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: 10525157
• 负责人: Ankit Sakhuja
• 金额: $ 14.75万
• 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 2027-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10525157
• 关键词: Acute Renal Failure with Renal Papillary Necrosis; Admission activity; Award; Biological Markers; Blood Pressure; Cardiac Surgery procedures; Caring; Characteristics; Clinic; Clinical; Clinical Decision Support Systems; Creatinine; Critical Care; Data; Data Science; Development; Development Plans; Dialysis procedure; Ensure; Evolution; Feedback; Foundations; Goals; Hour; Hyperglycemia; Incidence; Injury to Kidney; Intensive Care Units; Intervention; Kidney Diseases; Leadership; Learning; Liquid substance; Machine Learning; Medicine; Mentors; Methodology; Nephrology; Operative Surgical Procedures; Outcome; Patient Care; Patient Rights; Patients; Pharmaceutical Preparations; Phenotype; Prevention Measures; 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; base; career development; clinical decision-making; collaborative environment; data mining; digital; early detection biomarkers; 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，数据挖掘和机器学习，以改善护理 患有急性肾脏损伤（AKI）的患者。我接受了肾脏病和重症监护的双重培训 药品。我已经在发展健康信息方面的技能。该提议提出了五年的职业生涯 NIH K08奖的开发计划重点是高级数据挖掘，机器学习及其其培训 对重症监护肾脏的应用。为此，我已经组建了一个强大的心理团队，数十年 具有心理，研究和领导力的经验。概述的职业发展计划与 密集的心理和动手训练将为我提供成为领先的理想平台 该领域的独立调查员。 在三分之一的心脏手术患者中，AKI可见。几次调查了各种试验 在过去的二十年中，预防或治疗AKI的药物已被证明是徒劳的。因此，AKI的管理 专注于预防，减少进一步发展的措施以及对其并发症的管理。这 防止AKI及其进展的策略依赖于临床干预措施来优化患者的液体状况， 血压和避免肾毒素和高血糖。这些临床干预措施提供给 需要心脏手术作为护理束的患者与AKI事故减少有关。这个关心 - 但是，捆绑包对实施的遵守非常低，并且缺乏个性化护理的能力 患者。先前的工作表现出对AKI表型中治疗的反应差异，因此至关重要 确定个性化策略，以防止持久性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 合适的时间。由于瞬态AKI在48小时内赞助，因此将干预措施集中在高处 发展持久性AKI的风险将导致这种方法的进一步个性化。总体目标 该项目是使用机器学习来确定个性化策略，以防止开发 心脏手术后持续的AKI。我将追求这项研究的具体目标：（1）发展 基于强化学习（RL）的策略，以防止心脏手术后持续性AKI的发展。 （2）开发数字生物标志物，以预测心脏手术后持续性AKI风险的患者。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，以防止发展 心脏手术后持续的AKI。它还将为我提供初步数据和必要的经验 作为领导数据科学研究计划的独立研究人员，申请R01应用程序 护理肾脏病。