AICORE-kids: Artificial Intelligence COVID-19 Risk AssEssment for kids

AICORE-kids：针对儿童的人工智能 COVID-19 风险评估

• 批准号: 10320488
• 负责人: CARL E ALLEN
• 金额: $ 77.84万
• 依托单位: BAYLOR COLLEGE OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-01-01 至 2023-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10320488
• 关键词: 2019-nCoV; Admission activity; Adopted; Adoption; Algorithms; Ambulatory Care; Artificial Intelligence; Award; Biological Assay; Blood; COVID-19; COVID-19 diagnostic; COVID-19 patient; COVID-19 risk; COVID-19 severity; Caregivers; Characteristics; Child; Childhood; Clinical; Communities; Country; Data; Data Analyses; Data Collection; Data Coordinating Center; Development; Diagnostic Procedure; Differentiation Antigens; Disease; Emergency Situation; Ensure; FDA Emergency Use Authorization; Family; Future; Image; Immune; Individual; Infection; Inherited; Laboratories; Literature; Machine Learning; Methods; Monitor; Mucocutaneous Lymph Node Syndrome; Multisystem Inflammatory Syndrome in Children; Participant; Pathway interactions; Patients; Pediatric Hospitals; Phase; PhenX Toolkit; Physiologic Monitoring; Policies; Preparation; Process; Progressive Disease; Publishing; RADx Radical; Readiness; Records; Risk; Risk Assessment; Schools; Serology; Severities; Severity of illness; Speed; Spottings; Stratification; System; Testing; Texas; Time Series Analysis; TimeLine; Training; Translations; Validation; Work; artificial intelligence algorithm; assay development; base; biomedical referral center; biosignature; case-based; chemokine; cytokine; data integration; data standards; design; epigenomics; genetic variant; hemodynamics; heterogenous data; improved; inflammatory marker; interoperability; learning progression; learning strategy; machine learning algorithm; next generation; novel; patient population; pediatric patients; prognostication; programs; radiomics; repository; response; social health determinants; transcriptomics; transfer learning; treatment planning

This work is directed at characterizing pediatric COVID-19 and stratifying incoming patients by projected (future) disease severity. Such stratification has several implications: immediately improving treatment planning, and as disease mechanistic pathways are uncovered, directing treatment. Predicting future severity will inform the risks of outpatient treatment; to the patients themselves, their family, other caregivers/cohabitants, and to schools and employers. As varying levels of “reopening” are adopted across the country (and the world), such prognostication will inform policy on the handling of pediatric carriers in the community. Based on our preliminary analysis we assert that a combination of novel assays including quantitative serology inflammatory markers (cytokine/chemokine profiles, immune profiles), transcriptomics, epigenomics, longitudinal physiological monitoring, time series analysis, imaging, radiomics and clinical observation including social determinants of health, contains adequate information even at early stages of infection to stratify the disease and predict disease severity. We propose an artificial intelligence/machine learning approach to integrate this rich and heterogeneous dataset, characterize the spectrum of disease and identify biosignatures that predict severity in progressive disease. To facilitate translation of the approaches developed in this work to a wide user community, we incorporate a Translational Development function, to oversee the design-control process and ensure readiness of our methods for regulatory review. Incorporated into our timelines are appropriate regulatory milestones intended to conform with the Emergency Use Authorization (EUA) programs in effect for SARS- CoV-2 diagnostics.

这项工作旨在表征儿科COVID-19，并根据预计的 （未来）疾病严重程度。这种分层有几个含义：立即改善治疗计划， 因为疾病的机械途径被揭示，指导治疗。预测未来的严重程度将告知风险， 门诊治疗;患者本人、其家人、其他照顾者/同居者，以及学校， 雇主随着全国（乃至全世界）采取不同程度的“重新开放”， 告知社区处理儿科携带者的政策。根据我们的初步分析，我们断言， 一种新的测定方法的组合，包括定量血清学炎症标志物（细胞因子/趋化因子谱， 免疫谱），转录组学，表观基因组学，纵向生理监测，时间序列分析，成像， 放射组学和临床观察，包括健康的社会决定因素，即使在早期， 感染的阶段，以分层疾病和预测疾病的严重程度。我们提出一种人工智能/机器 学习方法来整合这一丰富和异构的数据集，表征疾病谱， 预测疾病进展严重程度的生物特征。为了便于翻译本报告中提出的方法， 工作，以广泛的用户社区，我们纳入了翻译开发功能，以监督设计控制 处理并确保我们的方法为监管审查做好准备。纳入我们的时间表是适当的 旨在符合SARS紧急使用授权（EUA）计划的监管里程碑- CoV-2诊断。

项目成果

Outcomes After SARS-CoV-2 Vaccination Among Children With a History of Multisystem Inflammatory Syndrome.

• DOI: 10.1001/jamanetworkopen.2022.4750
• 发表时间: 2022-03-01
• 期刊: JAMA network open
• 影响因子: 13.8
• 作者: Wisniewski M;Chun A;Volpi S;Muscal E;Sexson Tejtel SK;Munoz F;Vogel TP
• 通讯作者: Vogel TP

Social and Demographic Disparities in the Severity of Multisystem Inflammatory Syndrome in Children.

儿童多系统炎症综合征严重程度的社会和人口差异。

• DOI: 10.1097/inf.0000000000003511
• 发表时间: 2022
• 期刊: The Pediatric infectious disease journal
• 作者: Savorgnan,Fabio;Acosta,Sebastian;Alali,Alexander;Moreira,Axel;Annapragada,Ananth;Rusin,CraigG;Flores,Saul;Loomba,RohitS;Moreira,Alvaro
• 通讯作者: Moreira,Alvaro

Clinical performance of a semi-quantitative assay for SARS-CoV2 IgG and SARS-CoV2 IgM antibodies.

• DOI: 10.1016/j.cca.2020.09.023
• 发表时间: 2020-11
• 期刊: Clinica chimica acta; international journal of clinical chemistry
• 作者: Jung J;Garnett E;Jariwala P;Pham H;Huang R;Benzi E;Issaq N;Matzuk M;Singh I;Devaraj S
• 通讯作者: Devaraj S

Analytical and clinical performance of cPass neutralizing antibodies assay.

• DOI: 10.1016/j.clinbiochem.2021.09.008
• 发表时间: 2021-12
• 期刊: Clinical biochemistry
• 影响因子: 2.8
• 作者: Jung J;Rajapakshe D;Julien C;Devaraj S
• 通讯作者: Devaraj S

Population stratification enables modeling effects of reopening policies on mortality and hospitalization rates.

人口分层可实现重新开放政策对死亡率和住院率的建模影响。

• DOI: 10.1016/j.jbi.2021.103818
• 发表时间: 2021-07
• 期刊: Journal of biomedical informatics
• 影响因子: 4.5
• 作者: Huang T;Chu Y;Shams S;Kim Y;Annapragada AV;Subramanian D;Kakadiaris I;Gottlieb A;Jiang X
• 通讯作者: Jiang X