Evaluation of an electronic medical record−based Paediatric Nutrition Screening Tool

Abstract

BackgroundNutrition screening is recommended to identify children at risk for malnutrition. A unique screening tool was developed based on American Society for Parenteral and Enteral Nutrition (ASPEN) recommendations and embedded in the electronic medical record to assess for nutrition risk.MethodsThe components of the tool included the Paediatric Nutrition Screening Tool (PNST) and other elements recommended by ASPEN. To evaluate the screening tool, retrospective data were analysed on all patients admitted to acute care units of Children's Wisconsin in 2019. Data collected included nutrition screen results, diagnosis and nutrition status. All patients who received at least one full nutrition assessment by a registered dietitian (RD) were included in analysis.ResultsOne thousand five hundred seventy‐five patients were included in analysis. The following screen elements were significantly associated with a diagnosis of malnutrition: any positive screen (p < 0.001), >2 food allergies (p = 0.009), intubation (p < 0.001), parenteral nutrition (p = 0.005), RD‐identified risk (p < 0.001), positive risk per the PNST (p < 0.001), BMI‐for‐age or weight‐for‐length z‐score (p < 0.001), intake <50% for 3 days (p = 0.012) and NPO > 3 days (p = 0.009). The current screen had a sensitivity of 93.9%, specificity of 20.3%, positive predictive value (PPV) of 30.9% and negative predictive value (NPV) of 89.8%. This is compared with the PNST which had a sensitivity of 32%, specificity of 94.2%, PPV of 71% and NPV of 75.8% in this study population.ConclusionThis unique screening tool is useful for predicting nutrition risk and has a greater sensitivity than the PNST alone.

Nutrition Screening in the Pediatric Intensive Care Unit: Evaluation of an Electronic Medical Record-Based Tool

“…A risk factor analysis was completed to assess which combination of screening elements produced the best predictability for malnutrition. The steps for this analysis have been described previously [12]. Briefly, a weighted prediction quality score was computed by assigning penalty weights of 1, 3, and 5 to failing to predict mild, moderate, or severe malnutrition, respectively, and a penalty weight of 0.2 for predicting malnutrition when there was none.…”

“…The development of the CWNST has been described in detail previously [12]. Briefly, this screen was developed based on the ASPEN Pediatric Nutrition Care Pathway, the Academy of Nutrition and Dietetics (AND)/ASPEN pediatric malnutrition criteria, as well as from the clinical experience of the RDs and medical providers at CW [13,14].…”

“…The daily screening process has also been outlined previously [12]. In summary, a nurse administers the PNST upon hospital admission and enters this into the EMR, and then the EMR automatically generates the remaining 7 screen elements every day at 6:00 a.m.…”

“…The evaluation of the CWNST in the PICU population was a retrospective cohort study that included all patients who were admitted to the PICU at CW in 2019 who were ≤17 years old and who were assessed by an RD. Patients from the Neonatal Intensive Care Unit (NICU) were excluded, and data from patients on acute care units have been reported previously [12]. The data obtained included age, length of stay, primary diagnosis (including presence of a childhood chronic condition (CCC), defined as any medical condition that typically lasts ≥12 months and involves one or more organ systems severely enough to require specialty care and possibly hospitalization in a tertiary care center [13]), results of the CWNST (including that of the PNST), and malnutrition assessment.…”

“…The Children's Wisconsin Nutrition Screening Tool (CWNST) is a unique tool created by the dietitians at the Children's Wisconsin (CW) hospital that is embedded in the electronic medical record (EMR) and with that advantage, it contains many more elements which can be automatically obtained from the EMR. The CWNST is described below and was previously evaluated to determine which screening elements were associated with malnutrition risk in the acute care population [12]. We hypothesized that the additional data elements would make the tool more accurate than prior screening tools.…”