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 K08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。 阿基是一种具有异质性表型的复杂综合征，在超过三分之一的患者中观察到， 心脏手术尽管阿基严重程度的增加与更差的结局相关，但初步文献 表明阿基的持续性本身很重要。虽然迅速消退的一过性阿基仍有更严重的 结果，对于持续超过48小时的持续性阿基患者，结果要差得多。 一旦确诊，建议进行额外的监测，重新评估原因和管理方案。 持续性阿基的风险。然而，预防阿基仍然至关重要。预防行动， 虽然有效，但在心脏手术患者中具有低顺应性。超过40%的阿基发生在心脏术后 手术是短暂的，并在48小时内自发解决，有针对性地应用这些行动， 持续性阿基高危患者将允许更集中的资源分配。 在患有持续性阿基的患者中鉴定不同的表型将进一步允许鉴定 对治疗的不同反应，并导致阿基治疗的个性化。目前的阿基研究， 然而，其重点是识别和预防阿基的严重性增加。本研究的目的 因此，识别和表征心脏手术后存在持续性阿基风险的患者。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阿基风险的患者，（2）确定不同的临床表型 心脏手术后发生持续性阿基的患者。这些目标的实现将提供一个结构化的 为阿基患者提供个性化护理。它还将为我提供初步数据， 作为领导数据科学的独立研究者申请R01申请所需的经验 重症监护肾病学的研究项目。