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。 在接受心脏手术的患者中，超过三分之一的患者出现了AKI。几起调查不同案件的审判 在过去的二十年里，预防或治疗AKI的药物被证明是徒劳的。因此，AKI的管理 重点是其预防、减少其进一步进展的措施和并发症的处理。这个 预防AKI及其进展的策略依赖于临床干预以优化患者的液体状况， 降血压，避免肾毒素和高血糖。这些临床干预措施在提供给 需要心脏手术作为护理包的患者与AKI的发生率降低有关。这份关怀- 然而，捆绑包对实施的遵从性非常低，并且缺乏个性化护理的能力 病人。由于先前的工作显示AKI表型对治疗的反应不同，因此迫切需要 确定个性化策略以防止持续性AKI的发展。个性化治疗 基于患者动态临床特征的策略将确保在 恰逢其时。随着短暂性急性心肌梗死在48小时内自发缓解，将干预重点放在高血压者 开发持久性AKI的风险将导致这种方法的进一步个性化。总的目标是 这个项目是为了确定一种个性化的策略，使用机器学习来防止发展 心脏手术后持续性急性心肌梗死。我将追求以下研究的具体目标：(1)发展 基于强化学习(RL)的预防心脏手术后持续性AKI的策略。 (2)开发数字生物标记物来预测心脏手术后持续AKI的风险。完成 这些目标将提供一个结构化的框架，以提供个性化的护理，以防止发生 心脏手术后持续性急性心肌梗死。它还将为我提供必要的初步数据和经验 作为独立调查员申请R01申请，领导关键领域的数据科学研究计划 护理肾脏科。

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 is seen in over one-third of patients undergoing cardiac surgery. Several trials investigating various medications to prevent or treat AKI over the last two decades have proven futile. Management of AKI therefore focuses on its prevention, measures to reduce further progression and management of its complications. The strategy to prevent AKI and its progression relies on clinical interventions to optimize a patient’s fluid status, blood pressure and avoiding nephrotoxins and hyperglycemia. These clinical interventions when provided to patients requiring cardiac surgery as a care-bundle are associated with decreased incidence of AKI. This care- bundle, however, has very low compliance with implementation and lacks the ability to personalize care for patients. With prior work showing differential response to therapy in AKI phenotypes, there is a critical need to determine personalized strategies to prevent the development of persistent AKI. Personalization of treatment strategies based on dynamic clinical characteristics of patients will ensure that the right action is performed at the right time. As transient AKI resolves spontaneously within 48 hours, focusing interventions to those at high risk for developing persistent AKI will lead to further personalization of this approach. The overall objective of this project is to determine a personalized strategy using machine learning to prevent the development of persistent AKI after cardiac surgery. I will pursue following specific aims for this study: (1) Develop reinforcement learning (RL) based strategy to prevent the development of persistent AKI after cardiac surgery. (2) Develop digital biomarkers to predict patients at risk for persistent AKI after cardiac surgery. Completion of these aims will provide a structured framework to provide personalized care to prevent the development of persistent AKI after cardiac surgery. It will also provide me with preliminary data and experience necessary to apply for R01 applications as an independent investigator leading a data science research program in critical care nephrology.