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

项目摘要 预防肾结石进展仍然是公共卫生领域的一个严重障碍，尽管取得了进展， 结石形成的潜在机制肾结石，通常沿着剧烈的疼痛， 流行于地球仪，影响了近12%的世界人口。在美国，估计 肾结石疾病的终生患病率在男性中约为10.6%，在女性中约为7.1%。会谈后两国领导 在首次发作时，结石形成者复发结石形成的风险较高，超过50%的患者经历过 10年内复发，反映出目前预防办法的不足。此外该 结石病的形成与慢性肾病的长期并发症密切相关， 终末期肾病，沿着显著的发病率、死亡率以及医疗保健费用负担。 作为对PA-20-185的回应，本提案的总体目标是最大限度地发挥预防性药物的功效。 通过开发临床预测工具以及 计算算法和软件。更具体地说，我们提出了新的证据积累驱动 方法（1）开发肾结石疾病进展的患者水平风险预测模型， 结石疾病的亚型;和（2）通过以下方法确定结石疾病进展的可改变的风险因素： 将历史上现有的预测模型整合到新的EHR或注册数据集中。我们将申请并验证 建议的方法，以现实世界的数据，包括UTSW矿物代谢结石登记处，推 泌尿系结石疾病研究网络（这是一个由美国国家卫生研究院资助的临床研究网络）进行的试验。 NIDDK）和瑞士肾结石队列。 该项目的成功将填补肾结石疾病进展的知识空白，并导致 预测工具箱，告知临床医生肾结石进展的风险，从而促进及时的临床 决策和实施有针对性的策略，以预防或减少结石疾病的进展。

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.