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.

项目概要 轻链 (AL) 淀粉样变性是一种顽固性致命血液疾病，其特征是器官功能障碍 来自单克隆伽马病产生的克隆游离轻链的不溶性原纤维沉积。 由于心力衰竭，晚期 AL 患者两年内的早期死亡率高达 40-45%。 由于心脏功能障碍，淀粉样变性在诊断后的初期具有很高的发病率和死亡率。 尽管出现多种症状并表现出疾病迹象，但许多患者还是被诊断出来 晚了，有时晚了好几年，因为这些“前兆诊断”通常也是非特异性的。 表明黑人更有可能对心脏淀粉样变性诊断不足。 意义未明的伽马病 (MGUS) 和冒烟型多发性骨髓瘤 (MGUS+) 更常见 在黑人中很常见，肥厚性心肌病和慢性肾脏病的患病率也是如此， 这两种情况也发生在 AL 淀粉样变性中，我们相信患者可以通过评估进行早期诊断。 早于 AL 淀粉样变性诊断的先兆诊断模式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的算法创建一个警报系统，可以帮助指导 为了尽早考虑 AL 淀粉样变性诊断，我们将执行以下具体操作。 目标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先兆诊断模式： 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 具有 MGUS+ 模式的医疗保险受益人的技术将在一年内进行纵向检查。 AL 淀粉样变性诊断之前五年内的时间点，比较 MGUS+ 与 已知的 AL 和 MGUS+ 与未知的 AL 以确定最能预测疾病的模式 2) 研究 预测 MGUS-AL 算法的性能将在 Medicare 数据集中进行内部评估， 整体和按种族分组（目标 2A）以及使用 MGUS+ 的 TriNetX 多中心 EHR 数据进行外部验证 所有年龄、种族和保险覆盖范围的患者（目标 2B）估计潜在未确诊人数。 患有 MGUS+ 的 AL 淀粉样变性患者 根据目标 1 中确定并在目标 2 中验证的模式，我们将 识别未确诊 AL 淀粉样变性高风险的受试者（目标 3A）并估计 2 年超额死亡率 以及我们的预警系统总体和按种族群体可能挽救的生命数量（目标 3B）。 研究为识别先兆诊断模式以诊断 AL 提供了前所未有的机会 早期淀粉样变性的一个重要预期结果是通过增加 AL 淀粉样变性来改善健康差异。 对患有 MGUS 和其他相关终末器官损伤风险较高的黑人进行诊断 新颖、严格且易于实施的早期预警系统具有 AL 淀粉样变性的潜力。 通过早期诊断来改变 AL 淀粉样变性患者的治疗结果。