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: 10979324
• 负责人: Ankit Sakhuja
• 金额: $ 13.26万
• 依托单位: ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
• 依托单位国家: 美国
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-01-04 至 2027-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10979324
• 关键词: Using; Novel; Machine; Learning; Methods

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.

我的长期目标是整合健康信息学、数据挖掘和机器学习，以改善对患者的护理 患有急性肾损伤 (AKI) 或有患急性肾损伤 (AKI) 风险的患者。我接受过肾脏病学和重症监护的双重培训 药品。我已经在发展健康信息学方面的技能。该提案提出了五年的职业生涯 NIH K08 奖的发展计划重点关注高级数据挖掘、机器学习及其相关领域的培训 在重症监护肾病学中的应用。为此，我组建了一支拥有数十年经验的强大指导团队 指导、研究和领导方面的经验。概述的职业发展计划连同 密集的指导和实践培训将为我提供成为领导者的完美平台 该领域的独立调查员。 AKI 是一种具有异质表型的复杂综合征，超过三分之一的患者出现 AKI 心脏手术。尽管 AKI 严重程度的增加与更差的结果相关，但初步文献 已经表明 AKI 的持续存在本身就很重要。虽然快速消退的短暂性 AKI 的情况仍然更糟 对于持续超过 48 小时的持续性 AKI 患者，结果要差得多。 一旦诊断，建议进行额外的监测、重新评估原因和管理方案 建立了持续性 AKI。然而，预防 AKI 仍然至关重要。预防措施， 尽管有效，但心脏手术患者的依从性较低。超过 40% 的 AKI 发生在心脏病之后 手术是短暂的，48小时内自然消退，有针对性地应用这些措施 患有持续性 AKI 的高风险患者将能够更加集中地分配资源。 识别持续性 AKI 患者的不同表型将进一步识别 对治疗的不同反应导致 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。本研究的目的 因此，我们的目标是识别和描述心脏手术后有持续 AKI 风险和持续 AKI 的患者。这 将通过解决以下两个具体目标来实现：（1）开发数字生物标志物来预测 心脏手术后有持续性 AKI 风险的患者，(2) 确定不同的临床表型 心脏手术后出现持续性 AKI 的患者。完成这些目标将提供一个结构化的 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 作为领导数据科学的独立研究者申请 R01 申请所需的经验 重症监护肾病学研究计划。