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R01NIH · NATIONAL INSTITUTE OF DIABETES AND DIGESTIVE AND KIDNEY DISEASESNIH

Novel evidence-accumulation-driven methods for characterizing kidney stone progression

Liu, Yu-Lun (Contact)·UT SOUTHWESTERN MEDICAL CENTER, TX·2022–2026·ACTIVE
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INSTITUTION

UT SOUTHWESTERN MEDICAL CENTER, TX

PRINCIPAL INVESTIGATOR

Liu, Yu-Lun (Contact)

FUNDING

$611K

YEAR

2022

MOONBASE SCORE

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Abstract

PROJECT SUMMARY Preventing kidney stone progression remains a serious obstacle in public health, despite advances unravelling the underlying mechanisms of stone formation. Kidney stone disease, often along with excruciating pain, is highly prevalent around the globe, affecting nearly 12% of the world population. In the United States, the estimated lifetime prevalence of kidney stone disease is approximately 10.6% in men and 7.1% in women. Following their initial episode, stone formers are at higher risk for recurrent stone formation, with more than 50% experiencing a recurrence within 10 years, reflecting the inadequacies of current prevention regimens. In addition, the formation of stone disease is strongly associated with long-term complications of chronic kidney disease and end-stage renal disease, along with significant morbidity, mortality as well as burden of health care cost. In response to PA-20-185, the overarching goal of this proposal is to maximize the efficacy of preventive strategies against kidney stone disease progression by developing clinical prediction tools as well as computational algorithms and software. More specifically, we propose novel evidence-accumulation-driven methods (1) to develop patient-level risk prediction models for kidney stone disease progression accounting for subtypes of stone conditions; and (2) to identify modifiable risk factors for stone disease progression by integrating historically existing prediction models into new EHR or registry datasets. We will apply and validate the proposed methods to real-world data, including the UTSW Mineral Metabolism Stone Registry, the PUSH trial conducting by the Urinary Stone Disease Research Network (which is a clinical research network funded by NIDDK), and the Swiss Kidney Stone Cohort. The success of this project will fill the knowledge gap of kidney stone disease progression, and lead to a predictive toolbox to inform clinicians on the risk of kidney stone progression, thereby facilitating timely clinical decision-making and implementation of targeted strategies to prevent or reduce stone disease progression.

R01NATIONAL INSTITUTE OF DIABETES AND DIGESTIVE AND KIDNEY DISEASESClinical Data Management and Analysis Study Section[CDMA]modelsexistinglevelhealthaffectingstageurinaryinitialstonealongaccumulationaccountingcomplicationssubtypesfacilitatingintegratingyearsdiseaseunderlyingconditions

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