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

使用新颖的机器学习方法制定个性化策略，预防心脏手术后持续性 AKI

• 批准号: 10704097
• 负责人: Ankit Sakhuja
• 金额: $ 2.01万
• 依托单位: WEST VIRGINIA UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-30 至 2023-10-20
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10704097
• 关键词: Acute Renal Failure with Renal Papillary Necrosis; Admission activity; Award; Blood Pressure; Cardiac Surgery procedures; Caring; Characteristics; Clinic; Clinical; Clinical Decision Support Systems; Creatinine; Critical Care; Data; Data Science; Development; Development Plans; Dialysis procedure; Digital biomarker; Ensure; Evolution; Feedback; Foundations; Goals; Hour; Hyperglycemia; Incidence; Injury to Kidney; Intensive Care Units; Intervention; Kidney Diseases; Leadership; Learning; Liquid substance; Machine Learning; Measures; Medicine; Mentors; Methodology; Nephrology; Operative Surgical Procedures; Outcome; Patient Care; Patients; Pharmaceutical Preparations; Phenotype; Prevention; Prevention strategy; Psychological reinforcement; Public Health Informatics; Research; Research Personnel; Rest; Risk; Serum; Structure; Time; Training; United States National Institutes of Health; Universities; West Virginia; Work; career development; clinical decision-making; collaborative environment; data mining; early detection biomarkers; empowerment; experience; high risk; improved; improved outcome; learning algorithm; machine learning algorithm; machine learning method; mortality; mortality risk; novel; personalized approach; personalized care; personalized decision; personalized medicine; personalized strategies; predictive marker; prevent; programs; skills; supervised learning; tool; treatment response; treatment strategy

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.

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 患有急性肾损伤（阿基）或有急性肾损伤风险的患者。我接受过肾脏病学和重症监护的双重培训 药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五年的职业生涯 NIH K 08奖的开发计划，重点是高级数据挖掘，机器学习及其 应用于重症监护肾病学。为此，我组建了一个强大的指导团队， 在指导、研究和领导方面的经验。职业发展计划大纲， 密集的指导和实践培训将为我提供一个完美的平台，成为一个领先的 独立调查员在现场。 在接受心脏手术的患者中，超过三分之一的患者会出现阿基。几项审判调查了 在过去的二十年中，预防或治疗阿基的药物已被证明是无效的。因此，阿基的管理 重点是它的预防，减少进一步发展的措施和并发症的管理。的 预防阿基及其进展的策略依赖于临床干预以优化患者的体液状态， 血压和避免肾毒素和高血糖症。这些临床干预措施提供给 需要心脏手术作为护理包的患者与阿基发生率降低相关。这种关怀- 然而，捆绑式服务的实施依从性非常低，并且缺乏个性化护理的能力。 患者由于先前的工作显示了阿基表型对治疗的不同反应，因此迫切需要 确定个性化的策略，以防止持续性阿基的发展。个性化治疗 基于患者动态临床特征的策略将确保在 正确的时间由于短暂性阿基在48小时内自发消退，因此将干预重点放在高风险人群， 发展为持续性阿基的风险将导致该方法的进一步个性化。的总体目标 这个项目是确定一个个性化的策略，使用机器学习，以防止发展 心脏手术后持续性阿基。本研究的具体目标如下：（1）发展 基于强化学习（RL）的策略，以预防心脏手术后持续性阿基的发展。 (2)开发数字生物标志物，以预测心脏手术后存在持续性阿基风险的患者。完成 这些目标将提供一个结构化的框架，提供个性化的护理，以防止发展， 心脏手术后持续性阿基。它还将为我提供必要的初步数据和经验， 申请R 01应用程序作为一个独立的调查员领导的数据科学研究计划，在关键 护理肾病科。