Improving Diagnosis of Multiple Sclerosis Through the Integration of Novel Imaging and Laboratory Biomarkers

通过整合新型成像和实验室生物标志物改善多发性硬化症的诊断

• 批准号: 10322412
• 负责人: Andrew J Solomon
• 金额: $ 13.98万
• 依托单位: UNIVERSITY OF VERMONT & ST AGRIC COLLEGE
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-01-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10322412
• 关键词: Algorithms; Appearance; Atrophic; Binding; Biological Assay; Biological Markers; Blood Tests; C-Peptide; Central Vein; Clinical; Clinical/Radiologic; Computer Models; Data; Data Collection; Development; Diagnosis; Diagnostic; Diagnostic Procedure; Differential Diagnosis; Disease; Early Diagnosis; Enrollment; Erythrocytes; Evaluation; Evolution; Functional disorder; Funding; Gene Expression; Goals; Gold; Health Care Costs; Image; Inflammatory; Laboratories; Lesion; Light; Liquid substance; Machine Learning; Magnetic Resonance Imaging; Medical; Mentors; Methodology; Methods; Morbidity - disease rate; Multiple Sclerosis; Myelin; Nerve Degeneration; Neurodegenerative Disorders; Neuronal Injury; Participant; Pathogenesis; Patients; Peptides; Pilot Projects; Process; Prospective cohort; Prospective cohort study; Qualitative Research; RNA; Rare Diseases; Risk; Sensitivity and Specificity; Serum; Specificity; Symptoms; Syndrome; Techniques; Testing; Thalamic structure; Time; Training; United States National Institutes of Health; Untranslated RNA; Whole Blood; accurate diagnosis; career development; clinical application; clinical diagnosis; clinical diagnostics; clinical heterogeneity; clinical phenotype; clinical practice; cohort; diagnostic accuracy; diagnostic criteria; diagnostic strategy; disability; disease heterogeneity; experience; gray matter; improved; machine learning method; multimodality; multiple sclerosis patient; neurofilament; neuroinflammation; novel; novel diagnostics; novel imaging technique; prevent; prospective; radiological imaging; recruit; research clinical testing; skills; specific biomarkers; support vector machine; white matter

Project Summary/Abstract: The diagnosis of multiple sclerosis (MS) remains challenging due to its clinical heterogeneity and lengthy differential diagnosis. The incorrect assignment of a diagnosis of MS occurs in approximately 9% of newly evaluated patients and is associated with considerable clinically important, and avoidable, medical risk, morbidity, and healthcare costs. At the same time studies have demonstrated that many patients encounter a significant diagnostic delay prior to confirmation of a correct diagnosis of MS. In such patients early and accurate diagnosis of MS can result in prompt initiation of disease modifying therapy and consequent preventable disability. MS remains a clinical diagnosis and diagnostic criteria for MS are revised periodically, including most recently in 2017. Since implementation of the 2017 criteria, like all prior revisions, will continue to rely on subjective clinical and radiological assessments for its fulfillment, misdiagnosis will remain a risk. New objective, automated, and clinically applicable approaches to MS diagnosis are needed. Recent preliminary data from cross-sectional pilot studies in patients with established diagnoses have shown promise for three new radiographic and three new laboratory methods to differentiate MS from other disorders. The present study will evaluate these six methods for the first time in a prospective cohort of 125 patients undergoing an initial evaluation for MS at an academic MS subspecialty center. The specificity and sensitivity of each method will be compared to fulfillment of 2017 MS diagnostic criteria at the time of initial clinical evaluation. Using diagnostic thresholds developed from this analysis, a two year post-enrollment analysis will also be performed in participants who did not meet 2017 criteria initially but did so during the subsequent two year interval to determine if the study methods could have predicted a diagnosis of MS earlier in such patients. The use of a multimodal and machine-learning approach to evaluate the integration of each of these six new methods which represent different aspects of MS neuroinflammatory and neurodegenerative processes will also be performed during each analysis, and such a combination of radiographic and laboratory methodology may provide superior diagnostic accuracy compared to any given method alone. Planned collaborative career development, mentoring, and advising activities will facilitate acquisition of specific advanced quantitative and qualitative research skills necessary to develop and coordinate collection of data for this large prospective cohort study to rigorously evaluate new diagnostic methods for MS and incorporate machine learning analyses. Successful completion of this study will provide experience and skills necessary to move the field of MS diagnosis forward through a planned prospective multicenter NIH R01 funded study.

项目摘要/摘要： 多发性硬化(MS)的诊断仍然具有挑战性，因为它的临床异质性和 冗长的鉴别诊断。多发性硬化症的错误诊断发生在大约9%的 新评估的患者，并与相当重要的临床和可避免的医疗风险有关， 发病率和医疗费用。与此同时，研究表明，许多患者遇到了 这类患者在确认MS的正确诊断之前，早期和 对多发性硬化症的准确诊断可导致迅速启动疾病调整治疗并随后 可预防的残疾。MS仍然是一种临床诊断，MS的诊断标准会定期修订， 最近一次是在2017年。由于将继续执行2017年标准，就像以前的所有修订一样 如果依靠主观的临床和放射学评估来实现这一点，误诊仍将是一个风险。 需要新的客观的、自动化的和临床适用的多发性硬化症诊断方法。近期 在确诊的患者中进行的横断面先导研究的初步数据显示了希望 三种新的放射学方法和三种新的实验室方法来区分MS和其他疾病。这个 本研究将在125名患者的前瞻性队列中首次对这六种方法进行评估。 在学术MS分专业中心接受MS的初步评估。特异度和敏感性 将每种方法的使用情况与初始临床时2017年多发性硬化症诊断标准的满足情况进行比较 评估。使用从该分析得出的诊断阈值，注册后两年的分析将 也适用于最初未达到2017年标准，但在随后的两年中达到标准的参与者 每隔一年确定研究方法是否可以更早地预测此类患者的多发性硬化症诊断。 使用多模式和机器学习方法来评估这六个新的 代表多发性硬化神经炎性和神经变性过程的不同方面的方法将 也可以在每次分析过程中进行，这种射线照相和实验室方法的结合 与任何给定的方法相比，可以提供更好的诊断准确性。 计划的协作式职业发展、指导和咨询活动将促进获得 开发和协调收集数据所需的特定高级定量和定性研究技能 为这项大型前瞻性队列研究提供数据，以严格评估MS和 结合机器学习分析。成功完成这项研究将提供经验和技能 有必要通过规划的多中心NIH R01推进多发性硬化症诊断领域 资助的研究。