Identification and characterization of children with asthma-associated comorbidities through computational and immune phenotyping

通过计算和免疫表型分析患有哮喘相关合并症的儿童的识别和特征分析

基本信息

批准号：
10337267
负责人：
YOUNG J JUHN
金额：
$ 79.14万
依托单位：
MAYO CLINIC ROCHESTER
依托单位国家：
美国
项目类别：
财政年份：
2015
资助国家：
美国
起止时间：
2015-04-01 至 2025-01-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10337267
关键词：
Address Adult Algorithms Antigens Appendicitis Asthma Autoimmune Diseases Birth CD3 Antigens Caregivers Caring Celiac Disease Cell Cycle Centers for Disease Control and Prevention (U.S.)Characteristics Child Childhood Asthma Chronic Disease Clinic Clinical Clinical Research Cohort Studies Communicable Diseases Computerized Medical Record County Cross-Sectional Studies Data Development Diabetes Mellitus Disease Early identification Enrollment Family history of Future Goals Health Herpes zoster disease Immune Immunity Impairment Infection Inflammatory Knowledge Longitudinal Studies Measurement Measures Memory Natural Immunity Natural Language Processing Outcome Patient-Focused Outcomes Patients Pediatric Hospitals Phenotype Pneumococcal Infections Pneumococcal Pneumonia Pneumonia Population Population Surveillance Records Reproducibility Research Research Design Research Personnel Respiratory Tract Infections Rheumatoid Arthritis Risk Risk Factors Sampling Sioux Falls Site Subgroup Symptoms T-Lymphocyte Time Time Management United States Work adaptive immunity asthmatic base cohort comorbidity cost empowered immunosenescence improved improved outcome indexing inhibitor meetings phenotyping algorithm portability prevent pulmonary function respiratory response senescence tool

项目摘要

PROJECT SUMMARY Asthma, the most common chronic disease among children, is one of the five most burdensome diseases in the US. While current care and research efforts focus on symptom control and exacerbation risk, children with asthma may suffer from infectious and inflammatory diseases, ie, asthma-associated infectious and inflammatory disease comorbidities (AIICs) (eg, pneumococcal disease, herpes zoster, appendicitis, and celiac disease). Although AIICs pose serious threats to children with asthma, they are largely under-recognized, as evidenced by diabetes mellitus being widely recognized but a less common chronic illness with a magnitude similar to that for asthma. Presently, the mechanisms underlying AIICs are unknown. We postulate immunosenescence might be related, as AIICs coincide with cardinal immunosenescence features. AIICs are not clinically defined and a suitable tool has not been developed to identify children with AIICs. Thus, no strategies mitigating AIICs risks and outcomes exist. Addressing these knowledge gaps depends upon two key questions: (1) “How can asthmatic children subgroups with increased AIICs risk be identified at a population level using electronic medical records?” and (2) “What immune parameters characterize such children?” Answering these is this proposal's primary goal. To this end, our current R01 study successfully developed, validated, and implemented natural language processing (NLP)- empowered computational phenotyping algorithms for two existing criteria for childhood asthma (Predetermined Asthma Criteria, PAC and Asthma Predictive Index, API). NLP-empowered algorithm application to the 1997-2007 Olmsted County Birth Cohort (OCBC) enabled us to profile a subgroup of children with asthma at increased AIICs risk, disproportionately represented by children who met both NLP-PAC and NLP-API. Our new pilot data suggest asthma potentially accelerates immunosenescence leading to AIICs in a subgroup of asthmatic children. In this renewal proposal, we target this subgroup who meet both NLP-PAC and NLP-API. We will develop and apply NLP-empowered computational phenotyping algorithms for AIICs to identify such children at a population level, then characterize their immune parameters measuring immunosenescence. In Aim 1, we will develop new NLP-empowered computational phenotyping algorithms for recognized and unrecognized AIICs (NLP-AIIC) for children enrolled in Mayo Clinic `s 1997-2016 OCBC, then assess portability of NLP-AIIC at Sanford Children's Hospital, Sioux Falls, SD . In Aim 2, we will identify and characterize children with AIICs through new NLP-AIIC and NLP algorithms for asthma status, by utilizing clinical and immune parameters to measure immunosenescence. In Aim 3, we will assess changes (eg, waning adaptive immunity) over time in immune parameters measuring immunosenescence for 300 children in our R01 study, re-enrolling them for further characterization. This proposed study is indispensable to understanding why some children with asthma develop AIICs, while others do not. This knowledge will allow us to identify, manage, mitigate, and improve outcomes for AIICs among children.

项目摘要哮喘是儿童中最常见的慢性疾病，是五种最朴实的疾病之一我们。尽管当前的护理和研究工作着重于症状控制和加剧风险，但患有哮喘的儿童可能患有感染性和炎症性疾病，即与哮喘相关的感染性和炎症性疾病合并症（AIICS）（例如，肺炎球菌疾病，带状疱疹，阑尾炎和腹腔疾病）。虽然aiics 对哮喘儿童构成严重威胁，在很大程度上被低估了，如糖尿病所证明被广泛认可，但不常见的慢性疾病与哮喘相似。目前， AIIC的基础机制尚不清楚。我们假设免疫衰老可能是相关的与基本免疫衰老特征一致。 AIICS没有临床定义，合适的工具没有开发旨在识别副自力儿童。这是没有策略来减轻AIIC的风险和结果。解决这些知识差距取决于两个关键问题：（1）“哮喘儿童如何使用电子病历在人群中确定AIICS风险增加？”和（2）“什么免疫参数是这种孩子的特征吗？当前的R01研究成功开发，验证和实施了自然语言处理（NLP） - 赋予两个现有童年哮喘标准的计算表型算法（预定）哮喘标准，PAC和哮喘预测指数，API）。 NLP授权的算法应用1997-2007 Olmsted County出生队列（OCBC）使我们能够在AIICS中概述一群哮喘子群风险不成比例地由遇到NLP-PAC和NLP-API的孩子代表。我们的新试点数据建议哮喘可能会加速免疫衰老，从而导致哮喘儿童亚组的疾病。在此续签建议中，我们针对符合NLP-PAC和NLP-API的亚组。我们将发展和应用NLP授权的计算表型算法，以识别人口的此类儿童水平，然后表征其免疫过敏。在AIM 1中，我们将开发新的 NLP授权的计算表型算法，用于公认和未识别的AIIC（NLP-aiic）梅奥诊所的儿童1997-2016 OCBC，然后评估桑福德儿童的NLP-aiic的可移植性医院，SIOUX FALLS，SD 。在AIM 2中，我们将通过新的NLP-aiic和通过使用临床和免疫参数来测量免疫力，NLP的哮喘状态算法。在 AIM 3，我们将随着时间的流逝评估变化（例如，使适应性免疫组织化学减弱）测量在我们的R01研究中，300名儿童的免疫衰老，重新注册他们以进一步表征。这项拟议的研究是必不可少的，要理解为什么有些患有哮喘的儿童会发展AIIC，而另一些则是不要。这些知识将使我们能够识别，管理，减轻和改善儿童副奏的结果。

项目成果

期刊论文数量（37）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Identification of asthma control factor in clinical notes using a hybrid deep learning model.

DOI：
10.1186/s12911-021-01633-4
发表时间：
2021-11-09
期刊：
BMC medical informatics and decision making
影响因子：
3.5
作者：
Agnikula Kshatriya BS;Sagheb E;Wi CI;Yoon J;Seol HY;Juhn Y;Sohn S
通讯作者：
Sohn S

Rural-urban health disparities for mood disorders and obesity in a midwestern community.

DOI：
10.1017/cts.2020.27
发表时间：
2020-03-24
期刊：
Journal of clinical and translational science
影响因子：
2.6
作者：
Patten CA;Juhn YJ;Ryu E;Wi CI;King KS;Bublitz JT;Pignolo RJ
通讯作者：
Pignolo RJ

Assessing health disparities in children using a modified housing-related socioeconomic status measure: a cross-sectional study.

使用修改后的与住房相关的社会经济地位指标评估儿童的健康差异：一项横断面研究。

DOI：
10.1136/bmjopen-2016-011564
发表时间：
2016
期刊：
BMJ open
影响因子：
2.9
作者：
Ryu,Euijung;Wi,Chung-Il;Crow,SheriS;Armasu,SebastianM;Wheeler,PhilipH;Sloan,JeffA;Yawn,BarbaraP;Beebe,TimothyJ;Williams,ArthurR;Juhn,YoungJ
通讯作者：
Juhn,YoungJ

Role of Geographic Risk Factors in COVID-19 Epidemiology: Longitudinal Geospatial Analysis.