Identification of nutritional risk in the acute care setting: progress towards a practice and evidence informed systems level approach
Abstract Background To improve nutritional assessment and care pathways in the acute care setting, it is important to understand the indicators that may predict nutritional risk. Informed by a review of systematic reviews, this project engaged stakeholders to prioritise and reach consensus on a list...
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| Autores principales: | , , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
BMC
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/37842897a84e42b889e50129f9f4afd2 |
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| Sumario: | Abstract Background To improve nutritional assessment and care pathways in the acute care setting, it is important to understand the indicators that may predict nutritional risk. Informed by a review of systematic reviews, this project engaged stakeholders to prioritise and reach consensus on a list of evidence based and clinically contextualised indicators for identifying malnutrition risk in the acute care setting. Methods A modified Delphi approach was employed which consisted of four rounds of consultation with 54 stakeholders and 10 experts to reach consensus and refine a list of 57 risk indicators identified from a review of systematic reviews. Weighted mean and variance scores for each indicator were evaluated. Consistency was tested with intra class correlation coefficient. Cronbach's alpha was used to determine the reliability of the indicators. The final list of indicators was subject to Cronbach’s alpha and exploratory principal component analysis. Results Fifteen indicators were considered to be the most important in identifying nutritional risk. These included difficulty self-feeding, polypharmacy, surgery and impaired gastro-intestinal function. There was 82% agreement for the final 15 indicators that they collectively would predict malnutrition risk in hospital inpatients. Conclusion The 15 indicators identified are supported by evidence and are clinically informed. This represents an opportunity for translation into a novel and automated systems level approach for identifying malnutrition risk in the acute care setting. |
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