with all the available evidence before them. Thus, the ILCOR approach complements the Cochrane method by including evidence from observational sources, simplifying the review process, generating consensus recommendations based on the best available evidence, and providing a pragmatic approach to guide the busy clinician in making clinical decisions. Levels of evidence (LOE) for therapeutic interventions Data adapted with permission from reference 9. RCTs Randomized controlled trials Levels of evidence (LOE) for therapeutic interventions Data adapted with permission from reference 9. RCTs Randomized controlled trials To meet the objectives of Pillar 2, coded observational data regarding practices and outcomes were collected from Canadian neonatal intensive care units on an ongoing basis to form the Canadian Neonatal Network Database. Data from this database were complemented by additional specific targeted data, and were used to provide information on practices and interventions that result in greater quality of care. Through cluster randomization experiments, clinical hypotheses could then be developed and tested in the clinical setting. Information from Pillars 1 and 2 were combined to develop practice change strategies. Finally, in Pillar 3, neonatal intensive care units collaborated to share their experiences with practice change strategies and their outcomes, and to encourage one another. In a cluster randomized controlled trial of Canadian neonatal intensive care units, EPIQ reduced bronchopulmonary dysplasia and nosocomial infection by 15% and 44%, respectively, although the latter did not reach statistical significance (5). The accompanying review on continuous positive airway pressure by Yee et al (8) (pages 633–637) is an example of how an EPIQ evidence review can be conducted and presented in a clear and succinct way. The authors concluded that although the existing literature does not provide conclusive evidence for use of continuous positive airway pressure in preterm infants, the available literature does suggest that there is sufficient evidence to support its use in certain circumstances, and they make recommendations for its use accordingly. Reviews of this nature provide clear, succinct and pragmatic guidance based on the best available evidence, which is what clinicians really need. It is, of course, necessary for the evidence to be periodically reviewed. In this regard, the evidence review worksheet proves extremely valuable because it can be readily updated when new information becomes available, and the conclusions and recommendations can be quickly revised and disseminated. Thus, unlike more academic models for evidence review, the EPIQ evidence review can provide a powerful, yet practical tool for knowledge translation and dissemination to the vast majority of health care practitioners, and is especially suitable for use in quality improvement. As part of a nationwide effort to improve quality of care in the neonatal intensive care unit, the Canadian Neonatal Network has established 30 evidence review teams comprising neonatologists, fellows, neonatal nurse practitioners, respiratory therapists and nurses at neonatal intensive care units across Canada. The 30 teams attended a national workshop in Barrie, Ontario, in November 2008, where they received training in the EPIQ evidence review method and met to identify current neonatal intensive care unit practice issues that could benefit from evidence reviews. They then reviewed the published evidence as part of an EPIQ quality improvement process. Each team was assigned specific topics for evidence review, and was given tasks and timelines. They defined the questions to be asked and the proposed review methodology, and submitted them to a central EPIQ Evidence Review Panel. Following approval by the panel, each team completed their assigned evidence reviews, which were then peer reviewed by selected members from other review teams. Practice recommendations were arrived at by debate and consensus using a peer group of clinicians. The completed evidence reviews will be submitted to Paediatrics & Child Health for peer review and publication. Summaries of the reviews will be published in the Journal, and the full reviews will be available from the EPIQ Library of Evidence Reviews on the public access EPIQ website at www.EPIQ.ca under ‘Evidence Reviews’. A training video is available on the same website for those who wish to learn more about how to conduct EPIQ evidence reviews. EPIQ evidence reviews have tremendous potential to contribute toward knowledge translation and evidence-based health care, and the method can be applied in all areas of health care. The authors thank the staff of the Canadian Neonatal Network EPIQ Study Coordinating Centre (Charles Ah Knit, Sukhy Mahl and Phillip Ye) for their tireless and diligent work.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,350 | 0,633 |
| Méta-épidémiologie (sens strict) | 0,005 | 0,006 |
| Méta-épidémiologie (sens large) | 0,014 | 0,010 |
| Bibliométrie | 0,035 | 0,036 |
| Études des sciences et des technologies | 0,002 | 0,005 |
| Communication savante | 0,018 | 0,021 |
| Science ouverte | 0,007 | 0,020 |
| Intégrité de la recherche | 0,010 | 0,013 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,055 | 0,018 |
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; l’étiquette directe de Gemma et le classifieur distillé Codex s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».