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)

基本信息

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
项目类别：
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

项目摘要

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.

抽象的镰状细胞病 (SCD) 的特点是影响多个终末器官的血管病变，并伴有并发症包括慢性肾病 (CKD)，蛋白尿是肾小球损伤的早期指标，在以下疾病中很常见。 SCD 并预测进行性肾病，SCD 患者的肾功能下降速度比正常人更快。一般非裔美国人群体中 SCD 快速下降的患病率是普通人的 3 倍。此外，高风险 APOL1 变异与罹患癌症的风险增加有关。 SCD 中的蛋白尿和 CKD 进展，无论严重程度如何，以及 eGFR 快速下降。与 SCD 死亡率增加相关，因此，早期识别有进展风险的患者。 CKD 的研究对于解决潜在的可改变的危险因素、减缓 eGFR 下降和降低死亡率非常重要。尽管 CKD 患病率很高且导致发病率和死亡率增加，但可尽管血管紧张素转换酶抑制剂对 SCD 相关肾脏疾病的治疗仍然有限。（ACE-I）、血管紧张素受体阻滞剂（ARB）和羟基脲在短期研究中可减少白蛋白尿，它们在预防或减缓 SCD 肾功能进行性丧失方面的益处仍不清楚。我们最近报道，机器学习 (ML) 模型可以识别快速衰退的高风险患者此外，较高的血红蛋白浓度也是肾功能下降的独立预测因素。肾功能快速下降的可能性与血管内溶血的病理生理学的贡献有关。 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提高镰状红细胞存活率，可能减少肾小球损伤并减缓个体 CKD 的进展与SCD。在此应用中，我们建议进行一项前瞻性多中心研究，以构建基于机器学习的预测模型患有 SCD 的成人中 CKD 进展的模型此外，在预测有快速风险的个体中。肾功能下降，基于持续性白蛋白尿的存在（尿液 ACR ≥ 100 mg/g），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 病理生理学及其并发症的认识不断进步，结合批准的药物疗法数量不断增加，及早识别有进展性肾病风险的患者疾病和随后死亡风险的增加对于改变已知的危险因素、启动有针对性的此外，已知溶血性贫血会导致死亡。 SCD 相关肾小球病和进行性肾病的发病机制，减少药物溶血可能有助于预防和/或减缓肾病的进展患者群体。