Early diagnosis of light chain amyloidosis

项目摘要 轻链淀粉样变性是一种以器官功能障碍为特征的恶性、致死性血液病 来自于单克隆丙种球蛋白病产生的克隆游离轻链的不溶性原纤维沉积。的 由于心力衰竭，该疾病在两年时具有40-45%的高早期死亡率。晚期AL患者 淀粉样变在诊断初期因心功能不全而有较高的发病率和死亡率。 尽管经历了多种症状和表现出疾病的迹象，许多患者被诊断为 晚，有时是几年，因为这些“前兆诊断”往往是非特异性的。观察数据还 表明黑人更容易被诊断为心脏淀粉样变性。单克隆 意义不明的丙种球蛋白病（MGUS）和郁积型多发性骨髓瘤（MGUS+） 在黑人中很常见，肥厚性心肌病和慢性肾病的患病率也很高， 这两种情况也发生在AL淀粉样变性中。我们假设患者可以通过评估 早于AL淀粉样变性诊断的前驱诊断模式。我们的应用程序旨在创建一个 算法使用贝叶斯机器学习统计方法来创建警报系统， 医生对AL淀粉样变性诊断的早期考虑。我们将执行以下具体 目的是使用具有全国代表性的医疗保险数据：1）确定与以下疾病相关的前驱诊断模式： AL淀粉样变性发生并使用贝叶斯机器学习开发预测算法 技术在医疗保险受益人与MGUS+。模式将在一年内进行纵向检查 AL淀粉样变性诊断前5年内的时间点，比较MGUS+与 已知的AL和MGUS+与未知的AL，以确定可能最好地预测疾病的模式。2)研究 预测MGUS-AL算法的性能。这将在医疗保险数据集中进行内部评估， 总体和按人种组（Aim 2A）以及使用TriNetX多中心EHR数据进行的MGUS+外部验证 所有年龄、种族和保险范围的患者（目标2B）。3)估计可能未确诊的人数 MGUS+的AL淀粉样变性患者。基于目标1中确定并在目标2中验证的模式，我们将 确定未确诊AL淀粉样变性（Aim 3A）的高风险受试者，并估计2年死亡率 以及我们的预警系统所挽救的潜在生命的总体和按种族群体分列的数量（目标3B）。这 一项研究提供了一个前所未有的机会，以确定诊断AL的前驱诊断模式 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 已经处于MGUS和其他终末器官损伤相关的较高风险的黑人个体的诊断 AL淀粉样变性这一新颖、严格和易于实施的预警系统有可能 通过允许在早期阶段进行诊断来改变AL淀粉样变性患者的结局。

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.