AICORE-kids: Artificial Intelligence COVID-19 Risk AssEssment for kids
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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.
本研究旨在明确儿童新型冠状病毒肺炎（COVID-19）的疾病特征，并根据预测的未来病情严重程度对接诊患儿进行分层管理。此类病情分层具有多重意义：既可即时优化治疗方案，又可随着疾病机制通路的逐步阐明，为临床治疗提供精准指导。对病情严重程度的前瞻性预测，能够明确门诊治疗的潜在风险——这一风险不仅涉及患儿本人、其家属、其他照护者及共同居住者，还关联到学校与用人单位的健康管理。随着全球各国陆续推行不同程度的“复工复学”政策，此类预后评估可为社区内儿童病毒携带者的管理政策提供科学依据。基于初步分析结果，我们认为，将多项新型检测技术相结合——包括定量血清学炎症标志物（细胞因子/趋化因子谱、免疫细胞谱）、转录组学（transcriptomics）、表观基因组学（epigenomics）、纵向生理监测、时间序列分析、影像学、放射组学（radiomics），以及涵盖健康社会决定因素的临床观察——即便在感染早期，即可获取足够信息以实现疾病分层与病情严重程度预测。本研究提出一种人工智能/机器学习方法，用于整合这类多源异构的丰富数据集，明确疾病的临床谱特征，并识别可预测进展性疾病严重程度的生物标志物特征。为推动本研究开发的方法向广大用户群体落地转化，我们设置了转化开发（Translational Development）职能，负责监督设计-管控流程，确保我们的方法具备提交监管审查的条件。我们的研究时间表中纳入了符合当前针对严重急性呼吸综合征冠状病毒2（SARS-CoV-2）诊断用品的紧急使用授权（Emergency Use Authorization, EUA）政策的合规监管里程碑节点。
