Using the electronic health record to risk-stratify patients with systemic lupus erythematosus

使用电子健康记录对系统性红斑狼疮患者进行风险分层

• 批准号: 10152361
• 负责人: April Lynn Barnado
• 金额: $ 16.33万
• 依托单位: VANDERBILT UNIVERSITY MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-05-04 至 2023-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10152361
• 关键词: Adult; Affect; African American; Algorithms; Atrial Fibrillation; Autoantibodies; Autoimmune Diseases; Award; Big Data; Bioinformatics; Biological Markers; Biometry; Cardiovascular Diseases; Caucasians; Childhood; Classification; Clinical Data; Clinical Trials; Cluster Analysis; Code; Cohort Studies; Complement; Creatinine; Cyclophosphamide; Data; Data Analyses; Databases; Disease; Electronic Health Record; Environment; Female; Foundations; Funding; Genetic; Genotype; Goals; Health system; ICD-9; Immunosuppressive Agents; Intelligence; International; Joints; Kidney; Kidney Diseases; Knowledge; Learning; Link; Medical Records; Medicine; Mentors; Methodology; Methods; Modeling; Monitor; Morbidity - disease rate; Neoadjuvant Therapy; Nephritis; Outcome; Patients; Pharmaceutical Preparations; Physicians; Positioning Attribute; Pragmatic clinical trial; Precision Medicine Initiative; Prediction of Response to Therapy; Predisposition; Proteins; Publishing; Records; Research; Research Personnel; Rheumatology; Risk; Sample Size; Scanning; Scientist; Serum; Single Nucleotide Polymorphism; Skin; Specific qualifier value; Susceptibility Gene; System; Systemic Lupus Erythematosus; Testing; Time; Training; Work; base; biobank; bioinformatics resource; biomedical informatics; career; career development; clinical care; cohort; comorbidity; comparative effectiveness study; demographics; disease heterogeneity; follow-up; genetic information; genetic risk factor; improved; improved outcome; innovation; male; mortality; mycophenolate mofetil; novel; novel therapeutics; patient stratification; phenome; predictive modeling; response; risk stratification; risk variant; screening; targeted therapy trials; targeted treatment; tool; treatment response; urinary

PROJECT SUMMARY Systemic lupus erythematosus (SLE) is a heterogeneous, autoimmune disease with highly variable morbidity and mortality. Disease heterogeneity is a major challenge to both the clinical care of SLE patients and successful clinical trials. With heterogeneous diseases, identifying clusters results in improved classification, biomarkers, and targeted therapies. Electronic health records (EHRs) represent a powerful tool to identify clusters and risk-stratify SLE patients. We have published an algorithm that accurately identifies SLE patients in the EHR and have assembled a cohort of 2,376 SLE patients with a mean follow-up of 9 years. This data is linked to one of the world's largest biobanks, BioVU, with 400 SLE patients already genotyped. The EHR and BioVU allow for novel methods such as phenome-wide association studies (PheWAS) that use billing codes for a comprehensive scan of the entire EHR. We have performed the first PheWAS in SLE. Our overall goal is to use readily available EHR data in an intelligent way to improve outcomes in SLE patients. We hypothesize that SLE is composed of multiple clusters of patients with different disease courses and comorbidities, and our EHR-based methodology that incorporates genetic information will serve as novel tools to risk-stratify SLE patients. Using PheWAS in Aim 1, we will uncover differences in comorbidities between SLE patients with and without autoantibodies and with and without pre-specified SLE susceptibility single nucleotide polymorphisms (SNPs). In Aim 2, we will perform clustering analyses using demographics, autoantibodies, comorbidities, and SLE SNPs to risk-stratify SLE patients. We will assess renal outcomes, survival, and treatments received among the clusters. In Aim 3, we will evaluate treatment response to induction therapy for SLE nephritis in the EHR and compare to published outcomes. These aims are the necessary first steps to risk-stratify SLE patients and define treatment response in the EHR to then build models to predict treatment response and conduct EHR-based pragmatic clinical trials of targeted therapies. Additional mentored training and didactic coursework in genetics, biomedical informatics, and biostatistics will advance Dr. Barnado's career. Vanderbilt serves as an exceptional environment to support Dr. Barnado's transition to an independent physician scientist. Notable strengths include the Synthetic Derivative (SD), a de-identified EHR with over 2.7 million subjects, and BioVU, a genetic biobank linked to the SD. The Department of Medicine and Division of Rheumatology are in support of Dr. Barnado's career. Her mentors, Drs. Crofford and Denny, are internationally recognized in rheumatology and biomedical informatics with successful track records of mentoring. With Vanderbilt's institutional commitment to young investigators and expertise in biomedical informatics, Dr. Barnado's will successfully leverage her innovative proposal to independent R01 funding.

项目摘要 系统性红斑狼疮（SLE）是一种异质性自身免疫性疾病，发病率差异很大 and mortality.疾病异质性是SLE患者临床护理的主要挑战， 成功的临床试验。对于异质性疾病，识别聚类导致改进的分类， 生物标志物和靶向治疗。电子健康记录（EHR）是一个强大的工具， 聚类和风险分层SLE患者。我们已经发布了一种算法，可以准确识别SLE患者 在EHR中，我们收集了2,376例SLE患者，平均随访9年。该数据 与世界上最大的生物库之一BioVU相关联，已有400名SLE患者的基因分型。EHR和 BioVU允许新的方法，如全表型关联研究（PheWAS）， 全面扫描整个电子病历我们已经在SLE中进行了第一次PheWAS。 我们的总体目标是以智能的方式使用现成的EHR数据来改善SLE患者的预后。 我们假设SLE是由不同病程的多组患者组成， 合并症，我们的EHR为基础的方法，结合遗传信息将作为新的工具 对SLE患者进行风险分层。 在目标1中使用PheWAS，我们将揭示SLE患者与非SLE患者之间合并症的差异， 自身抗体以及有和没有预先指定的SLE易感性单核苷酸多态性（SNP）。 在目标2中，我们将使用人口统计学、自身抗体、合并症和SLE进行聚类分析 SNP对SLE患者进行风险分层。我们将评估肾脏结局、生存率和接受的治疗， 集群。在目标3中，我们将在EHR中评估SLE肾炎诱导治疗的治疗反应 并与已发表的结果进行比较。 这些目标是在EHR中对SLE患者进行风险分层和确定治疗反应的必要的第一步 然后建立模型来预测治疗反应，并进行基于EHR的针对性临床试验。 治疗在遗传学，生物医学信息学， 生物统计学将推动巴纳多博士的事业 范德比尔特作为一个特殊的环境，以支持博士巴纳多的过渡到一个独立的 医学科学家值得注意的优势包括合成衍生物（SD），这是一种去识别化的EHR，具有超过2.7 和BioVU，一个与SD相关的基因生物库。医学系和 流变学是巴纳多博士事业的支柱。她的导师克罗夫德博士和丹尼博士 在流变学和生物医学信息学方面获得国际认可， 指导。凭借范德比尔特对年轻研究人员的机构承诺和生物医学领域的专业知识， 信息学，博士巴纳多的将成功地利用她的创新建议，以独立的R 01资金。