Early diagnosis of light chain amyloidosis

轻链淀粉样变性的早期诊断

• 批准号: 10562721
• 负责人: Anita D'Souza
• 金额: $ 27.3万
• 依托单位: MEDICAL COLLEGE OF WISCONSIN
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-02-01 至 2028-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10562721
• 关键词: Affect; African American population; Age; Aging; Agreement; Algorithms; Amyloid; Amyloid Fibrils; Amyloidosis; Bayesian learning; Black Populations; Calibration; Cardiac; Cardiomyopathies; Cessation of life; Chronic Kidney Failure; Clinical Research; Cohort Studies; Data; Data Reporting; Data Set; Deposition; Diagnosis; Diagnostic; Disease; Disease Management; Disparity; Early Diagnosis; Electronic Health Record; Epidemiology; Ethnic Origin; Functional disorder; Heart failure; Hematological Disease; Hypertrophic Cardiomyopathy; Incidence; Individual; Insurance Coverage; Light; Literature; Medicare; Methodology; Monoclonal Gammapathies; Monoclonal gammopathy of uncertain significance; Morbidity - disease rate; Multiple Myeloma; Myocardial dysfunction; Organ; Outcome; Pathway interactions; Patient-Focused Outcomes; Patients; Pattern; Performance; Physicians; Plasma Cells; Precancerous Conditions; Prevalence; Prognosis; Race; Risk; Sampling; Specialist; Symptoms; System; Techniques; Testing; Time; Underserved Population; Validation; age group; beneficiary; body system; cardiac amyloidosis; chemotherapy; experience; follow-up; health disparity; high risk; improved; improved outcome; machine learning algorithm; mortality; novel; prediction algorithm; premalignant; primary amyloidosis of light chain type; racial difference; racial population; response; statistical learning; tool

PROJECT SUMMARY Light chain (AL) amyloidosis is a recalcitrant and deadly hematologic disease characterized by organ dysfunction from insoluble fibril deposition derived from clonal free light chains arising from a monoclonal gammopathy. The disease has a high early mortality of 40-45% at two years due to heart failure. Patients with advanced AL amyloidosis have high morbidity and mortality in the initial period after diagnosis owing to cardiac dysfunction. Despite experiencing multiple symptoms and demonstrating signs of the disease, many patients are diagnosed late, sometimes by years, because these ‘precursor diagnoses’ are often non-specific. Observational data also suggest that Black individuals are more likely to be underdiagnosed with cardiac amyloidosis. Monoclonal gammopathy of undetermined significance (MGUS) and smoldering multiple myeloma (MGUS+) are more common in Black individuals, as is the prevalence of hypertrophic cardiomyopathy and chronic kidney disease, both of which also occur in AL amyloidosis. We hypothesize that patients can be diagnosed early by assessing the patterns of precursor diagnoses that predate AL amyloidosis diagnosis. Our application seeks to create an algorithm using Bayesian machine learning statistical methodology to create an alert system that can help guide physicians toward early consideration of an AL amyloidosis diagnosis. We will execute the following specific aims using nationally representative Medicare data: 1) Identify patterns of precursor diagnoses associated with the occurrence of AL amyloidosis and develop a predictive algorithm using Bayesian machine learning techniques in Medicare beneficiaries with MGUS+. Patterns will be examined longitudinally at one-year timepoints over a five-year period preceding the AL amyloidosis diagnosis contrasting between MGUS+ with known AL and MGUS+ with no known AL to identify patterns that might best predict disease. 2) Study the performance of the predictive MGUS-AL algorithm. This will be assessed internally in the Medicare data set, overall and by race groups (Aim 2A) and external validation using TriNetX multicenter EHR data for MGUS+ patients of all ages, races, and insurance coverage (Aim 2B). 3) Estimate the number of potentially undiagnosed AL amyloidosis patients with MGUS+. Based on the patterns identified in Aim 1 and validated in Aim 2, we will identify subjects at high risk for undiagnosed AL amyloidosis (Aim 3A) and estimate the excess 2-year mortality and number of potential lives saved by our early warning system, overall and by racial group (Aim 3B). This study provides an unprecedented opportunity to identify patterns of precursor diagnoses to diagnose AL amyloidosis early. An important anticipated outcome is to improve health disparities by increasing AL amyloidosis diagnosis in Black individuals who are already at higher risk for MGUS and other end-organ damage associated with AL amyloidosis. The novel, rigorous and easy-to-implement early warning system has the potential to transform the outcomes of patients with AL amyloidosis by allowing the diagnosis to occur at an early stage.

项目摘要 轻链（AL）淀粉样变性是一种由器官功能障碍的顽固性和致命的血液学疾病 来自由单克隆胶质病引起的克隆游离光链得出的不溶性纤维矿床。这 由于心力衰竭，疾病在两年后的早期死亡率高40-45％。晚期AL患者 由于心脏功能障碍，在诊断后的初期，淀粉样变性具有高发病率和死亡率。 尽管经历了多种症状并证明了这种疾病的迹象，但许多患者被诊断出 迟到，有时是多年来，因为这些“前体诊断”通常不是特定的。观察数据也是如此 表明黑人个体更有可能被心脏淀粉样变性诊断出来。单克隆 不确定意义（MGU）和闷烧多发性骨髓瘤（MGUS+）的伽马病更为 在黑人个体中很常见，肥厚性心肌病和慢性肾脏疾病的患病率也 两者也发生在Al淀粉样变性中。我们假设可以通过评估可以尽早诊断患者 前体诊断的模式早于Al淀粉样变性诊断。我们的应用程序旨在创建一个 使用贝叶斯机器学习统计方法来创建一个警报系统，可以帮助指导指导的算法 医师早日考虑淀粉样变性诊断。我们将执行以下特定 使用全国代表Medicare数据的目的：1）确定与之相关的前体诊断模式 AL淀粉样变性的发生并使用贝叶斯机器学习开发预测算法 MGUS+Medicare受益人的技术。模式将在一年中纵向检查 在MGUS+之间的Al淀粉样变性诊断对比之前的五年内的时间点 已知的AL和MGUS+没有已知的Al来识别可以最好预测疾病的模式。 2）研究 预测MGUS-AL算法的性能。这将在Medicare数据集内部进行评估， 使用Trinetx多中心EHR数据进行MGUS+的总体和竞赛组（AIM 2A）和外部验证 各个年龄段，种族和保险范围的患者（AIM 2B）。 3）估计潜在的未诊断的数量 MGUS+的Al淀粉样变性患者。基于AIM 1中确定的模式并在AIM 2中验证，我们将 确定未诊断淀粉样蛋白病的高风险的受试者（AIM 3A），并估计多余的2年死亡率 我们的预警系统，整体和种族群体（AIM 3B）挽救了潜在的生命。这 研究提供了一个前所未有的机会，可以识别前体诊断模式来诊断 早期淀粉样变性。一个重要的预期结果是通过增加AL淀粉样变性来改善健康差异 黑人诊断已经面临MGU和其他最终器官损害的风险较高的黑人诊断 与Al淀粉样变性。这部小说，严格且易于实施的预警系统有可能 通过允许早期诊断来改变AL淀粉样变性患者的结局。