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

项目摘要 哮喘是儿童中最常见的慢性疾病，是世界上五种最严重的疾病之一。 我们虽然目前的护理和研究工作集中在症状控制和加重风险， 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（AIIC）（例如，肺炎球菌疾病、带状疱疹、阑尾炎和乳糜泻）。虽然AIIC 对哮喘儿童构成严重威胁，但它们在很大程度上被低估了，糖尿病就是明证 被广泛承认，但不太常见的慢性疾病的规模类似于哮喘。目前， AIIC的潜在机制尚不清楚。我们假设免疫衰老可能是相关的，因为AIIC 与免疫衰老的主要特征一致。AIIC没有临床定义，也没有合适的工具。 用于识别AIIC儿童。因此，不存在减轻独立投资公司风险和结果的战略。 解决这些知识差距取决于两个关键问题：（1）“哮喘儿童亚组如何 使用电子医疗记录在人群水平上识别AIIC风险增加？”（2）“什么 这些孩子的免疫参数特征？”实现这些目标是本提案的主要目标。为此，我们 目前的R 01研究成功地开发、验证和实施了自然语言处理（NLP）- 授权的计算表型算法，用于儿童哮喘的两个现有标准（预定 哮喘标准，PAC和哮喘预测指数，API）。NLP授权算法应用到1997-2007 奥姆斯特德县出生队列（OCBC）使我们能够描述AIIC增加的哮喘儿童亚组 同时符合NLP-PAC和NLP-API的儿童比例过高。 我们新的飞行员数据显示 哮喘可能加速免疫衰老，导致哮喘儿童亚组的AIIC。 在此更新提案中，我们针对同时满足NLP-PAC和NLP-API的这一亚组。创新和 对AIIC应用NLP授权的计算表型分析算法，以在人群中识别此类儿童 水平，然后表征其测量免疫衰老的免疫参数。在目标1中，我们将开发 用于识别和未识别AIIC的NLP授权的计算表型分析算法（NLP-AIIC）， 参加马约诊所1997-2016年OCBC的儿童， 然后评估NLP-AIIC在桑福德儿童医院的可移植性， 南达科他州苏福尔斯医院 .在目标2中，我们将通过新的NLP-AIIC识别和描述AIIC儿童， 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 目标3，我们将评估免疫参数测量随时间的变化（例如，适应性免疫减弱） 在我们的R 01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表征。 这项拟议的研究对于理解为什么一些哮喘儿童发展为AIIC，而另一些儿童则是不可或缺的。 不要。这些知识将使我们能够识别，管理，减轻和改善儿童中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.