Stanford University School of Medicine, Stanford, CA.
Department of Biomedical Data Science, Stanford University, Stanford, CA.
AMIA Annu Symp Proc. 2023 Apr 29;2022:866-873. eCollection 2022.
Early nephrology specialty care slows progression of chronic kidney disease (CKD) to end-stage renal disease (ESRD). However, identifying which patients are expected to progress to end-stage disease has been historically challenging to predict. With a limited supply of nephrologists, optimizing nephrology referral is essential for improving patient outcomes. The Kidney Failure Risk Equation (KFRE) provides an accurate metric to identify patients who are at high risk of progression to kidney failure. In this study, we utilize the KFRE to perform a retrospective analysis in a local health network to identify rates of nephrology referral for CKD patients stratified by risk of kidney failure progression. We found a nephrology referral gap in CKD patients at higher risk of progression and an underutilization of albuminuria testing in CKD, suggesting opportunities to improve outcomes by 1) proactively targeting high-risk patients using EHR-based informatics strategies and 2) increasing albuminuria testing as a screening tool.
早期肾脏病专业护理可减缓慢性肾脏病 (CKD) 向终末期肾病 (ESRD) 的进展。然而,确定哪些患者预计会进展为终末期疾病在历史上一直难以预测。由于肾科医生数量有限,因此优化肾科转介对于改善患者结局至关重要。肾衰竭风险方程 (KFRE) 提供了一种准确的指标,可以识别出有进展为肾衰竭高风险的患者。在这项研究中,我们利用 KFRE 在当地卫生网络中进行回顾性分析,根据肾衰竭进展风险对 CKD 患者进行肾科转介率分层。我们发现,进展风险较高的 CKD 患者存在肾科转介差距,并且 CKD 患者的白蛋白尿检测使用率不足,这表明有机会通过以下两种方式改善结局:1)使用基于电子健康记录的信息学策略主动针对高风险患者,2)增加白蛋白尿检测作为筛查工具。