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: 10676823
• 负责人: Kenneth I Ataga
• 金额: $ 62.62万
• 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/10676823
• 关键词: APOL1 gene; Address; Adult; Affect; Affinity; African American population; 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; Measures; Mediating; Morbidity - disease rate; Multicenter Studies; Nitric Oxide; Organ; Oxidative Stress; Oxygen; Pathogenesis; Pathway interactions; Patients; Pharmaceutical Preparations; Pharmacotherapy; Prevalence; Pulmonary Hypertension; Renal function; Reporting; Risk; Risk Factors; Severities; Sickle Cell; Sickle Cell Anemia; Sickle Hemoglobin; Testing; Urine; Variant; Vascular Diseases; functional decline; hemoglobin polymer; high risk; hydroxyurea; improved; machine learning model; machine learning prediction; modifiable risk; mortality; mortality risk; novel; patient population; prevent; progression risk; 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.

摘要 镰状细胞病（SCD）的特点是血管病变影响多个终末器官，并发症 包括慢性肾病（CKD）。蛋白尿是肾小球损伤的早期指标， SCD和预测进行性肾脏疾病。SCD患者的肾功能下降速度快于 非裔美国人的普遍。SCD患者的快速下降患病率是正常人的3倍。 一般人口。此外，高风险的APOL 1变异与以下风险增加相关： 蛋白尿和SCD中CKD的进展。肾脏疾病，无论严重程度如何，eGFR快速下降 与SCD的死亡率增加有关。因此，早期识别有进展风险的患者 对于解决潜在的可改变的风险因素、减缓eGFR下降和降低死亡率非常重要。 尽管CKD的高患病率及其对发病率和死亡率增加的贡献， SCD相关肾病的治疗仍然有限。虽然血管紧张素转换酶抑制剂 在短期研究中，血管紧张素I（ACE-I）、血管紧张素受体阻滞剂（ARB）和羟基脲可减少白蛋白尿， 但它们在预防或减缓SCD中肾功能进行性丧失方面的益处仍不明确。 我们最近报道，机器学习（ML）模型可以识别快速下降的高风险患者 肾脏功能。此外，较高的血红蛋白浓度也是降低的血红蛋白浓度的独立预测因子。 肾功能快速下降的几率由于血管内溶血对血管内凝血的病理生理学的贡献， SCD相关肾小球病，voxelotor，一种改变镰状血红蛋白氧亲和力的小分子， 改善镰状红细胞存活率，可能减少肾小球损伤并减缓个体CKD的进展 关于SCD 在本申请中，我们建议进行一项前瞻性、多中心研究，以建立一个基于ML的预测模型。 成人SCD患者的CKD进展模型。此外，在预测有快速死亡风险的个体中， 肾功能下降，基于持续性白蛋白尿（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理解的进展， 批准的药物疗法数量不断增加，早期识别有进行性肾脏风险的患者 疾病和随后的死亡风险增加是必要的，以修改已知的风险因素，启动有针对性的 治疗，并可能增加预期寿命。此外，已知溶血性贫血对 SCD相关性肾小球病和进行性肾病的发病机制， 溶血可能有益于预防和/或减缓肾脏疾病的进展， 患者人群。