Risk Prediction and Precision Prevention of Kidney Stone Recurrence

Kidney stone disease is one of the most common urological conditions worldwide, and its burden is driven less by the first episode than by its tendency to return. Recurrence affects a large share of patients within years of an initial stone, exposing them to repeated pain, procedures, and long-term risks to kidney function. Yet prevention is still largely applied in a uniform way, offering the same general advice to patients whose actual risk varies widely. Detailed metabolic evaluation can refine this picture, but it is not always accessible, timely, or affordable. As a result, many patients most likely to benefit from targeted prevention are never identified, while others receive intensive follow-up they may not need.This Research Topic aims to accelerate the shift from general prevention toward risk-stratified, individualized prevention of kidney stone recurrence. The central idea is to bring together research that estimates who is most likely to experience a recurrence, and to connect that prediction to concrete preventive action. We are especially interested in models that draw on routinely available clinical indicators, such as body mass index, serum creatinine, cystatin C, and other standard laboratory parameters, so that recurrence risk can be estimated even where specialist metabolic assessment is not immediately available. By assembling clinical prediction models, biomarker studies, and modern statistical and machine-learning approaches alongside personalized prevention strategies, the collection seeks to define how risk information can be translated into better decisions in everyday practice, improving outcomes and using preventive resources more efficiently.We welcome contributions addressing, but not limited to:- Clinical risk prediction models for kidney stone recurrence- Biomarkers and routine clinical indicators associated with recurrence risk- Machine-learning and statistical modeling approaches for recurrence prediction- Personalized prevention strategies based on risk stratification- Lifestyle, metabolic, dietary, and pharmacological interventions for recurrence prevention- Translational and real-world implementation of recurrence prediction models in clinical practiceThis Research Topic invites work from urology, nephrology, epidemiology, clinical prediction modeling, biomarker research, and preventive medicine. We welcome Original Research, Reviews, Systematic Reviews, Brief Research Reports, Study Protocols, and Perspective articles. Model-based studies should report development and, where possible, validation. We particularly encourage submissions that move beyond describing risk toward demonstrating how prediction can guide individualized prevention. Priority will be given to clinically actionable, externally validated, and implementation-ready approaches that demonstrate how recurrence-risk estimates can inform the intensity, timing, and type of preventive intervention.Topic Editor Anja Pfau is a consultant for Avanzanite Bioscience B.V. She is also a co-inventor on a filed, not yet granted, patent (EP21156284.8, “Composition and method for reducing oxalate levels in patients receiving maintenance dialysis”). Topic Editor Agnieszka Pozdzik is the founder and Scientific Director of Renal Care & Research SRL, a company developing digital health and connected-care solutions for nephrology and cardio-kidney-metabolic care. Topic Editor Hassan Razvi receives grant support from Cook Urological and Storz Lithotripsy for a clinical fellowship training program. The other Topic Editors report no competing interests related to this Research Topic. Topic editors: Kaiguo Xia, Anja Pfau, Agnieszka Pozdzik, Hassan Razvi, Xudong Shen Source: https://www.frontiersin.org/research-topics/83438/risk-prediction-and-precision-prevention-of-kidney-stone-recurrence