Predicting Progression of Chronic Kidney Disease in Sickle Cell Anemia Using Machine Learning Models (PREMIER)

摘要 镰状细胞病(SCD)是一种累及多个终末器官的血管病变，伴有并发症。 包括慢性肾脏疾病(CKD)。蛋白尿是肾小球损伤的早期指标，在 并预测进展性肾脏疾病。SCD患者肾功能下降速度快于 非洲裔美国人的总人口。慢性阻塞性肺病的患病率比过去的三倍高。 普通人口。此外，高风险的APOL1变异与增加的风险有关。 蛋白尿与慢性肾脏病病情进展的关系。肾脏疾病，无论严重程度，和EGFR快速下降 与SCD死亡率的增加有关。因此，及早识别有进展风险的患者 慢性肾脏病的发生对于解决潜在的可改变的危险因素、减缓EGFR下降和降低死亡率具有重要意义。 尽管慢性肾脏病的患病率很高，并导致发病率和死亡率增加，但有 SCD相关肾脏疾病的治疗仍然有限。尽管血管紧张素转换酶抑制剂 (ACE-I)、血管紧张素受体阻滞剂(ARB)和羟基脲在短期研究中减少蛋白尿， 它们在预防或延缓SCD患者进行性肾功能丧失方面的益处尚不清楚。 我们最近报道，机器学习(ML)模型可以识别出快速下降的高危患者 在肾功能方面。此外，较高的血红蛋白浓度也是降低的独立预测因子。 肾功能迅速下降的几率。血管内溶血对脑血管病变的病理生理作用 SCD相关性肾小球病变，Voxelotor，一种改变镰状血红蛋白氧亲和力的小分子，以及 改善镰状红细胞存活率，可能减轻肾小球损伤，减缓慢性肾脏病的进展 使用SCD。 在本申请中，我们建议进行前瞻性的多中心研究，以建立基于ML的预测 成人SCD患者慢性肾脏病进展的模型。此外，在预测有快速增长风险的个人中 肾功能下降，基于持续性蛋白尿(尿ACR≥100 mg/g)的存在，我们将 评估Voxelotor对蛋白尿、肾功能快速下降和CKD进展的影响。 随着对SCD及其并发症的病理生理学认识的进展，结合 越来越多的被批准的药物治疗，及早识别有进展性肾脏风险的患者 疾病和随后死亡风险的增加是必要的，以修改已知的危险因素，启动有针对性的 治疗，并可能延长预期寿命。此外，已知的溶血性贫血对 SCD相关性肾小球疾病和进展性肾脏疾病的发病机制 溶血可能有益于预防和/或减缓肾脏疾病的进展。 病人群体。

ABSTRACT Sickle cell disease (SCD) is characterized by a vasculopathy affecting multiple end organs, with complications including chronic kidney disease (CKD). Albuminuria, an early measure of glomerular injury, is common in SCD and predicts progressive kidney disease. Kidney function decline is faster in SCD patients than in the general African American population. The prevalence of rapid decline in SCD is 3-fold higher than in the general population. Furthermore, high-risk APOL1 variants are associated with an increased risk of albuminuria and progression of CKD in SCD. Kidney disease, regardless of severity, and rapid eGFR decline are associated with increased mortality in SCD. As such, early identification of patients at risk for progression of CKD is important to address potentially modifiable risk factors, slow eGFR decline and reduce mortality. Despite the high prevalence of CKD and its contribution to increased morbidity and mortality, available treatments for SCD-related kidney disease remain limited. Although angiotensin converting enzyme inhibitors (ACE-I), angiotensin receptor blockers (ARBs), and hydroxyurea decrease albuminuria in short-term studies, their benefits in preventing or slowing progressive loss of kidney function in SCD remain undefined. We have recently reported that machine learning (ML) models can identify patients at high risk for rapid decline in kidney function. Further, higher hemoglobin concentration is also an independent predictor of decreased odds of rapid kidney function decline. With the contribution of intravascular hemolysis to the pathophysiology of SCD-related glomerulopathy, voxelotor, a small molecule which modifies sickle hemoglobin oxygen affinity and improves sickle RBC survival, may decrease glomerular injury and slow the progression of CKD in individuals with SCD. In this application, we propose the conduct of a prospective, multicenter study to build a ML-based predictive model for progression of CKD in adults with SCD. Furthermore, in individuals predicted to be at risk for rapid decline in kidney function, based on the presence of persistent albuminuria (urine ACR ≥ 100 mg/g), we will evaluate the effect of voxelotor on albuminuria, rapid decline in kidney function and progression of CKD. With advances in the understanding of the pathophysiology of SCD and its complications, combined with an increasing number of approved drug therapies, early identification of patients at risk for progressive kidney disease and subsequent increased risk of death is necessary to modify known risk factors, initiate targeted therapies and possibly increase life expectancy. Further, with the known contribution of hemolytic anemia to the pathogenesis of SCD-related glomerulopathy and progressive kidney disease, drugs that decrease hemolysis are likely to be beneficial in preventing and/or slowing the progression of kidney disease in this patient population.