Prediction of Major Adverse Kidney Events and Recovery (Pred-MAKER) in COVID-19 Patients

COVID-19 患者主要肾脏不良事件和恢复的预测 (Pred-MAKER)

• 批准号: 10216732
• 负责人: Evren U. AZELOGLU
• 金额: $ 46.88万
• 依托单位: ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-07-01 至 2023-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10216732
• 关键词: 2019-nCoV; Acute Renal Failure with Renal Papillary Necrosis; Admission activity; Algorithms; Artificial Intelligence; Biological; Biological Assay; Biological Markers; Biomedical Engineering; Biopsy; Biopsy Specimen; Blood; COVID-19; COVID-19 pandemic; Cells; Cellular biology; Clinical; Clinical Data; Coca; Collection; Complex; Consent; Consultations; Coronavirus; Data; Data Collection; Development; Dialysis procedure; Discrimination; Disease; Disease Outbreaks; Disease Outcome; Disease Progression; Early Intervention; Epidemiology; Event; Functional disorder; Future; Genetic Transcription; Health system; Hospital Mortality; Hospitalization; Hospitals; Hour; Human; In Situ; In Vitro; Incidence; Individual; Infection; Informatics; Injury; Injury to Kidney; Institutional Review Boards; Kidney; Lead; Link; Logistic Regressions; Long-Term Effects; Lung diseases; Machine Learning; Measurement; Measures; Middle East Respiratory Syndrome; Modeling; Molecular; Molecular Biology; Multiomic Data; Nephrology; New York; New York City; Outcome; Pathway Analysis; Pathway interactions; Patient Triage; Patients; Phase; Phenotype; Plasma; Predictive Value; Proteinuria; Proteomics; Recovery; Renal Replacement Therapy; Reporting; Risk; Sampling; Sampling Studies; Scientist; Severe Acute Respiratory Syndrome; Severities; Site; Survivors; Symptoms; System; Systems Biology; Techniques; Therapeutic; Time; Tissues; Triage; Tubular formation; United States; Urine; Vaccines; base; biomarker discovery; biomarker panel; clinical care; clinical predictors; coronavirus disease; experience; high risk; improved; innovation; insight; kidney biopsy; machine learning algorithm; mortality; multidisciplinary; multiple omics; novel marker; novel therapeutics; patient stratification; podocyte; prediction algorithm; predictive modeling; prognostic value; proteomic signature; renal damage; response; sample collection; spatiotemporal; transcriptomics; translational impact; urinary; virology

PROJECT SUMMARY Major adverse kidney events (MAKE) are common in individuals hospitalized with COVID-19, particularly in the United States. Our data from Mount Sinai show that ~40% of hospitalized patients develop acute kidney injury (AKI); 20% of those need renal replacement therapy, and the mortality rate in patients that experience COVID-19 associated AKI is several-fold greater than patients without AKI. Furthermore, we have seen that the rate of non-recovery is also significantly higher compared to those observed in non-COVID AKI, highlighting the potential long-term effects of SARS-CoV-2-associated kidney damage. We propose to utilize the highly coordinated tissue and biospecimen collection machinery that has been initiated at the Mount Sinai Health System. As the largest hospital system at the epicenter of the crisis, Mount Sinai treated and discharged nearly 10,000 COVID-19 patients and created a central IRB approval and data coordination system under the auspices of the newly formed Mount Sinai COVID Informatics Center. As part of biospecimen and clinical data collation efforts, we have consented and obtained blood, urine or clinically indicated kidney biopsy samples from over 700 patients at the time of admission. Using these samples, we propose (1) to use a multipronged approach to determine the biomarkers that are associated with MAKE; (2) to develop a machine learning-based predictive algorithm using a combination of multiplexed biomarker expression levels and clinical metrics; and, (3) to determine cellular pathways that are responsible for COVID-associated AKI by combining multiomics interrogation of SARS-CoV-2 positive patient urine and kidney biopsies as well as the time-dependent transcriptomic signatures of in vitro primary proximal tubule cells. First, our results will have an immediate translational outcome, which will help focus clinical efforts on high risk patients and triage low risk patients quicker. In addition, our proposal will lead to improved understanding of the complex disease mechanisms that cause the unique kidney injury signatures in COVID-19 and may lead to development of novel biomarkers and therapeutics that may prove beneficial during post-COVID clinical care. Our rigorous approach is innovative, and it is supported by established complementary assays. We have assembled an experienced multidisciplinary team encompassing bioengineers, nephrologists, basic scientists, informaticians and virologists that will help improve the understanding of the landscape of kidney outcomes during COVID-19 hospitalizations.

项目摘要 重大肾脏不良事件（MAKE）在因COVID-19住院的个体中很常见，特别是在 美国的我们在西奈山的数据显示，约40%的住院患者发生急性肾功能衰竭。 损伤（阿基）; 20%的患者需要肾脏替代治疗， COVID-19相关的阿基是无阿基患者的数倍。此外，我们还看到， 未恢复率也显著高于在非COVID阿基中观察到的未恢复率， 强调SARS-CoV-2相关肾损伤的潜在长期影响。我们建议利用 在西奈山启动的高度协调的组织和生物标本收集机制 卫生系统。作为危机中心最大的医院系统，西奈山医院治疗并 为近10，000名COVID-19患者出院，并建立了中央IRB批准和数据协调系统 在新成立的西奈山新冠病毒信息学中心的主持下。作为生物标本的一部分， 临床数据整理工作，我们已同意并获得血液、尿液或临床指示的肾脏活检 700多名患者入院时的样本 使用这些样本，我们建议（1）使用多管齐下的方法来确定生物标志物， 与MAKE相关;（2）开发基于机器学习的预测算法， 多路生物标志物表达水平和临床指标;以及（3）确定 通过结合SARS-CoV-2阳性患者的多组学询问负责COVID相关阿基 尿和肾活检以及体外原发性近端癌的时间依赖性转录组学特征 肾小管细胞 首先，我们的结果将有一个立即转化的结果，这将有助于集中临床努力， 高风险患者和低风险患者的分流更快。此外，我们的建议将导致更好的理解， 导致COVID-19中独特的肾损伤特征并可能导致 开发新的生物标志物和治疗方法，这些生物标志物和治疗方法可能在COVID后的临床治疗中被证明是有益的。 在乎我们严格的方法是创新的，并得到了已建立的补充检测的支持。我们有 组建了一个经验丰富的多学科团队，包括生物工程师，肾病学家，基础科学家， 信息学家和病毒学家，这将有助于提高对肾脏结果的了解 在COVID-19住院期间。