Health-related quality of life for pediatric patients with end-stage kidney disease: A systematic review and meta-analysis of the Pediatric Quality of Life Inventory (PedsQL).

INTRODUCTION: Health-related quality of life (HRQoL) studies demonstrate the impact of end-stage renal disease (ESRD) on the physical and psychosocial development of children. While several instruments are used to measure HRQoL, few have standardized domains specific to pediatric ESRD. This review examines current evidence on self and proxy-reported HRQoL among pediatric patients with ESRD, based on the Pediatric Quality of Life Inventory (PedsQL) questionnaires. METHODS: Following PRISMA guidelines, we conducted a systematic review and meta-analysis on HRQoL using the PedsQL 4.0 Generic Core Scale (GCS) and the PedsQL 3.0 ESRD Module among 5- to 18-year-old patients. We queried PubMed, Embase, Web of Science, CINAHL, and Cochrane databases. Retrospective, case-controlled, and cross-sectional studies using PedsQL were included. FINDINGS: Of 435 identified studies, 14 met inclusion criteria administered in several countries. Meta-analysis demonstrated a significantly higher total HRQoL for healthy patients over those with ESRD (SMD:1.44 [95% CI: 0.78-2.09]) across all dimensional scores. In addition, kidney transplant patients reported a significantly higher HRQoL than those on dialysis (PedsQL GCS, SMD: 0.33 [95% CI: 0.14-0.53]) and (PedsQL ESRD, SMD: 0.65 [95% CI: 0.39-0.90]) concordant with parent-proxy reports. DISCUSSION: Patients with ESRD reported lower HRQoL in physical and psychosocial domains compared with healthy controls, while transplant and peritoneal dialysis patients reported better HRQoL than those on hemodialysis. This analysis demonstrates the need to identify dimensions of impaired functioning and produce congruent clinical interventions. Further research on the impact of individual comorbidities in HRQoL is necessary for developing comprehensive, integrated, and holistic treatment programs.

Nutrition in critically ill children with acute kidney injury on continuous kidney replacement therapy: a 2023 executive summary.

OBJECTIVES: Nutrition plays a vital role in the outcome of critical illness in children, particularly those with acute kidney injury. Currently, there are no established guidelines for children with acute kidney injury treated with continuous kidney replacement therapy. Our objective was to create clinical practice points for nutritional assessment and management in critically ill children with acute kidney injury receiving continuous kidney replacement therapy. METHODS: An electronic search using PubMed and an inclusive academic library search (including MEDLINE, Cochrane, and Embase databases) was conducted to find relevant English-language articles on nutrition therapy for children (<18 y of age) receiving continuous kidney replacement therapy. RESULTS: The existing literature was reviewed by our work group, comprising pediatric nephrologists and experts in nutrition. The modified Delphi method was then used to develop a total of 45 clinical practice points. The best methods for nutritional assessment are discussed. Indirect calorimetry is the most reliable method of predicting resting energy expenditure in children on continuous kidney replacement therapy. Schofield equations can be used when indirect calorimetry is not available. The non-intentional calories contributed by continuous kidney replacement therapy should also be accounted for during caloric dosing. Protein supplementation should be increased to account for the proteins, peptides, and amino acids lost with continuous kidney replacement therapy. CONCLUSIONS: Clinical practice points are provided on nutrition assessment, determining energy needs, and nutrient intake in children with acute kidney injury and on continuous kidney replacement therapy based on the existing literature and expert opinions of a multidisciplinary panel.

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.