Real-time Prediction of Adverse Outcomes After Surgery

实时预测手术后不良后果

• 批准号: 10724048
• 负责人: Andrew Bishara
• 金额: $ 18.83万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2028-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10724048
• 关键词: Acute Renal Failure with Renal Papillary Necrosis; Adoption; Adult; Affect; Algorithms; Anesthesia procedures; Anti-Inflammatory Agents; Area; Artificial Intelligence; Biometry; Bolus Infusion; Cardiovascular system; Cessation of life; Chronic Kidney Failure; Clinical; Clinical Trials; Collaborations; Complex; Consensus; Costs and Benefits; Critical Care; Data; Data Science; Decision Making; Development; Electronic Health Record; Event; Faculty; Focus Groups; Future; Goals; Health Care Costs; Healthcare; Human; Hypertension; Hypotension; Hypoxemia; Inflammation; Institution; Intervention; Intervention Studies; Intra-abdominal; Kidney; Learning; Liquid substance; Literature; Machine Learning; Medicine; Mentors; Modeling; Monitor; Operating Rooms; Operative Surgical Procedures; Outcome; Patient Care; Patients; Performance; Perioperative; Perioperative Care; Physicians; Physiologic pulse; Physiological; Positioning Attribute; Postdoctoral Fellow; Postoperative Period; Prevention strategy; Preventive measure; Preventive treatment; Process; Provider; Public Health; Publications; Publishing; Pulse Oximetry; Randomized, Controlled Trials; Recommendation; Research; Research Personnel; Retrospective cohort; Risk; Risk Assessment; Risk Factors; Risk Reduction; Running; Services; Specificity; Statistical Models; Stress; System; Techniques; Technology; Time; Training; United States National Institutes of Health; Update; Validation; Venous; Visualization; Work; abdominal pressure; acute care; adverse outcome; blood pressure elevation; care providers; career development; clinical care; computerized tools; design; experience; experimental study; graduate student; high risk; human-robot interaction; hypoperfusion; implementation science; improved; indexing; innovation; machine learning model; machine learning prediction; model development; modifiable risk; nephrotoxicity; new technology; novel; personalized medicine; predictive modeling; prevent; prognostic; programs; prospective; responsible research conduct; risk stratification; safety assessment; statistics; surgical risk; tool; training project; undergraduate student; urinary tract obstruction

The goal of this K23 application is to provide Dr. Bishara with the necessary research experience and time to establish himself as a primary investigator focused on designing and implementing machine learning (ML) and artificial intelligence in the perioperative setting. The career development activities in this application include early intensive course work in ML and statistics focused on improving model development and causal inference techniques. Then coursework focuses on clinical trial training, grantsmanship, responsible conduct of research, and culminates in a course studying implementation science and algorithmic human-robot interaction. Augmenting this training are project-specific tutorials with experts to improve the models proposed in this application with a focus on real-time prediction of perioperative acute kidney injury (AKI) and describing the risk landscape of perioperative AKI. To achieve these goals, Dr. Bishara has assembled a team of experts and mentors in the areas of data science, AKI, ML, and statistics. Dr. Atul Butte, his primary mentor, is an expert in data science and ML and has trained nearly 100 post-doctoral fellows, undergraduate and graduate students, and staff. Dr. Kathleen Liu is a thought leader in the field of AKI with an active research program focused on AKI and critical care clinical trials. She has mentored numerous junior faculty, including previous NIH K23 awardees. Dr. Romain Pirracchio is an expert in biostatistics and ML in acute care. He has collaborations with Berkeley and the FDA and over 100 publications in the realm. These three mentors and the impressive team of advisors will guide Dr. Bishara to complete the project described below and to grow into an independent investigator. There has been a recent surge in the published literature on ML in medicine, and studies have shown patient care improves when provider expertise is augmented by ML. Unfortunately, implementing published ML models to inform clinical care is not trivial, as many obstacles exist. This application focuses on exploring and overcoming those obstacles by implementing specific models in the perioperative setting. Dr. Bishara has developed novel ML visualization technology that allows for improved interactions between providers and models, which provide predictions and recommendations to those providers. This technology also allows for improved regular monitoring and interpretation of the model to assure sustained accuracy and reliability. He will apply this new technology to predict perioperative AKI in real-time, building upon models he has developed. Postoperative AKI is a major public health problem affecting up to 47% of patients and is consistently associated with adverse outcomes, including, major adverse cardiovascular events (MACE), increased healthcare costs, and death. Randomized controlled trials show that implementation of kidney-protective strategies prevents AKI for high-risk patients. Evidence suggests these strategies are underutilized as risk of AKI is often underestimated in the perioperative setting. Dr. Bishara hypothesizes his ML models will identify those patients who will most benefit from timely kidney protective interventions at the time when preventative strategies can be initiated.

K23申请的目的是为Bishara博士提供必要的研究经验， 是时候让自己成为一名专注于设计和实施机器学习的主要研究者了 (ML)和人工智能在围手术期的应用此应用程序中的职业发展活动 包括ML和统计学早期密集课程工作，重点是改进模型开发和因果关系 推理技术然后，课程重点是临床试验培训，保证，负责任的行为， 研究，并在学习实现科学和算法人机交互的课程中达到高潮。 增强此培训的是特定于项目的教程，专家将改进本 应用，重点是实时预测围手术期急性肾损伤（阿基）并描述风险 围手术期阿基的概况。为了实现这些目标，Bishara博士组建了一个专家团队， 数据科学、阿基、ML和统计学领域的导师。阿图尔·布特博士，他的主要导师，是一位专家， 数据科学和机器学习，并培养了近100名博士后研究员，本科生和研究生， 和工作人员。Kathleen Liu博士是阿基领域的思想领袖，专注于阿基的积极研究计划 和重症监护临床试验。她指导了许多初级教师，包括以前的NIH K23获奖者。 博士Romain Pirracchio是急性护理中的生物统计学和ML专家。他与伯克利合作， FDA和超过100种出版物。这三位导师和令人印象深刻的顾问团队将 指导Bishara博士完成下面描述的项目，并成长为一名独立的研究者。 最近关于医学ML的出版文献激增，研究表明， 当提供者的专业知识通过ML得到增强时，患者护理得到改善。不幸的是，实现已发布的ML 为临床护理提供信息的模型并不是微不足道的，因为存在许多障碍。该应用程序侧重于探索和 通过在围手术期环境中实施特定模式来克服这些障碍。比沙拉医生 开发了一种新的ML可视化技术，可以改善供应商之间的交互， 模型，为这些供应商提供预测和建议。这项技术还允许 改进对模型的定期监测和解释，以确保持续的准确性和可靠性。他将 应用这项新技术，以他开发的模型为基础，实时预测围手术阿基。 术后阿基是影响高达47%患者的主要公共卫生问题， 不良结局，包括主要心血管不良事件（MACE）、医疗费用增加， 与死随机对照试验表明，实施肾脏保护策略可预防阿基 高危患者的治疗有证据表明，这些策略未得到充分利用，因为阿基的风险往往被低估 在围手术期环境中。Bishara博士假设他的ML模型将识别出那些最愿意 在可以启动预防策略时，从及时的肾脏保护干预中受益。