Using Novel Machine Learning Methods to Personalize Strategies for Prevention of Persistent AKI after Cardiac Surgery

我的长期目标是整合健康信息学、数据挖掘和机器学习，以改善对 有急性肾损伤(AKI)的患者和有AKI风险的患者。我接受过肾脏病和重症护理的双重培训 医学。我已经在发展我在健康信息学方面的技能。这份提案提出了一个为期五年的职业生涯 NIH K08奖的发展计划侧重于高级数据挖掘、机器学习和他们的 在重症监护肾脏科的应用。为此，我用几十年的时间组建了一支强大的指导团队 在指导、研究和领导方面的经验。概述的职业发展计划与 密集的指导和实践培训将为我成为一名领导者提供完美的平台 该领域的独立调查员。 AKI是一种表型不同的复杂综合征，超过三分之一的患者接受了 心脏手术。虽然AKI的严重程度增加与更差的预后相关，但初步文献 已经表明，AKI的坚持性本身就很重要。而迅速解决的暂时性AKI仍有更糟糕的情况 结果，持续超过48小时的AKI患者的结果要糟糕得多。 建议在诊断后进行额外的监测、重新评估原因和管理选项 持久化AKI的建立。然而，预防AKI仍然是重中之重。预防行动， 虽然有效，但在心脏手术患者中依从性较低。心脏术后超过40%的急性心肌梗死 手术是短暂的，并在48小时内自行解决，这些行动的针对性应用在 持续AKI的高危患者将允许更有针对性地分配资源。 在持续性AKI患者中识别不同的表型将进一步允许识别 对治疗的不同反应，并导致AKI治疗的个性化。目前的AKI研究， 然而，重点是识别和预防日益严重的AKI。这项研究的目的是 因此，识别和描述心脏手术后持续AKI的风险和特征。这 将通过解决以下两个具体目标来实现：(1)开发数字生物标记物以预测 心脏手术后有持续AKI风险的患者，(2)确定不同的临床表型 心脏手术后发生持续性急性心肌梗死的患者。完成这些目标将提供一个有组织的 为AKI患者提供个性化护理的框架。它还将为我提供初步数据和 作为一名领导数据科学的独立研究员申请R01所需的经验 重症监护肾脏病研究计划。

My long-term goal is to integrate health informatics, data mining and machine learning to improve the care for patients with, and at risk for, acute kidney injury (AKI). I am dual trained in Nephrology and Critical Care Medicine. I am already developing my skills in health informatics. This proposal presents a five-year career development plan for NIH K08 award focused on training in advanced data mining, machine learning and their applications to critical care nephrology. To that effect, I have assembled a strong mentoring team with decades of experience in mentoring, research and leadership. The outlined career development plan in conjunction with intensive mentoring and hands-on training will provide me the perfect platform to become a leading independent investigator in the field. AKI, a complex syndrome with heterogenous phenotypes, is seen in over one third of patients undergoing cardiac surgery. Though increasing severity of AKI is associated with worse outcomes, preliminary literature has shown than persistence of AKI is itself important. While transient AKI that resolves rapidly still has worse outcomes, the outcomes are much worse for patients with persistent AKI that lasts beyond 48 hours. Additional monitoring, reassessment of causes and management options is recommended once the diagnosis of persistent AKI is established. The prevention of AKI, however, remains paramount. Preventive actions, though effective, have low compliance among cardiac surgery patients. As over 40% of AKI after cardiac surgery is transient and resolves spontaneously within 48 hours, targeted application of these actions in patients at high risk for developing persistent AKI will allow for a more focused allocation of resources. Identification of distinct phenotypes among patients with persistent AKI will further allow for identification of differential responses to therapy and lead to personalization of AKI therapy. The current AKI research, however, is focused on identification and prevention of increasing severity of AKI. The objective of this study therefore is to identify and characterize patients at risk for and with persistent AKI after cardiac surgery. This will be accomplished by addressing the following two specific aims: (1) Develop digital biomarkers to predict patients at risk for persistent AKI after cardiac surgery, (2) Determine distinct clinical phenotypes among patients who develop persistent AKI after cardiac surgery. Completion of these aims will provide a structured framework to provide personalized care to patients with AKI. It will also provide me with preliminary data and experience necessary to apply for R01 application as an independent investigator leading a data science research program in critical care nephrology.