91 Transforming preterm oral feeding with innovative algorithms: Insights from a quality improvement initiative
Notice bibliographique
Résumé
Abstract Background Establishing safe, efficient oral feeds for preterm infants is a crucial yet challenging milestone before NICU discharge. This process is often accompanied by heightened stress and anxiety among NICU staff and parents, as nonlinear feeding progression and a lack of standardized protocols add uncertainty. Without a structured approach, feeding practices rely heavily on individual experience, leading to inconsistent care and increased caregiver stress. This quality improvement initiative at Sunnybrook Health Sciences Centre, a tertiary perinatal care unit in Toronto, Ontario, was conducted to address these challenges and to improve the transfer of knowledge around preterm oral feeding practices. Improved feeding practices lead to reduced anxiety, smoother transitions to home care, and better developmental outcomes for preterm infants. Objectives The primary aim was to create a structured, evidence-based approach to oral feeding that is adaptable to both breast and bottle feeding, reduces variability, and ensures consistent practices across all caregivers. This structured approach not only promotes safer and more effective feeding but also supports caregivers, including parents, by providing clear, actionable guidance. Design/Methods The COVID-19 pandemic highlighted a pressing need for knowledge transfer in feeding practices, as high staff turnover introduced variability in the NICU. In 2020, the multidisciplinary Sunnybrook Feeding Committee was established, comprising physicians, nurses, nurse practitioners, occupational therapists, and dietitians. Initial staff training used Supporting Oral Feeding in Fragile Infants (SOFFI®) modules to create a consistent understanding of feeding principles. With guidance from the SOFFI® creator (Consultant), our committee adapted these modules to develop two specific, tailored algorithms: the Oral Feeding Readiness Algorithm and the Oral Feeding Challenges Algorithm. Approximately 80 NICU staff members participated in surveys and focus groups, offering qualitative feedback on the algorithms' impact. Data indicated that caregivers experienced a significant reduction in stress due to the clear, consistent framework provided by the algorithms. The algorithms introduced a reliable YES/NO decision flow, enabling caregivers to respond confidently to infant cues and adjust feeding practices accordingly. Results The algorithms demonstrated notable improvements in caregiver confidence, communication, and consistency in feeding practices. Staff surveys revealed that the structured protocols reduced variability clarified feeding readiness and challenged decision-making. The algorithms ensured a universal language and framework for all care providers, reducing subjective variations and providing clear guidance for safe, supportive feeding practices that align with each infant's cues. These changes reduced stress and anxiety for both staff and parents, creating a more cohesive NICU environment focused on supporting infant well-being. Conclusion This initiative represents a broader effort to transform oral feeding practices in the NICU. This structured oral feeding approach provides caregivers with tools for confident, informed oral feeding that aligns with infant cues, thereby reducing stress, facilitating a smoother transition home and enhancing outcomes for preterm infants. Moving forward, we will focus on refining the algorithms, adapting them for parental use, and developing a comprehensive program for families to support safe and consistent feeding practices.
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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 | 0,099 | 0,104 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,003 | 0,004 |
| Études des sciences et des technologies | 0,005 | 0,006 |
| Communication savante | 0,012 | 0,005 |
| Science ouverte | 0,004 | 0,012 |
| Intégrité de la recherche | 0,002 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 source (Gemma direct ou Codex distillé), 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 ».