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

使用机器学习模型预测镰状细胞性贫血慢性肾病的进展 (PREMIER)

• 批准号: 10280257
• 负责人: Kenneth I Ataga
• 金额: $ 70.53万
• 依托单位: UNIVERSITY OF TENNESSEE HEALTH SCI CTR
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-10 至 2026-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10280257
• 关键词: APOL1 gene; Address; Adult; Affect; Affinity; African American; Albuminuria; Angiotensin Receptor; Angiotensin-Converting Enzyme Inhibitors; Biological Availability; Chronic Kidney Failure; Early identification; Functional disorder; General Population; Glomerular Filtration Rate; Hemoglobin; Hemolysis; Hemolytic Anemia; High Prevalence; Immune response; Individual; Inflammatory Response; Injury; Injury to Kidney; Ischemic Stroke; Kidney; Kidney Diseases; Life Expectancy; Machine Learning; Measures; Mediating; Modeling; Morbidity - disease rate; Multicenter Studies; Nitric Oxide; Organ; Oxidative Stress; Oxygen; Pathogenesis; Pathway interactions; Patients; Pharmaceutical Preparations; Pharmacotherapy; Population; Prevalence; Pulmonary Hypertension; Renal function; Reporting; Risk; Risk Factors; Severities; Sickle Cell; Sickle Cell Anemia; Sickle Hemoglobin; Testing; Urine; Variant; Vascular Diseases; base; functional decline; high risk; hydroxyurea; improved; modifiable risk; mortality; mortality risk; novel; patient population; polymerization; predictive modeling; prevent; prospective; protective effect; randomized controlled study; renal damage; sickling inhibitor; small molecule; standard of care; targeted treatment

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.

抽象的 镰状细胞疾病（SCD）的特征是影响多个末端器官的血管病，并发症 包括慢性肾脏疾病（CKD）。蛋白尿是肾小球损伤的早期测量，在 SCD并预测进行性肾脏疾病。 SCD患者的肾功能下降比 一般非裔美国人人口。 SCD快速下降的患病率比 一般人口。此外，高风险Apol1变体与增加的风险有关 SCD中CKD的蛋白尿和进展。肾脏疾病，无论严重程度如何，EGFR迅速下降 与SCD死亡率增加有关。因此，早期确定有进展风险的患者 CKD的重要性对于解决潜在可修改的危险因素，缓慢的EGFR下降并降低死亡率很重要。 尽管CKD的患病率很高及其对发病率和死亡率增加的贡献，但可用 与SCD相关的肾脏疾病的治疗仍然有限。尽管血管紧张素转化酶抑制剂 （ACE-I），血管紧张素受体阻滞剂（ARB）和羟基脲在短期研究中降低蛋白尿， 他们在SCD中预防或放缓肾脏功能的逐步逐渐损失方面的好处仍然不确定。 我们最近报道说，机器学习（ML）模型可以识别出快速下降风险高风险的患者 在肾功能中。此外，较高的血红蛋白浓度也是改善的独立预测因子 快速肾功能下降的几率。血管内溶血对病理生理的贡献 与SCD相关的肾小球病，紫外线，一种小分子，可修饰镰状血红蛋白氧亲和力和 改善镰刀RBC的生存，可能会减少肾小球损伤并减慢个体的CKD进展 与SCD。 在此应用中，我们提出了一项前瞻性，多中心研究的行为，以建立基于ML的预测 SCD成人CKD进展的模型。此外，在个人中被预测有迅速的风险 肾功能下降，基于持续的蛋白尿（尿液ACR≥100mg/g）的存在，我们将 评估体素对蛋白尿的影响，肾功能快速下降和CKD的进展。 随着对SCD的病理生理及其并发症的理解的进步，结合了 批准的药物疗法数量增加，早期鉴定出患有进行性肾脏风险的患者 疾病和随后的死亡风险增加是改变已知危险因素，启动目标是必要的 疗法并可能增加预期寿命。此外，溶血性贫血对 与SCD相关的肾小球病和进行性肾脏疾病的发病机理，降低的药物 溶血可能有益于预防和/或减缓肾脏疾病的进展 患者人数。