Prediction of risk of disability worsening and inflammatory disease activity in MS utilizing multimodal prediction algorithms

利用多模态预测算法预测 MS 中残疾恶化和炎症性疾病活动的风险

• 批准号: 10447083
• 负责人: Elias S Sotirchos
• 金额: $ 16.74万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-01 至 2025-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10447083
• 关键词: Algorithms; Architecture; Award; Benign; Big Data; Biological Markers; Biostatistical Methods; Blood specimen; Brain; Characteristics; Clinical; Clinical Trials; Computerized Medical Record; Counseling; Data; Data Set; Decision Making; Demographic Factors; Development; Devices; Digit structure; Disease; Disease Outcome; Elements; Enhancing Lesion; Enrollment; Exhibits; Future; Goals; Hand; Health; Health system; Image; Individual; Inflammatory; Laboratories; Learning; Lesion; Light; Magnetic Resonance Imaging; Manuals; Measures; Medical; Modality; Modeling; Monitor; Multiple Sclerosis; Outcome; Participant; Patient Care; Patients; Persons; Phenotype; Population; Predictive Value; Prognostic Factor; Recording of previous events; Relapse; Reporting; Research; Research Personnel; Risk Assessment; Risk Factors; Serum; Severity of illness; Standardization; Stratification; Technology; Testing; Therapeutic; Training; Universities; Validation; Visit; Walking; base; biobank; career; clinical practice; clinical predictors; clinical risk; clinically relevant; comorbidity; demographics; design; dexterity; disability; disability risk; follow-up; foot; gray matter; high dimensionality; insight; inter-individual variation; lifestyle factors; machine learning method; multimodality; neurofilament; neuroimaging; novel; novel diagnostics; performance tests; personalized medicine; prediction algorithm; processing speed; prognostic; response; risk prediction; statistical and machine learning; tool; walking speed

Project Summary: Multiple sclerosis (MS) exhibits a markedly heterogeneous and unpredictable course, with a clinical spectrum ranging from very mild forms of the disease in some patients (often termed “benign MS”) to an aggressive disease course with rapid accumulation of disability in others. Furthermore, there appears to be significant inter-individual variability in the responses to the many available disease-modifying therapies (DMT). A variety of factors have been proposed to be associated with the disease course in MS, including demographics, lifestyle factors, clinical characteristics, MRI-derived measures, and elevated serum neurofilament light chain (NfL), among others. It remains unclear though if these factors are complementary or redundant in their predictive value. Moreover, there is a lack of validated tools to accurately predict, at an individual level, future inflammatory disease activity or disability worsening. This is largely due to the lack of datasets with sufficient size, breadth and representativeness. The use of electronic medical records (EMR) has dramatically increased in recent years, enabling the capture of a wide variety of data measures from large numbers of individuals. Furthermore, the development and refinement of statistical machine learning methods has revolutionized the approach to analysis of such high-dimensional datasets. This background provides a unique opportunity to leverage and analyze “big data” in order to develop clinical risk prediction algorithms and personalized medicine tools in MS. Multiple Sclerosis Partners Advancing Technology and Health Solutions (MS PATHS) is a network of 10 MS centers that have standardized elements of their clinical practice to implement a centralized health information exchange architecture. MS PATHS was designed around the concept of a learning health system (LHS), merging research with ongoing patient care by collecting standardized clinical and imaging data during routine medical visits. As of August, 2019, >15,000 patients have opted to participate in MS PATHS. Thus, the MS PATHS network is an ideal, deeply phenotyped, “real-world”, large population of people with MS, in which clinically relevant predictive algorithms may be developed and validated. The goal of the current project is to develop and validate multi-modal predictive algorithms of clinically relevant disease outcomes in MS. We hypothesize that integrating a wide variety of potential predictors, including demographics, clinical characteristics (including current/historical DMT use), comorbidities/lifestyle factors, MRI- derived measures and laboratory data (including serum NfL) will lead to the development and validation of algorithms that may accurately predict future clinical disability worsening and inflammatory disease activity. Furthermore, this approach will allow the assessment of the individual contribution of specific predictors to the developed predictive algorithms, and may aid with the identification of novel risk factors of disease severity in MS.

项目概要： 多发性硬化症（MS）表现出明显的异质性和不可预测的过程，与临床 从一些患者中非常轻微的疾病形式（通常称为“良性MS”）到 侵袭性病程，在其他人身上迅速累积残疾。此外，似乎有 对许多可用的疾病修饰疗法（DMT）的反应存在显著的个体间差异。 已提出多种因素与MS的病程相关，包括人口统计学， 生活方式因素、临床特征、MRI衍生指标和血清神经丝轻链升高 (NfL)等等。但目前尚不清楚这些因素在其预测中是互补还是多余 值此外，缺乏有效的工具来准确预测，在个人水平上，未来的炎症， 疾病活动或残疾恶化。这在很大程度上是由于缺乏足够大小、广度和 代表性。近年来，电子病历（EMR）的使用急剧增加， 使得能够从大量个体捕获各种各样的数据测量。而且 统计机器学习方法的发展和改进彻底改变了分析方法 此类高维数据集的。这一背景提供了一个独特的机会，利用和分析“大 数据”，以便在MS中开发临床风险预测算法和个性化医疗工具。 多发性硬化症合作伙伴推进技术和健康解决方案（MS PATHS）是一个由10个 MS中心已将其临床实践的要素标准化， 信息交换架构。MS PATHS是围绕学习健康系统的概念而设计的 (LHS)，通过收集标准化的临床和成像数据，将研究与正在进行的患者护理相结合， 例行医疗检查。截至2019年8月，超过15，000名患者选择参加MS PATHS。因此 MS PATHS网络是一个理想的、深度表型化的、“真实世界”的、大量MS患者群体，其中 可以开发和验证临床相关的预测算法。 当前项目的目标是开发和验证临床上的多模态预测算法。 我们假设，整合各种潜在的预测因子，包括 人口统计学、临床特征（包括当前/历史DMT使用）、合并症/生活方式因素、MRI- 衍生的测量和实验室数据（包括血清NfL）将导致开发和验证 可以准确预测未来临床残疾恶化和炎性疾病活动的算法。 此外，这一方法将允许评估特定预测因子对预测结果的个别贡献。 开发了预测算法，并可能有助于识别疾病严重程度的新风险因素， 女士