Are dietitian recommendations followed? A descriptive study of paediatric hospitalised and ambulatory patients.

BACKGROUND: The primary objective of this cross-sectional retrospective study was to describe the implementation of dietitian prescribed nutrition recommendations in malnourished paediatric patients in the hospital and ambulatory settings. We also aimed to investigate other characteristics that could be associated with differences in implementation. METHODS: Data were collected from 186 hospitalised and 565 ambulatory patients between February 2020 and January 2021. Data included age, hospital or ambulatory specialty departments, primary diagnosis, malnutrition status, hospital length of stay (LOS), and medical nutrition therapy recommendations. Implementation by the medical team in the hospital setting and adherence by the family in the outpatient setting were categorised as "Full", "Partial" or "None". "Partial" and "None" were combined for analysis. RESULTS: Dietitian prescribed recommendations were implemented in 79.6% of hospitalised patients. In the ambulatory population, 46.4% of patients were adherent with nutrition recommendations. Within the hospital, there was a significant difference in implementation of nutrition recommendations based on age (p = 0.047), hospital department (p = 0.002) and LOS (p = 0.04), whereas, in the ambulatory population, there were no significant differences in the rate of adherence among any of the studied characteristics. CONCLUSIONS: Dietitian recommendations are frequently implemented in the hospital, whereas adherence to such recommendations is poor in the outpatient population. Interventions to improve adherence to nutrition recommendations in the ambulatory setting are needed.

Perioperative nutrition in the setting of pediatric inflammatory bowel disease.

BACKGROUND: Optimization of nutrition prior to inflammatory bowel disease (IBD)-related surgery could improve outcomes. The aim of this study was to assess the perioperative nutrition status and management of children undergoing intestinal resection for treatment of their IBD. METHODS: We identified all patients with IBD who underwent primary intestinal resection. We identified malnutrition using established criteria and methods of nutrition provision at various time points (preoperative outpatient evaluation, admission, and postoperative outpatient follow-up) for elective cases (who underwent their procedure at a scheduled admission) and urgent cases (who underwent an unplanned surgical intervention). We also recorded data on postsurgical complications. RESULTS: A total of 84 patients were identified in this single-center study (male sex: 40%, mean age: 14.5 years, Crohn's disease: 65%). Thirty-four patients (40%) had some degree of malnutrition. Prevalence of malnutrition in the urgent and elective cohorts was similar (48% vs 36%; P = 0.37). Of these patients, 29 (34%) were noted to be on some type of nutrition supplementation prior to surgery. Postoperatively, BMI z scores increased (-0.61 vs -0.42; P = 0.0008), but the percentage of patients who were malnourished did not change from preoperative status (40% vs 40%; P = 0.10). Despite this, use of nutrition supplementation was only noted in 15 (17%) patients at postoperative follow-up. Complications were not associated with nutrition status. CONCLUSION: Utilization of supplemental nutrition decreased postprocedure despite no change in malnutrition prevalence. These findings support the development of a pediatric-specific perioperative nutrition protocol in the setting of IBD-related surgery.

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

Hospitalized, critically ill children are at increased risk of developing malnutrition. While several pediatric nutrition screening tools exist, none have been validated in the pediatric intensive care units (PICU). The Children's Wisconsin Nutrition Screening Tool (CWNST) is a unique nutrition screening tool that includes the Pediatric Nutrition Screening Tool (PNST) and predictive elements from the electronic medical record and was found to be more sensitive than the PNST in acute care units. The aim of this study was to assess the performance of the tool in detecting possible malnutrition in critically ill children. The data analysis, including the results of the current nutrition screening, diagnosis, and nutrition status was performed on all patients admitted to PICUs at Children's Wisconsin in 2019. All 250 patients with ≥1 nutrition assessment by a dietitian were included. The screening elements that were predictive of malnutrition included parenteral nutrition, positive PNST, and BMI-for-age/weight-for-length z-score. The current screen had a sensitivity of 0.985, specificity of 0.06, positive predictive value (PPV) of 0.249, and negative predictive value of 0.929 compared to the PNST alone which had a sensitivity of 0.1, specificity of 0.981, PPV of 0.658, and NPV of 0.749. However, of the 250 included patients, 97.2% (243) had a positive nutrition screen. The CWNST can be easily applied through EMRs and predicts the nutrition risk in PICU patients but needs further improvement to improve specificity.

Evaluation of an electronic medical record-based Paediatric Nutrition Screening Tool.

BACKGROUND: Nutrition 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. METHODS: The 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. RESULTS: One 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. CONCLUSION: This unique screening tool is useful for predicting nutrition risk and has a greater sensitivity than the PNST alone.

Novel Pediatric Height Outlier Detection Methodology for Electronic Health Records via Machine Learning With Monotonic Bayesian Additive Regression Trees.

OBJECTIVE: To create a new methodology that has a single simple rule to identify height outliers in the electronic health records (EHR) of children. METHODS: We constructed 2 independent cohorts of children 2 to 8 years old to train and validate a model predicting heights from age, gender, race and weight with monotonic Bayesian additive regression trees. The training cohort consisted of 1376 children where outliers were unknown. The testing cohort consisted of 318 patients that were manually reviewed retrospectively to identify height outliers. RESULTS: The amount of variation explained in height values by our model, R2 , was 82.2% and 75.3% in the training and testing cohorts, respectively. The discriminatory ability to assess height outliers in the testing cohort as assessed by the area under the receiver operating characteristic curve was excellent, 0.841. Based on a relatively aggressive cutoff of 0.075, the outlier sensitivity is 0.713, the specificity 0.793; the positive predictive value 0.615 and the negative predictive value is 0.856. CONCLUSIONS: We have developed a new reliable, largely automated, outlier detection method which is applicable to the identification of height outliers in the pediatric EHR. This methodology can be applied to assess the veracity of height measurements ensuring reliable indices of body proportionality such as body mass index.