Artificial intelligence in early detection and prediction of pediatric/neonatal acute kidney injury: current status and future directions.

Acute kidney injury (AKI) has a significant impact on the short-term and long-term clinical outcomes of pediatric and neonatal patients, and it is imperative in these populations to mitigate the pathways leading to AKI and be prepared for early diagnosis and treatment intervention of established AKI. Recently, artificial intelligence (AI) has provided more advent predictive models for early detection/prediction of AKI utilizing machine learning (ML). By providing strong detail and evidence from risk scores and electronic alerts, this review outlines a comprehensive and holistic insight into the current state of AI in AKI in pediatric/neonatal patients. In the pediatric population, AI models including XGBoost, logistic regression, support vector machines, decision trees, naïve Bayes, and risk stratification scores (Renal Angina Index (RAI), Nephrotoxic Injury Negated by Just-in-time Action (NINJA)) have shown success in predicting AKI using variables like serum creatinine, urine output, and electronic health record (EHR) alerts. Similarly, in the neonatal population, using the "Baby NINJA" model showed a decrease in nephrotoxic medication exposure by 42%, the rate of AKI by 78%, and the number of days with AKI by 68%. Furthermore, the "STARZ" risk stratification AI model showed a predictive ability of AKI within 7 days of NICU admission of AUC 0.93 and AUC of 0.96 in the validation and derivation cohorts, respectively. Many studies have reported the superiority of using biomarkers to predict AKI in pediatric patients and neonates as well. Future directions include the application of AI along with biomarkers (NGAL, CysC, OPN, IL-18, B2M, etc.) in a Labelbox configuration to create a more robust and accurate model for predicting and detecting pediatric/neonatal AKI.

A survey on nutritional management of critically ill children on continuous renal replacement therapy.

We conducted a survey of pediatric nephrologists to examine the knowledge and current practices of and identify challenges in the nutritional management of critically ill children during continuous renal replacement therapy (CRRT). Although it is known that there is a significant effect on nutrition during CRRT, there seems to be a lack of knowledge as well as variability in the practices of nutritional management in these patients, as indicated by our survey results. The heterogeneity of our survey results highlights the need to establish clinical practice guidelines and develop consensus around optimal nutritional management in pediatric patients requiring CRRT. The results as well as the known effects of CRRT on metabolism should be considered during the development of guidelines in critically ill children on CRRT. Our survey findings also highlight the need for further research in the assessment of nutrition, determination of energy needs and caloric dosing, specific nutrient needs, and management.