Studying exceptional treatment non-responders and genetics to predict treatment response in rheumatoid arthritis
研究特殊治疗无反应者和遗传学以预测类风湿关节炎的治疗反应
基本信息
- 批准号:10301407
- 负责人:
- 金额:$ 25.15万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2021
- 资助国家:美国
- 起止时间:2021-07-01 至 2023-06-30
- 项目状态:已结题
- 来源:
- 关键词:AddressAlgorithmsAntirheumatic AgentsArthritisAutoimmuneBiologicalClinicClinicalClinical DataCodeCohort StudiesDataDiseaseDisease-Modifying Second-Line DrugsDrug CostsDrug PrescriptionsElectronic Health RecordEpidemiologyFutureGene ClusterGeneticGenetic ModelsGenetic RiskGenotypeHeterogeneityInflammationInflammatoryInflammatory ArthritisInsuranceKnowledgeLinkMachine LearningMeasurementMedicalMedical GeneticsMedical RecordsMissionModelingNational Institute of Arthritis and Musculoskeletal and Skin DiseasesNatural Language ProcessingPatient CarePatientsPharmaceutical PreparationsPharmacotherapyPhenotypePrediction of Response to TherapyPublishingQuality of lifeResearchRheumatoid ArthritisRisk-TakingSpondylarthropathiesSubgroupTNF geneTestingTextTimeVisitarthropathiesbasebiobankclinical careclinical predictorscohortcostdemographicsdesignelectronic dataelectronic structureepidemiological modelepidemiology studyfollow-upgenetic predictorsgenetic risk factorgenomic dataimprovedinfection riskinhibitor/antagonistinnovationjoint inflammationnew therapeutic targetnon-drugnovelnovel strategiesoptimal treatmentspatient screeningpatient subsetspredicting responseresponserisk variantsmall moleculesuccesssupervised learningtreatment response
项目摘要
PROJECT SUMMARY/ABSTRACT
A major challenge in caring for patients with rheumatoid arthritis (RA) is determining the optimal therapy.
Several effective biologic disease modifying anti-rheumatic drugs (bDMARDs) are available for RA, reflecting
both advances in therapy, and the heterogeneity of RA; subsets of patients respond while others do not. Prior
studies focused on patients with a good response to tumor necrosis factor inhibitor (TNFi), the most common
bDMARD, with limited success in finding predictors that can be used in clinical care. This proposal seeks to
address that gap in knowledge by taking a different direction. The objective of this study is to focus on
exceptional bDMARD non-responders, defining and characterizing patients who have been on ≥3 classes of
bDMARDs for RA. We will test whether data available in clinical electronic health record data (EHR) or
genomic data can identify exceptional non-responders from TNFi responders. In Aim 1, we leverage data from
an EHR cohort of ~16K RA patients to determine clinical factors associated with exceptional non-response
using traditional epidemiologic approaches. As well, we will apply approaches using machine learning and
topic modeling that will enable us to evaluate the predictiveness of a broader range of features. Examples of
features include billing codes, prescriptions, and medical concepts extracted from text notes using natural
language processing. In Aim 2, we will test whether RA genetic risk factors available in a subset of patients in
Aim 1, and those of other inflammatory arthritides, e.g. axial spondyloarthropathy, can predict exceptional non-
response to bDMARD therapy. As part of aim 2, we will also incorporate any predictive clinical factors
identified in Aim 1 through the traditional or topic modeling approach. The overarching hypothesis is that the
exceptional non-responders may be less “RA-like” than patients who respond to TNFi, with fewer RA genetic
risk alleles and classic RA features from the narrative notes. This definition provides a new way to sub-
phenotype RA, focusing on those that will have a poor response to therapy. This study is significant because a
screen will be helpful not only in the clinic but can also identify patients to target for future studies of novel drug
targets. This approach is innovative because it considers contemporary data where patients now have more
“opportunity” to fail 3 classes of bDMARDs, where in the past there were only a limited number available.
These data will be examined both using traditional epidemiologic models and newer approaches such as topic
modeling that can integrate a broader range of data types. Finally, this proposal is designed to anticipate a
time when patients will come for their visit with genetic data as part of their medical record.
项目总结/文摘
项目成果
期刊论文数量(0)
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{{ truncateString('TIANXI CAI', 18)}}的其他基金
Bridging clinical trial and real-world data via machine learning to advance rheumatoid arthritis treatment strategies
通过机器学习连接临床试验和真实世界数据,以推进类风湿性关节炎的治疗策略
- 批准号:
10652251 - 财政年份:2022
- 资助金额:
$ 25.15万 - 项目类别:
Bridging clinical trial and real-world data via machine learning to advance rheumatoid arthritis treatment strategies
通过机器学习连接临床试验和真实世界数据,以推进类风湿性关节炎的治疗策略
- 批准号:
10339668 - 财政年份:2022
- 资助金额:
$ 25.15万 - 项目类别:
Semi-supervised Approaches to Denoising Electronic Health Records Data for Risk Prediction
用于风险预测的电子健康记录数据去噪半监督方法
- 批准号:
10453558 - 财政年份:2021
- 资助金额:
$ 25.15万 - 项目类别:
Studying exceptional treatment non-responders and genetics to predict treatment response in rheumatoid arthritis
研究特殊治疗无反应者和遗传学以预测类风湿关节炎的治疗反应
- 批准号:
10430273 - 财政年份:2021
- 资助金额:
$ 25.15万 - 项目类别:
Semi-supervised Approaches to Denoising Electronic Health Records Data for Risk Prediction
用于风险预测的电子健康记录数据去噪半监督方法
- 批准号:
10185327 - 财政年份:2021
- 资助金额:
$ 25.15万 - 项目类别:
Semi-supervised Approaches to Denoising Electronic Health Records Data for Risk Prediction
用于风险预测的电子健康记录数据去噪半监督方法
- 批准号:
10617781 - 财政年份:2021
- 资助金额:
$ 25.15万 - 项目类别:
Robust Approaches to the Development and Evaluation of Prognostic Classifiers
预后分类器开发和评估的稳健方法
- 批准号:
8181612 - 财政年份:2007
- 资助金额:
$ 25.15万 - 项目类别:
Robust Approaches to the Development and Evaluation of Prognostic Classifiers
预后分类器开发和评估的稳健方法
- 批准号:
7356026 - 财政年份:2007
- 资助金额:
$ 25.15万 - 项目类别:
Robust Approaches to the Development and Evaluation of Prognostic Classifiers
预后分类器开发和评估的稳健方法
- 批准号:
7185413 - 财政年份:2007
- 资助金额:
$ 25.15万 - 项目类别:
Robust Approaches to the Development and Evaluation of Prognostic Classifiers
预后分类器开发和评估的稳健方法
- 批准号:
8501533 - 财政年份:2007
- 资助金额:
$ 25.15万 - 项目类别:
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