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.

项目总结 轻链淀粉样变性是一种以器官功能障碍为特征的顽固性和致命性血液病 来自不溶性纤维沉积，源于克隆自由轻链，产生于单克隆性血球病。这个 由于心力衰竭，这种疾病在两年内的早期死亡率很高，达到40%-45%。晚期急性白血病患者 由于心功能不全，淀粉样变性在确诊后的初期有较高的发病率和死亡率。 尽管经历了多种症状并表现出疾病的迹象，许多患者还是被诊断出来 晚了，有时是几年，因为这些“先兆诊断”往往是非特异性的。观测数据也 这表明黑人更有可能被低估患有心脏淀粉样变性。单克隆 未确定意义的伽马病(MGUS)和阴燃多发性骨髓瘤(MGUS+)较多 在黑人中很常见，肥厚性心肌病和慢性肾脏疾病的流行也是如此， 这两种情况也都发生在AL淀粉样变性中。我们假设病人可以通过评估得到早期诊断 早于AL淀粉样变性诊断的先兆诊断模式。我们的应用程序试图创建一个 算法使用贝叶斯机器学习统计方法来创建一个可以帮助引导的警报系统 医生倾向于早期考虑AL淀粉样变性的诊断。我们将执行以下特定的 使用具有全国代表性的医疗保险数据的目标：1)识别与以下相关的先兆诊断模式 AL淀粉样变性的发生及基于贝叶斯机器学习的预测算法 MGUS+的医疗保险受益人的技术。将在一年内对图案进行纵向检查 在AL淀粉样变性诊断前五年的时间点对比MGUS+和 已知的AL和MGUS+，但没有已知的AL，以确定最可能预测疾病的模式。2)研究 预测MGUS-AL算法的性能。这将在联邦医疗保险数据集中进行内部评估， 总体和按种族分组(目标2A)和使用TriNetX多中心MGUS+电子病历数据的外部验证 所有年龄、种族和保险覆盖范围的患者(目标2B)。3)估计潜在未诊断患者的数量 伴MGUS+的淀粉样变性患者。根据目标1中确定并在目标2中验证的模式，我们将 确定未诊断的AL淀粉样变性的高危受试者(目标3A)，并估计超出的两年死亡率 以及我们的预警系统拯救的潜在生命数量，总体上和按种族分组(目标3B)。这 这项研究为确定诊断AL的先兆诊断模式提供了前所未有的机会 早期的淀粉样变性。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距 对已经有较高风险的MGUS和其他相关终末器官损害的黑人的诊断 患有AL淀粉样变性。这一新颖、严谨、易于实施的预警系统有可能 通过允许早期诊断来改变AL淀粉样变性患者的预后。