Clinical Decision Support Tools and a Standardized Order Set Enhances Early Enteral Nutrition in Critically Ill Children. (1st April 2019)

• Record Type: Journal Article
• Authors: Ziemba, Keegan J.; Kumar, Rajeswari; Nuss, Kathryn; Estrada, Maria; Lin, Ada; Ayad, Onsy
• Is Part Of: Nutrition in clinical practice. Volume 34:Number 6(2019)
• Page Start: 916
• Page End: 921
• Publication Date: 2019-04-01
• Subjects: child -- critical illness -- electronic health records -- enteral nutrition -- nutrition support -- pediatric intensive care unit -- pediatrics -- quality improvement; Nutrition -- Periodicals; Diet therapy -- Periodicals; Artificial feeding -- Periodicals; 615.854
• DOI: 10.1002/ncp.10272
• Languages: English
• ISSNs: 0884-5336

Abstract

Abstract: Background: Critically ill children in a pediatric intensive care unit (PICU) have unique nutrition needs that are challenging to achieve and thus are at high risk of malnutrition. There is increasing evidence that children who reach caloric goals early have improved outcomes. The purpose of this initiative was to implement an enteral nutrition (EN) algorithm in a tertiary care PICU utilizing clinical decision support tools (CDSTs) and a standardized order set within an electronic health record. Methods: A quality improvement initiative was undertaken to implement an EN feeding protocol using electronic CDSTs, including a new standardized order set. Results: In a historical cohort of 376 patients, only 18% met goal EN in the first 48 hours of admission. The EN protocol was implemented in 272 patients who met 88% goal feed volume within 48 hours of intensive care unit admission. Median time to start EN (1.7 vs 1.3 days, P < 0.0001) and time to goal nutrition (2.8 vs 2.2 days, P < 0.001) improved after project implementation. Length of stay in the PICU was significantly reduced following protocol implementation (202 hours pre‐implementation vs 156 hours post implementation, P < 0.0001). Conclusions: We used CDSTs and standardized order sets to implement a nutrition algorithm to facilitate and likely improve the nutrition care of critically ill children.