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

项目总结 哮喘是儿童中最常见的慢性病，是世界上负担最重的五种疾病之一。 我们。虽然目前的护理和研究努力集中在症状控制和恶化风险上，但患有哮喘的儿童 可能患有感染性和炎症性疾病，即哮喘相关的感染性和炎症性疾病 合并症(AIICs)(如肺炎球菌病、带状疱疹、阑尾炎和乳糜泻)。尽管AIIC 对患有哮喘的儿童构成严重威胁，他们在很大程度上被低估，糖尿病就是明证。 被广泛认识，但不太常见的慢性病，其严重程度与哮喘相似。目前， AIIC的潜在机制尚不清楚。我们推测免疫衰老可能与AIICs有关 符合主要的免疫衰老特征。AIIC没有在临床上定义，也没有合适的工具 开发用于识别患有AIIC的儿童。因此，不存在减轻AIICs风险和后果的战略。 解决这些知识差距取决于两个关键问题：(1)“哮喘儿童亚群如何 随着AIIC风险的增加，是否可以使用电子医疗记录在人群层面上进行识别？“和(2)“什么” 免疫参数是这样的孩子的特征吗？回答这些问题是这项提案的主要目标。为此，我们的 当前的R01研究成功地开发、验证和实施了自然语言处理(NLP)- 为儿童哮喘的两个现有标准(预先确定)提供支持的计算表型算法 哮喘标准、PAC和哮喘预测指数(API)。NLP授权的算法在1997-2007年间的应用 奥姆斯特德县出生队列(OCBC)使我们能够描述AIIC增加的哮喘儿童亚群 风险，不成比例地由同时满足NLP-PAC和NLP-API的儿童所代表。 我们新的飞行员数据表明 哮喘可能加速免疫衰老，导致哮喘儿童亚组中的AIICs。 在这份续签提案中，我们针对同时满足NLP-PAC和NLP-API的这一子组。我们将发展和 将NLP授权的计算表型算法应用于AIIC以识别人群中的此类儿童 水平，然后表征他们的免疫参数衡量免疫衰老。在目标1中，我们将开发新的 NLP支持的已识别和未识别AIIC的计算表型算法(NLP-AIIC) 梅奥诊所1997-2016年度华侨城的孩子们 然后在桑福德儿童医院评估NLP-AIIC的便携性 康涅狄格州苏福尔斯医院 。在目标2中，我们将通过新的NLP-AIIC和 哮喘状态的NLP算法，通过利用临床和免疫参数来测量免疫衰老。在……里面 目标3，我们将评估免疫参数测量中随时间的变化(例如，适应性免疫减弱)。 在我们的R01研究中，我们对300名儿童进行了免疫衰老研究，重新招募他们进行进一步的鉴定。 这项拟议的研究对于理解为什么一些哮喘儿童会患上AIIC，而另一些儿童则是如此，是不可或缺的 不要这样做。这些知识将使我们能够识别、管理、缓解和改善儿童AIIC的结果。

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.