Abstract
Extracorporeal Shock Wave Lithotripsy (ESWL) has been a cornerstone in treating pediatric urinary stones for nearly four decades, but requires tailored approaches due to anatomical and physiological differences from adults. This review synthesizes current evidence on ESWL efficacy predictors in children, integrating multicenter data and emerging technologies. Key traditional predictors include favorable stone characteristics [density ≤600 Hounsfield units [HU], size ≤15 mm, skin-to-stone distance [SSD] ≤6.6 cm, upper/middle calyx or ureteral location] and patient factors (age ≤3 years, male sex); conversely, urinary tract infections (UTIs), BMI >22, and multiple stones correlate with poorer outcomes. Innovations like dual-energy CT (DECT), AI-based models, shear wave elastography (SWE), and bioelectric impedance analysis (BIA) offer promising non-invasive preoperative assessment. We highlight the need for standardized multifactorial predictive models to optimize pediatric ESWL outcomes. Future directions emphasize AI, big data, and multidisciplinary collaboration to enhance personalized treatment and reduce complications. This analysis provides clinicians with evidence-based tools to refine pediatric ESWL protocols.